Jamie-BitFlight Skills

Add automated documentation updater to any Claude skill. Creates a Python sync script that downloads upstream docs, processes markdown for AI consumption, and maintains local cache with configurable refresh. Collects template variables, then delegates implementation through 5-phase workflow. Use when adding auto-updating reference documentation to plugins or skills.

SAM-style feature initiation workflow — discovery through codebase analysis, architecture spec, task decomposition, validation, and context manifest. Use when a user asks to add a feature, plan a feature, or convert an idea into executable task files.

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

Browser automation for AI agents using the agent-browser CLI and Playwright. Use when navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, logging into sites, or automating any browser task. Triggers on "open a website", "fill out a form", "click a button", "scrape data", "test this web app", "automate browser actions", or any programmatic web interaction request.

Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.

Create high-quality Claude Code agents from scratch or by adapting existing agents as templates. Use when the user wants to create a new agent, modify agent configurations, build specialized subagents, or design agent architectures. Guides through requirements gathering, template selection, and agent file generation following Anthropic best practices (v2.1.63+).

Scientific delegation framework for orchestrators coordinating sub-agents. Provides WHERE-WHAT-WHY context patterns while preserving agent autonomy. Use when delegating tasks, structuring sub-agent prompts, planning multi-agent workflows, or coordinating specialist agents.

Rules for orchestrators handling sub-agent results without lossy re-summarization. Activates when receiving agent output, relaying research results, passing data between agents, or combining agent findings. Prevents the failure mode where 'not found' becomes 'doesn't exist' and counts get dropped.

Agentskill kaizen plugin documentation index. Load when needing to read about cross-platform notes, improvement plans, or DuckDB integration.

Agent Skills Open Standard reference (agentskills.io). Use when creating portable skills for Claude Code, Cursor, Gemini CLI, OpenAI Codex, VS Code, Roo Code, and 20+ compatible agents. Covers frontmatter schema, naming rules, directory structure, progressive disclosure, validation, and authoring. Load before creating cross-agent skills.

Analyzes failing test cases to determine whether failures indicate genuine bugs or incorrect test implementations. Use when debugging test failures, investigating test errors, classifying failures as test bugs vs implementation bugs vs ambiguous behavior, or when given specific failing test names or pytest output. Applies balanced investigative reasoning — never auto-fixes tests without establishing root cause first.

Use when analyzing failing test cases to determine whether failures indicate genuine bugs or test implementation issues. Activates on "analyze failing tests", "debug test failures", "investigate test errors", or when provided with specific failing test names or output. Applies balanced investigative reasoning — does not auto-fix tests without establishing root cause.

Use when analyzing failing test cases to determine whether failures indicate genuine bugs or test implementation issues. Activates on "analyze failing tests", "debug test failures", "investigate test errors", or when provided with specific failing test names or output. Applies balanced investigative reasoning — does not auto-fix tests without establishing root cause.

Fetch and report current API syntax, changelog entries, and breaking changes for a specific library or protocol version. One research angle within a parallel technical-research set — runs independently and returns a structured cited report. Invoke when a specific library name and version are the target.

Knowledge reference for Autonomous Refinement Loop research — pattern research into prerequisites for autonomous execution without synchronous human blocking gates. Defines failure categories, prerequisites, and conditions for replacing human judgment with machine-verifiable checks. Use when designing or evaluating autonomous agent loops, gate conditions, or HOOTL execution patterns.

Assess a plugin and create refactoring task files for parallel agent execution. Use when you need to analyze a plugin structure, score its quality, and generate a phased refactoring plan with design map and implementation tasks.

Use when building async APIs, concurrent systems, or I/O-bound Python applications requiring non-blocking operations. Covers asyncio, async/await patterns, task scheduling, synchronization primitives, and high-performance concurrent programming.

Use when writing asyncio Python code — async/await coroutines, concurrent I/O with asyncio.gather, task creation and cancellation, semaphore rate limiting, producer-consumer queues, async context managers, async generators, WebSocket servers, aiohttp web scraping, async database operations, run_in_executor for blocking calls, or testing async code with pytest-asyncio. Covers FastAPI and aiohttp patterns, synchronization primitives, timeout handling, and common pitfalls like event loop blocking and missing await.

Audit agent lifecycle — validates agent execution capability against configuration. Accepts plugin path, runs 8 semantic audits (capability vs config alignment, skill loading correctness, inter-agent contracts, prompt contradictions, tool sufficiency, dead agents, scriptable patterns, pattern learning), writes reports to .plugin-creator/audits/. Use when auditing agent lifecycle, checking agent capabilities, verifying tool access, finding dead agents, validating agent contract alignment, or confirming agents can execute workflows.

Evaluate a single skill's quality against its stated purpose. Classifies the skill's purpose type, then scores applicable quality categories — universal dimensions always apply; structural dimensions (scripts, references, assets) are scored only when warranted by the skill's purpose. A focused 20-line behavioral skill with no bundled resources scores fully on all applicable categories. Use when auditing skill quality, checking marketplace readiness, evaluating skill completeness, performing pre-publication evaluation.

Audit skill lifecycle by tracing call chains, detecting circular dependencies, finding instruction contradictions, identifying duplicated datasets, analyzing bidirectional coherence, discovering scriptable sequences, and learning patterns. Use when checking skill coherence, validating skill workflow, finding semantic gaps in plugin structure, or auditing plugin before marketplace submission. Generates audit reports to .plugin-creator/audits/ with findings by dimension.

Use when creating, listing, viewing, updating, closing, resolving, grooming, or syncing backlog items and GitHub Issues — single interface for all backlog CRUD via MCP tools (mcp__plugin_dh_backlog__*). GitHub Issues are the source of truth; direct file edits are bypassed in favour of MCP tool calls.

Administer the backlog tooling ecosystem when a capability gap is discovered. Invoke when backlog.py, backlog skills, or backlog agents lack a needed operation and a workaround was used or is about to be used. Classifies gaps as script (delegates to @python-cli-architect), process (loads improve-processes), or documentation (delegates to @ai-doc-optimizer). Domain: backlog.py, create/work/groom-backlog-item skills, backlog-item-groomer agent, hooks, templates, references, rules, and tests.

Administer the backlog tooling ecosystem when a capability gap is discovered. Invoke when backlog.py, backlog skills, or backlog agents lack a needed operation and a workaround was used or is about to be used. Classifies gaps as script (delegates to @python-cli-architect), process (loads improve-processes), or documentation (delegates to @contextual-ai-documentation-optimizer). Scope — backlog.py, create/work/groom-backlog-item skills, backlog-item-groomer agent, hooks, templates, references, rules, and tests.

Bash 5.1 release features and improvements with practical examples. Use when working with Bash 5.1 features, epoch time variables, redirection enhancements, or when user asks about Bash 5.1 changes, new features, or version-specific capabilities.

Bash 5.2 release features and improvements with practical examples. Use when working with Bash 5.2 features, variable handling enhancements, readline improvements, or when user asks about Bash 5.2 changes, new features, or version-specific capabilities.

Bash 5.3 release features and improvements with practical examples. Use when working with Bash 5.3 features, new command substitution, GLOBSORT, loadable builtins, or when user asks about Bash 5.3 changes, new features, or version-specific capabilities.

This skill should be used when the user asks to "write a bash script", "create a shell script", "implement bash function", "parse arguments in bash", "handle errors in bash", or mentions bash development, shell scripting, script templates, or modern bash patterns.

This skill should be used when the user asks to "lint bash script", "run shellcheck", "format shell script", "use shfmt", "fix shellcheck errors", or mentions shell script linting, formatting, code quality, or pre-commit hooks for bash.

This skill should be used when the user asks to "add logging to bash script", "colorize output", "implement log levels", "CI/CD sections", "terminal colors in bash", or mentions logging functions, emoji output, collapsible CI sections, or shlocksmith.

This skill should be used when the user asks about "POSIX compatibility", "portable shell scripts", "cross-shell compatibility", "bashisms", "shebang selection", or mentions writing scripts that work on different shells (bash, sh, dash, zsh) or different systems.

This skill should be used when the user asks to "test bash script", "write shell tests", "use shunit2", "use shellspec", "create test suite for bash", or mentions unit testing, test frameworks, mocking, or test-driven development for shell scripts.

Activates Standard of Excellence enforcement for the current session. Load before starting any task to apply completion standards: finish the whole thing, fix the root cause, ship the complete working result. Blocks partial solutions, workarounds, deferred threads, and invented content limits when the permanent solve is within reach. Triggers: 'do the whole thing', 'boil the ocean', 'standard of excellence', 'finish it completely', before starting any implementation, refactoring, or multi-step task where partial output is a risk. Does NOT apply to: read-only queries, one-line typo fixes, pure knowledge questions, or single-output summarize requests.

You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or when users request help with ideation, marketing, and strategic planning. Explores user intent, requirements, and design before implementation using 30+ research-validated prompt patterns.

This skill should be used when code search is needed (whether explicitly requested or as part of completing a task), when indexing the codebase after changes, or when the user asks about ccc, cocoindex-code, or the codebase index. Trigger phrases include 'search the codebase', 'find code related to', 'update the index', 'ccc', 'cocoindex-code'.

Configure clang-format code formatting. Use when: user mentions clang-format or .clang-format, analyzing code style/patterns, creating/modifying formatting config, troubleshooting formatting, brace styles/indentation/spacing/alignment/pointer alignment, or codifying conventions.

Complete reference for Claude Code plugins system (April 2026). Use when creating plugins, understanding plugin.json schema, marketplace configuration, bundling skills/commands/agents/hooks/MCP/LSP/monitor servers, plugin caching, validation, or distribution. Covers plugin components, directory structure, installation scopes, environment variables, CLI commands, debugging, and enterprise features.

Claude Code session log schema — JSONL record types, message structure, tool call/result pairing, subagent file locations, team session layout, task/plan/team configuration paths. Use when parsing ~/.claude/projects/**/*.jsonl files, writing PostToolUse hooks that measure response sizes, building session analyzers, or any agent that reads or queries Claude session transcripts.

Reference guide for Claude Code skills system (March 2026). Use when creating, modifying, or understanding skills, SKILL.md format, frontmatter fields, hooks, context fork, or skill best practices.

Reference spec and schema for Claude Code subagents — covers built-in agents (Explore, Plan, general-purpose), all frontmatter fields (name, description, tools, disallowedTools, model, permissionMode, maxTurns, skills, mcpServers, hooks, memory, background, effort, isolation, color, initialPrompt), scope and file locations, invocation patterns (@-mention, --agent, --agents CLI), tool restrictions, and example subagent definitions. Use when creating, configuring, or debugging a subagent definition file, looking up a frontmatter field, choosing between subagents and agent teams, or understanding what loads at subagent startup.

Runs structured Python cleanup and modernization — static analysis via prek/ruff, smell investigation to root cause, typed-boundary hardening by inventorying Any usage, and modernization within the project's requires-python lane. Use when refactoring Python code, removing dead code, hardening type boundaries, or running a modernization pass on a file or scope.

Use when orchestration or planning agents are producing task plans, task prompts, or TASK.md instructions that must be unambiguous, verifiable, and resistant to hallucination. Applies CLEAR (Concise, Logical, Explicit, Adaptive, Reflective) to structure and write agent task files, then adds CoVe (Chain of Verification) checks where accuracy risk is meaningful. Activates on draft task prompts, swarm plans, migration tasks, and multi-step plans requiring independently executable steps.

Local codebase analysis research angle — derives behavioral contracts, coding conventions, SKILL.md flow insertion points, and agent data availability maps from actual source files. Use when the blocking question is answered by reading the repository: what does this function actually do, what pattern does the codebase use for X, where in this workflow does a new step go, or what data does this agent already have.

Execute large-scale automated code transformations safely and idempotently. Use when renaming symbols across a codebase, migrating API call-sites, enforcing new patterns at scale, or applying structural edits to many files at once. Triggers on: 'codemod', 'mass rename', 'migrate all usages', 'transform codebase', 'apply pattern at scale', AST-based refactoring, or any task requiring consistent edits across 10 or more files.

Use when a task asks for architecture review, dependency graph visualization, module coupling analysis, or circular dependency detection. Auto-detects scope (git diff → PR diff → full project). Reads project config (pyproject.toml, tsconfig.json, go.mod, Cargo.toml) to establish the intra-project module namespace before parsing imports. Builds a module-dependency graph across Python, TypeScript, JavaScript, Go, Rust, and Java. Detects cycles via graphify output or an executable Python script. Checks Conway's Law alignment against CODEOWNERS and directory structure. For Claude plugin repos, also traces cross-language chains: hook configs → hook scripts, SKILL.md/agent docs → node/uv-run scripts, PEP 723 inline deps, and MCP tool calls. Emits Mermaid flowcharts with severity color-coding (red = circular dep, yellow = high-coupling, green = clean, blue = Conway violation). Applies recursive semantic partitioning for graphs > 40 nodes. Registers each diagram as a codebase-analysis artifact.

Loaded automatically when reviewing Claude skills or agent definitions — covers SKILL.md structure, frontmatter validity, token budget, description quality, and agent contract compliance.

Reviews CLI application code for correctness and quality. Use when reviewing tools that use argparse, click, typer, commander.js, or similar argument parsers — covers exit codes, help flags, stdin/stdout/stderr separation, non-interactive operation, signal handling, argument validation, ANSI color safety, and dry-run support for destructive operations.

Use when reviewing AI/ML code or LLM integration — activates on prompt templates, model selection logic, token budget concerns, or evaluation harness code. Enforces prompt hygiene, model tier matching, context window management, token economics, structured output validation, temperature settings, retry logic, streaming error handling, and PII/safety rules.

Applies Node.js-specific code review patterns for async I/O, streams, security, process management, and dependency hygiene. Use when reviewing Node.js server code, route handlers, middleware, or any JavaScript file alongside package.json without TypeScript. Triggers on sync I/O in request paths, missing stream backpressure, process.exit misuse, eval/exec injection risks, wildcard version ranges, missing lockfiles, EventEmitter cleanup gaps, and unvalidated environment variables at startup.

Provides Python-specific code review rules for the dh code-reviewer agent. Activates on pyproject.toml or *.py file detection — enforces uv, ruff, ty, pytest, type annotation, error handling, and Python 3.11+ idioms including pathlib, match statements, and modern union syntax.

Provides TypeScript-specific code review patterns covering strict mode, ESM, type safety, branded types, discriminated unions, async patterns, runtime safety, and common anti-patterns. Activates on detection of tsconfig.json, *.ts, or *.tsx files during code review — loaded automatically by dh:code-reviewer.

Use when reviewing web frontend code — HTML, CSS, JSX, or browser-targeted JavaScript. Enforces accessibility (WCAG AA, aria labels, focus management), XSS prevention (innerHTML, dangerouslySetInnerHTML), performance (layout thrash, CLS, lazy loading), CSS design tokens, form labeling, and event listener cleanup. Loaded by dh:code-reviewer on *.html, *.css, *.jsx detection.

Create and configure slash commands for Claude Code — the legacy .claude/commands/ format. Use when asked to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or for guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices. Commands are the legacy format superseded by skills — for new development prefer /plugin-creator:skill-creator

When setting up commit message validation for a project. When project has commitlint.config.js or .commitlintrc files. When configuring CI/CD to enforce commit format. When extracting commit rules for LLM prompt generation. When debugging commit message rejection errors.

Generates and commits conventional-commits messages by analyzing staged git diffs — fast and fork-context safe. Use when the user asks to commit staged changes, needs a type-scope-description message generated from the current diff, or wants scope selection guidance and pre-commit hook handling.

Use when all tasks for a feature are marked COMPLETE — runs holistic quality gates including code review, feature verification, integration check, documentation drift audit and update, and context refinement. Creates follow-up task files when issues are found.

Close a completed GitHub milestone. Args: {milestone-number}. Audits open and closed issues, offers to carry forward open items to a new or existing milestone, closes the GitHub milestone, updates Project V2 Status to Done for closed issues, and generates a completion summary. Use when a sprint or release is finished and needs to be officially closed.

Close a completed GitHub milestone and generate a completion summary. Use when a sprint or release is finished and needs to be officially closed. Audits open and closed issues, offers to carry forward open items to a new or existing milestone, closes the milestone, updates Project V2 Status to Done for closed issues, and produces a completion report.

Decide which plugin component type to use and how to organize components at scale. Covers the component lifecycle (discovery and activation phases), decision framework for choosing between commands, skills, agents, hooks, and MCP servers, and organization patterns for each component type. Use when asking "which component type should I use", "command vs skill vs agent", "when to use a hook vs MCP server", "component lifecycle", "how to organize plugin components", "plugin structure patterns", or "scale a plugin with many components".

Use when reviewing pytest test suites for coverage, isolation, mock usage, naming conventions, or completeness. Activates on requests like "review test coverage", "audit test quality", or "check tests for completeness". Performs thorough checklist-driven review for test isolation, mock correctness, AAA pattern adherence, and naming standards.

Performs checklist-driven review of pytest test suites against coverage thresholds (80% line/branch minimum, 95% for critical paths), AAA pattern adherence, pytest-mock usage, test isolation, naming clarity, type hints, and flaky pattern detection. Use when auditing test quality before a release, reviewing coverage gaps, checking tests for completeness or best practices, or validating mocking standards. Accepts a test file or directory as input and outputs prioritized findings grouped by HIGH, MEDIUM, and LOW priority.

Use when reviewing test suites for coverage, isolation, mock usage, naming conventions, or completeness. Activates on "review test coverage", "audit test quality", or "check tests for completeness" requests. Performs thorough checklist-driven review covering test isolation, mock correctness, AAA pattern adherence, and naming standards.

Use when the PLAN artifact from SAM Stage 2 needs contextualization against actual codebase state — grounds the design plan in reality by performing scope analysis (NEW/MODIFY/COMPLETE classification), conflict detection between plan assumptions and codebase patterns, and resource mapping to concrete file paths and integration points. Produces an updated ARTIFACT:PLAN registered via MCP with a Contextualization section appended.

When writing a git commit message. When task completes and changes need committing. When project uses semantic-release, commitizen, git-cliff. When choosing between feat/fix/chore/docs types. When indicating breaking changes. When generating changelogs from commit history.

When the user wants to edit, review, or improve existing marketing copy. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' or 'sharpen the messaging.' Use this when the user already has copy and wants it improved rather than rewritten from scratch. For writing new copy, see copywriting.

Explains Chain of Verification (CoVe) prompt design — a 4-step pattern separating generation from independent factual verification. Use when designing prompts that require factual accuracy, reducing hallucinations, checking technical standards or APIs, or producing step-by-step procedures where subtle errors are costly.

Register a plan artifact via the MCP backlog server. Use when you produce a document or report that downstream agents or worktree-isolated environments need to retrieve — feature-context, codebase-analysis, architect, task-plan, T0-baseline, TN-verification, or research artifacts. Triggers include "store an artifact", "register a plan artifact", "write a report to the backlog", "upload artifact content".

Creates a new backlog item and routes through the work-backlog-item create workflow. Use when the user asks to add a backlog item, log a task, capture a feature request, or track a work item.

Use when creating a new feature task with structured tracking, phases, and documentation. Activates on "create a feature task", "set up development tracking", or "plan a feature implementation" requests. Produces a comprehensive feature development task with acceptance criteria, phase breakdown, and tracking artifacts ready for SAM pipeline execution.

Use when creating a new feature development task — scaffolds a structured task file at .claude/tasks/{feature-name}.md with phased breakdown (Design, Implementation, Testing, Documentation), acceptance criteria, context preservation, and TaskCreate tracking. Activates on "create a feature task", "set up development tracking", "plan a feature implementation", or when preparing work for python-cli-architect or python-pytest-architect agents.

Analyze git branches and generate structured MR/PR descriptions with domain-based change categorization — bug fixes, enhancements, technical debt, documentation, testing, build/CI, and non-functional changes. Use when preparing merge request descriptions, pull request bodies, writing changelogs from git diffs, documenting branch changes, or generating release notes. Works with GitHub and GitLab without requiring JIRA or issue tracker integration.

Creates a GitHub milestone on the current repository and returns its number for downstream use. Use when starting a new sprint, release, or theme grouping of backlog items. No args triggers guided intake (title, due date, description); 'quick {title}' skips to description only. Checks for duplicates before creating. Returns milestone number for /group-items-to-milestone.

Create GitHub Releases with AI-analyzed changelogs for every calendar day with commits on origin/main. Use when creating daily release notes, backfilling releases, or generating AI-categorized changelogs per day. Uses collect → bucket → analyze → synthesize → publish pipeline via Haiku subagents. Idempotent — skips up-to-date days, updates releases where new commits were added. Accepts optional --start-date, --end-date, --branch, and --dry-run arguments.

Use when querying, modifying, or converting JSON, YAML, TOML, XML, CSV, HCL, or INI with dasel v3. Complete reference for selectors, functions, conditionals, variables, spread operator, type casting, and format-specific patterns.

Use when exploring unknown structured data files with dasel v3 — discover schema, list keys, find nested values, sample arrays, identify data types across JSON, YAML, TOML, XML, CSV, HCL, INI formats

Use when modifying, converting, or transforming structured data with dasel v3 — in-place mutations, format conversion, batch operations, array manipulation, object construction, and merge patterns across JSON, YAML, TOML, XML, CSV, HCL, INI

Structured 6-phase Python debugging workflow covering problem intake, scoping, hypothesis formation, systematic investigation, root-cause analysis, and fix implementation. Use when diagnosing tracebacks, test failures, AttributeError, TypeError, intermittent failures, async/await issues, or any unexpected Python behavior. Applies a dual-hypothesis approach (implementation bug vs test bug), minimal reproduction isolation, data-flow tracing, and produces a structured Bug Investigation Report with confirmed root cause and regression test.

Delegation prompt template enforcing WHERE-WHAT-WHY structure for sub-agent prompts. Use when assigning work to a sub-agent, before invoking the Agent tool, preparing prompts for specialized agents, or needing the OBSERVATIONS-SUCCESS-CONTEXT format with authoring rules and pre-send checklist. For comprehensive delegation guidance, activate the agent-orchestration how-to-delegate skill.

Quick delegation template for sub-agent prompts. Use when assigning work to a sub-agent, before invoking the Agent tool, or when preparing prompts for specialized agents. Provides the WHERE-WHAT-WHY framework. For comprehensive delegation guidance, activate the agent-orchestration how-to-delegate skill.

Enforce anti-AI UI design rules based on the Uncodixfy methodology. Use when generating HTML, CSS, React, Vue, Svelte, or any frontend UI code. Prevents generic AI aesthetic — soft gradients, glassmorphism, hero sections in dashboards, oversized rounded corners, and decorative copy. Applies constraints from Linear/Raycast/Stripe/GitHub design philosophy for functional, honest, human-designed interfaces. Triggers on UI generation, dashboard building, component creation, CSS styling, landing page design, or any task producing visual interface code.

Use before any CLI/TUI display code is written, modified, or audited — runs the 7-stage discipline (Context, Register, Shape brief, Implement, Critique, Audit, Polish) for Typer, Rich, Textual, and Questionary work, grounded in per-project PRODUCT.md and DESIGN.TUI.md/DESIGN.md. Triggers on output formatting, display design, interactive prompts, visual consistency, TUI layout, progress display, dashboard design, design audit, design polish, design critique, shape brief, register decision (brand-cli vs product-cli), and AI-slop checks.

Routes to the correct dh skill entry point by intent. Use when unsure which development-harness skill to invoke, starting a development workflow, or invoking /dh directly. Covers capture, groom, plan, execute, single task, quality gates, and milestone routing.

Development harness plugin documentation index. Use when looking up SAM pipeline, backlog lifecycle, SDLC layers, task file format, plan artifacts, quality gates, or dispatch schema documentation.

Use when starting a new feature, gathering requirements for an unfamiliar domain, refining a vague idea into actionable scope, or when a user request is ambiguous or underspecified. Conducts SAM Stage 1 discovery — structured requirements gathering through user discussion, asking WHO/WHAT/WHEN/WHY and never HOW. Produces the ARTIFACT:DISCOVERY document containing feature requirements, NFRs, goals, anti-goals, references, and resolved questions. Supports backlog item self-initialization via a #N argument.

Orchestrate parallel agent teams as a manager — not a micromanager. Use when coordinating 2+ independent workers, running SAM task waves, relaying discoveries between worker waves, handling blockers, or synthesizing team results. Covers both SAM structured dispatch (task file does the work) and ad-hoc dispatch (reference agent-orchestration for prompt template).

Use when starting, stopping, or checking the dot-dash live session dashboard — a real-time browser UI that monitors all active Claude Code sessions, streams transcripts, and supports prompt injection

Research community usage patterns, real-world gotchas, and client compatibility for a specific known library, tool, or protocol feature. Use when a technical-researcher orchestrator needs community-sourced evidence about a named library or feature — bug reports, workarounds, compatibility gaps, and patterns from issue trackers and discussions. Distinct from the broad ecosystem-researcher agent — this skill targets a KNOWN entity and mines what real users have actually experienced, not what exists in a domain.

Design pattern for converting rule-following, checklist, or rubric skills into a fan-out map-reduce ensemble of parallel rigid sub-agents with corroboration-weighted merge; worker model tier and diversity are knobs matched to inference load and stakes, not fixed values. Apply when creating or refactoring a skill or agent that applies 10+ independent criteria in a single pass. Triggers on: 'review against a checklist', 'fan out', 'map reduce review', 'ensemble', 'split the rules', 'apply rubric', or any large ruleset being applied by one agent in one pass. NOT for tight single-pass transforms or rulesets under 5 criteria.

Use when refactoring is complete and needs validation. Performs holistic review of completed plugin refactoring, validates improvements against original assessment score, checks for documentation drift, and creates follow-up task files if issues remain

Dasel v3 query patterns for Hibernate .hbm.xml mapping files — entity-table binding, Java property-to-column extraction, one-to-many set/list/bag relationship tracing, many-to-one foreign key discovery, batch scanning across 60+ HBM files. Use when querying Hibernate ORM class mappings, extracting schema metadata from Java persistence layer, or auditing entity-column relationships in enterprise legacy codebases.

Dasel v3 query patterns for InstallAnywhere .iap_xml installer definitions — use when querying action sequences, discovering variables, resolving platform conditions, navigating panels, or comparing installer variants. Files are 2.5+ MB, 65,000+ lines — too large for context reads, requires structural dasel queries.

Dasel v3 selector patterns for Maven POM XML files — use when querying dependency versions, filtering by groupId or scope, extracting module hierarchy from parent POMs, or detecting version conflicts across enterprise multi-module Java projects. Load this skill when working with pom.xml files using dasel.

Dasel v3 selectors for Spring bean factory XML — use when querying any Spring ApplicationContext XML for bean discovery, dependency wiring, JMS destination mapping, property injection extraction, or cross-bean reference tracing. Load this skill before writing dasel selectors against Spring bean XML files (applicationContext.xml, *_beans.xml, spring-*.xml).

Dasel v3 patterns for querying Tomcat web.xml deployment descriptors — use when inspecting servlet enumeration, filter chain discovery, listener listing, context parameter extraction, or init-param inspection in web.xml files

Evaluate and iterate on the SDLC Layer Separation Architecture implementation. Runs validation checks (cross-references, doc completeness, layer metadata, integration points), produces a findings report, and supports iterative fixes. Use when validating first-pass implementation, before claiming layer work is complete, or when improving layer docs/schema.

Validate and iterate on the SDLC Layer Separation Architecture implementation across 6 check categories — cross-references, doc completeness, knowledge-explorer layer filters, research entry metadata, integration points, and plan consistency. Produces a structured findings report and optionally applies safe fixes. Use when validating a first-pass implementation, before claiming layer work complete, auditing layer docs or schema, or running --dry-run to preview findings without changes.

Use when debugging software, investigating incidents, diagnosing flaky tests, or analyzing performance regressions — enforces structured observation recording with evidence IDs, causality validation, and verification gates to prevent correlation-causation pollution. Use when an agent might otherwise summarize or speculate instead of reporting observed evidence.

Test harness for Claude Code skill argument substitution — demonstrates capture-block pre-declaration, XML tag referencing, unintentional variable corruption in code blocks, and correct placement of shell examples in reference files. Use when verifying substitution behavior before applying a pattern to other skills, testing how arguments flow from skill invocations, or understanding the pre-declaration and reference file pattern with greet/farewell/inspect actions.

Executes SAM Stage 5 — dispatches a single ARTIFACT:TASK file to a fresh stateless agent session, runs quality gates, and produces an ARTIFACT:EXECUTION with implementation results and verification output. Use when Stage 4 Task Decomposition is complete and tasks are ready for execution, when re-executing a task after Stage 6 returns NEEDS_WORK, or when dispatching a task to a language-appropriate specialist agent via the development harness pipeline.

Design and run controlled experiments using the experiment-registry MCP server — domain-agnostic, pluggable, mechanically enforced. Use when you need evidence that a change actually improves behaviour.

Integrates patterns from external sources (URLs or local files) into local skills, agents, and plugins. Use when comparing external agent definitions against local equivalents, extracting best practices from frameworks like GSD or BMAD-METHOD, enhancing local skills with external workflow patterns, or adding interoperability with external tool ecosystems. Runs a 3-phase workflow — parallel candidate mapping, contextual enhancement, and validation — with source attribution and backlog tracking for deferred enhancements.

Verifies claims in backlog items, skill documentation, or plugin content against primary sources using web lookups. Spawns parallel verification agents that must use WebFetch/WebSearch/gh — training data recall is explicitly rejected as evidence. Produces VERIFIED/REFUTED/INCONCLUSIVE verdicts with citations. Use when items are marked UNVERIFIED or when verifying tool API claims, CLI flags, or documented software behavior.

Verify claims in backlog items, skill documentation, or plugin content against primary sources. Spawns parallel @dh:fact-checker agents using mcp__Ref, mcp__exa, mcp__context7 as primary tools — training data recall is rejected as evidence. WebFetch/WebSearch are last-resort fallbacks. Produces VERIFIED/REFUTED/INCONCLUSIVE verdicts with citations. Triggers on "fact check", "verify claims", "check against primary sources", or when backlog items are marked UNVERIFIED.

Query and invoke tools on MCP servers using fastmcp list and fastmcp call. Use when you need to discover what tools a server offers, call tools, or integrate MCP servers into workflows.

Use when building, extending, or debugging FastMCP v3 Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, and testing. Grounded in local v3.3 docs — zero speculation.

Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.

Autonomous feature research and gap analysis. Use when starting /add-new-feature or analyzing existing architecture documents. Explores codebase patterns, identifies ambiguities, and produces feature-context-{slug}.md for orchestrator RT-ICA phase. Does NOT make technical decisions.

Classifies changed files into review treatment tiers before any reviewer decides SKIP. Prevents reviewers from misclassifying LLM prompt engineering artifacts (agents/*.md, skills/*/SKILL.md, CLAUDE.md, rules/*.md) as documentation-only. Load this skill when reviewing any diff that contains markdown or prose files. Authoritative definition is verdict-schema.md §2.5.

Summarize files by reading content, extracting key passages, and applying type-specific strategies. Activates on summarize this file, what's in this file, describe this codebase, file summary, analyze this file, tl;dr this file, what does this code do, explain this config, break down this script. Routes to strategies for code, config, data, documentation, markup, and binary files based on extension and word count.

Certifies that a feature achieves its original objectives via goal-backward verification (SAM Stage 7). Use when all tasks pass forensic review — starts from expected outcomes, works backwards to verify each was achieved, and returns CERTIFIED or NOT_CERTIFIED with specific gaps.

Wraps investigation requests with evidence-chain discipline. Use when asked to find out why something happens, research a root cause, debug an issue, or investigate unexpected behavior. Transforms vague investigation requests into reproducible-proof investigations with a 5-step protocol — disambiguate, reproduce, read source, build evidence chain, present findings. Invoke with /find-cause followed by a description of what to investigate.

Wrap investigation requests with evidence-chain discipline. Use when the user asks to find out why something happens, look into something, research a root cause, debug an issue, or investigate unexpected behavior. Transforms vague investigation requests into reproducible-proof investigations. Invoke with /dh:find-cause <description of what to investigate>.

Use when SAM Stage 5 Execution has completed and task results need independent verification against acceptance criteria. Dispatches a separate reviewer agent to fact-check implementation outputs and returns COMPLETE or NEEDS_WORK with specific findings and remediation tasks.

Single-verb branch-to-PR quality gate pipeline. Use when the user wants to gate, push, and open a PR for a branch in one command.

Generates one worker task prompt conforming to the CLEAR + selective CoVe task design standard and swarm-task-planner structure. Use when creating or rewriting a single TASK file or task block inside a plan — providing a title and brief description as input.

Install and configure the GitHub CLI (gh) for AI agent environments where gh may not be pre-installed and git remotes use local proxies instead of github.com. Use when gh command not found, shutil.which returns None, need GitHub API access for issues or PRs or releases or workflow runs, or repository operations fail with 'failed to determine base repo' error. Provides auto-install script with SHA256 verification, GITHUB_TOKEN auth with anonymous fallback, and the required -R flag pattern for proxy environments. Covers project management via GitHub Projects V2, milestones via REST API, issue lifecycle templates, and label taxonomy management.

Analyze git repository history to produce a codebase risk profile before code review. Probes repo size, runs 7 parallel git analysis pipelines (hotspots, bug magnets, bus factor, contributor momentum, firefighting frequency, newly added files), cross-references hotspots with bug magnets to identify high-risk files with primary owner annotation, and writes walkthrough/recon-report.md. Use before code review to prioritize reviewer attention, at the start of linear-walkthrough Phase 1 Discovery to weight file coverage, or when onboarding to an unfamiliar codebase.

GitLab CI/CD pipeline configuration and GLFM documentation expertise. Use when modifying .gitlab-ci.yml, optimizing pipelines, testing with gitlab-ci-local, writing GitLab README/Wiki content, configuring Docker-in-Docker workflows, or implementing CI Steps composition.

Grooms a backlog item by running RT-ICA assessment, enriching acceptance criteria, and preparing it for implementation. Use when the user asks to groom, refine, prioritize, or prepare a backlog item — activate with a backlog item number (#N) or title.

Grooms a GitHub milestone for parallel execution — batch-grooms ungroomed items, assesses scope gaps, analyzes cross-item dependencies via Impact Radius overlap, builds conflict groups, assigns items to execution waves, and persists the dispatch plan via dispatch_create_plan MCP tool. Calls dispatch_wave_start per wave to register state. Use when preparing a milestone for /work-milestone execution. Pass the milestone number as the first argument. Requires milestone items assigned via /group-items-to-milestone.

Assign backlog items to a GitHub milestone by bridging per-item files to GitHub Issues. Use when populating a sprint or release milestone, after create-milestone, or when a milestone number is specified with optional P0/P1/P2 or title filter. Creates missing GitHub Issues for selected P0/P1 items, assigns all selected items to the milestone, and updates Project V2 Status to Backlog.

Use when assigning backlog items to a GitHub milestone. Args: {milestone-number} [P0|P1|P2|title-filter]. Uses backlog list to load items, shows items with GitHub Issue status, lets user select which to assign. Creates missing GitHub Issues for selected P0/P1 items, assigns all to the milestone, updates Project V2 Status to Backlog. Use after create-milestone to populate a sprint or release.

Provides Hatchling build backend guidance for Python packaging — use when configuring pyproject.toml metadata, build targets (wheel, sdist, binary), file selection with git-style globs, build hooks, metadata hooks, version management (code/regex/env sources), editable installs, the hatch-vcs plugin, plugin development, build environment setup (UV/pip/Cython), setuptools migration, or troubleshooting Hatchling errors. Covers PEP 517/518/621/660 standards and context variable interpolation.

Use when working with Hatchling — configuring build system setup, pyproject.toml metadata, dependencies, entry points, build hooks, version management, wheel and sdist builds, package distribution, setuptools migration, or troubleshooting Hatchling build errors. Covers PEP 517/518/621/660 standards.

Comprehensive linting and formatting verification workflows. Provides automatic format-lint-resolve pipelines for orchestrators and sub-agents. Use when running linters, fixing ruff/mypy/bandit errors, ensuring code quality before completion, or resolving linting issues systematically.

Orchestrator delegation workflows for linting. Guides orchestrators on when and how to delegate to linting-root-cause-resolver and post-linting-architecture-reviewer agents. Use when orchestrating linting tasks, delegating quality checks, or reading linting resolution reports.

Linter-specific resolution workflows for ruff, mypy, pyright, and basedpyright. Provides systematic root-cause analysis procedures, suppression gates, and verification steps. Use when resolving linting errors as a sub-agent, implementing fixes systematically, or conducting type flow analysis.

Guide for creating Claude Code plugin hooks — language selection heuristic (Node.js default, match project runtime when obvious), hooks.json configuration, event selection, prompt-based vs command hooks, ${CLAUDE_PLUGIN_ROOT} paths, stdio suppression, timeout sizing, and testing. Use when adding hooks to a plugin, creating PreToolUse/PostToolUse/Stop/SubagentStop/SessionStart/UserPromptSubmit hooks, or wiring hook scripts to hooks.json.

Hook system fundamentals — all events, configuration structure, matchers per event type, environment variables, execution behavior, security, and debugging. Use when creating hooks, understanding hook events, matchers, configuration locations, environment variables, or troubleshooting hook issues.

Cross-platform hooks reference for AI coding assistants — Claude Code, GitHub Copilot, Cursor, Windsurf, Amp. Covers hook authoring in Node.js (ESM and CJS) and Python, per-platform event schemas, inline-agent hooks and MCP in agent frontmatter, common JSON I/O, exit codes, best practices, and a fetch script to refresh docs from official sources. Use when writing, reviewing, or debugging hooks for any AI assistant.

Hook JSON input/output API reference — what data hooks receive via stdin and what JSON they can return to control Claude Code behavior. Use when writing hook scripts, checking exit code behavior, building JSON output for PreToolUse permissions, or understanding event-specific input schemas.

Hook recipes and working examples — plugin hooks, frontmatter hooks in skills/agents/commands, prompt-based LLM hooks, and complete code examples in Python and Node.js. Use when building hook scripts, integrating hooks into plugins, implementing prompt-based hooks, or looking for hook configuration patterns.

Scientific delegation framework for orchestrators — provide observations and success criteria while preserving agent autonomy. Use when assigning work to sub-agents, before invoking the Agent tool, or when preparing delegation prompts for specialist agents.

Describe images and screenshots by viewing content with multimodal Read tool and documenting only visible elements. Activates on what's in this image, describe this screenshot, summarize this diagram, image summary, what does this screenshot show, explain this diagram, break down this chart. Handles UI screenshots, architecture diagrams, charts, photos, code screenshots, and terminal output. Never infers from filenames.

Quantitative cost measurement for technical research — token injection costs, payload sizes, context window consumption, and file-level counts from actual repo files. Use when a technical-researcher orchestrator needs the cost dimension of adding or changing something: how many tokens will it inject, how big are the artifacts, what is the context window impact? This is NOT blast-radius analysis (which files break) — it covers size, tokens, and performance cost only.

Manages feature implementation task state via SAM MCP tools. Use when querying task status, listing ready tasks, claiming tasks for execution, updating task timestamps, or coordinating multi-task feature rollout. Activated by the /dh:execution orchestrator to track progress — also activates directly when managing task files or configuring hook profiles.

Executes the SAM implementation loop when a task plan exists — dispatches ready tasks to specialist agents in parallel, manages bookend tasks (T0 baseline capture and TN verification), tracks concerns and contract violations per task, and relies on hooks to update task status. Use when a plan address (P{id}) or feature slug is provided after add-new-feature planning is complete. Manages task batches via sam_plan and sam_task MCP tools.

Use when a refactoring task file exists from /assessor and tasks need execution. Reads task files, resolves dependencies, delegates to specialist agents (SKILL_SPLIT, AGENT_OPTIMIZE, DOC_IMPROVE), and tracks completion with parallel orchestration

Process quality methodology for the process-siren agent — use before or during Mermaid conversion when the source process shows ambiguity, missing decisions, undefined actors, vague conditions, or structural weakness. Provides triage sequence, excellence criteria, and an improvement framework drawn from Lean, Six Sigma, BPR, Design Thinking, Systems Thinking, and Theory of Constraints. Activates when source content is poorly structured enough that converting it as-is would encode wrong behavior for AI readers.

Routes a Superpowers plan file through the /work-backlog-item pipeline and writes SAM task back-references and chunk annotations into the original plan. Use when given a path to a Superpowers plan file via $ARGUMENTS and needing to create a linked backlog item plus SAM task file.

Spawn and manage persistent interactive Claude Code CLI sessions with bidirectional communication via tmux. Provides spawn, send, read, status, list, and kill subcommands for orchestrating parallel peer sessions. Uses built-in --worktree and --tmux flags. Sessions stay alive for multi-turn steering. Triggers on "spawn claude session", "launch separate claude", "peer session", "inter-session communication", "shadow clone", "kage bunshin".

Transform transcript analysis findings into actionable improvements. Triggers on "generate hooks from findings", "improve agent", "fix anti-pattern", "kaizen improvement", "generate hook proposals", or "create improvement plan". Provides templates for hook generation, agent prompt refinement, skill patches, CLAUDE.md updates, and script automation from analysis data.

Manages the research/ knowledge base of verified tool and library research entries via knowledge-explorer.py. Commands — list (browse by category or SDLC layer), fetch-github (draft entry from GitHub repo), add (validate and route to category dir), update-append (append dated revision), generate-descriptions (repair missing or low-quality descriptions), migrate (convert inline-header entries to frontmatter). Use when browsing KB topics, adding or updating research entries, fetching GitHub repo metadata, generating descriptions in parallel via Haiku subagents, or migrating old-format entries to skill-spec frontmatter.

Produces a structured, end-to-end linear walkthrough of an unfamiliar codebase by orchestrating parallel subagents across four phases — discovery, tracing, validation, and synthesis. Use when onboarding to a new repository, understanding execution paths from entry points, generating navigable codebase documentation, or needing fact-checked coverage of architecture, deployment, testing, and operations.

Runs deterministic Python quality checks against a path or scope — formatting, linting, type checking, and typing-boundary policy. Use when checking or fixing code quality via prek, ruff, ty, pytest, or the check-typing-boundaries policy script. Reports results grouped by category; fixes only when explicitly requested.

Use when checking skill quality, validating frontmatter before commit, or diagnosing validator warnings. Runs the plugin validator on a skill, agent, or plugin directory — reports token complexity, broken links, frontmatter issues, and structural problems. Pass the path as an argument.

When calling LLM APIs from Python code. When connecting to llamafile or local LLM servers. When switching between OpenAI/Anthropic/local providers. When implementing retry/fallback logic for LLM calls. When code imports litellm or uses completion() patterns.

When setting up local LLM inference without cloud APIs. When running GGUF models locally. When needing OpenAI-compatible API from a local model. When building offline/air-gapped AI tools. When troubleshooting local LLM server connections.

Integrate MCP servers into Claude Code plugins — covers .mcp.json configuration, plugin.json mcpServers field, server types (stdio, SSE, HTTP, WebSocket), environment variable expansion, tool naming conventions, OAuth and token authentication, security best practices, and testing workflows. Use when adding an MCP server to a plugin, configuring MCP authentication, debugging MCP tool discovery, setting up Model Context Protocol integration, or choosing between stdio and SSE transport types.

Configure and manage Claude Code persistent memory (CLAUDE.md, auto memory, rules) across sessions. Use when setting up project memory, organizing .claude/rules/, managing auto memory files, creating CLAUDE.md with imports, debugging memory loading, or advising on memory hierarchy and best practices.

Mermaid diagram syntax reference for all diagram types — flowchart, sequence, class, state, ER, gantt, git graph, mindmap, timeline, user journey, pie, quadrant, XY chart, block, sankey, C4, kanban, and more. Use when constructing or debugging any Mermaid diagram definition.

Use when extracting specific data points from large agent output transcripts, kaizen analysis reports, or JSONL session files — tool timings, query counts, error summaries, or any structured facts — without loading raw data into orchestrator context. Activates when the orchestrator needs targeted facts from large files and context pollution must be avoided.

Entry point for refreshing the DH workflow graph after changes to skills, agents, Mermaid flowcharts, MCP tools, or artifact flows. Use after any structural workflow change to keep docs/dh-workflow-graph.json current.

Define and develop plugin mission statements — purpose, values, anti-patterns, and trade-offs. Use when creating a new plugin, auditing an existing plugin's alignment, or providing a reference for the alignment check loop to evaluate decisions against. Produces mission.json with [draft] status and creates a backlog interview task for the human to refine it.

Comprehensive guide for creating and managing MkDocs documentation projects with Material theme. Includes official CLI command reference with complete parameters and arguments, and mkdocs.yml configuration reference with all available settings and valid values. Use when working with MkDocs projects including site initialization, mkdocs.yml configuration, Material theme customization, plugin integration, or building static documentation sites from Markdown files.

MkDocs documentation project reference covering CLI commands, mkdocs.yml configuration, Material theme setup, and plugin integration. Bundled references include complete CLI parameters, all mkdocs.yml settings with valid values, Material theme customization options, and plugin configs for mkdocstrings, mermaid2, mkdocs-gen-files, mkdocs-literate-nav, and mkdocs-typer2. Use when initializing a MkDocs site, configuring mkdocs.yml, customizing the Material theme, integrating plugins, building static docs from Markdown, or generating API documentation from Python docstrings.

Modern Git workflows, best practices, and commands. Use when the user asks about Git branch management (git switch vs checkout), file restoration (git restore), fixup commits, autosquash rebasing, git worktrees, rerere, force-with-lease, repository cleanup (git clean, stale branches, bloat analysis), history navigation (revision syntax, range notation, pickaxe search), recommended global git config, or GitButler and the but CLI.

Applies and teaches Python 3.11+ modernization patterns with PEP citations. Use when reviewing or writing Python code to apply built-in generics (PEP 585), pipe unions (PEP 604), walrus operator (PEP 572), match-case (PEP 634), Self type (PEP 673), exception notes (PEP 678), StrEnum, tomllib, pytest-mock fixtures, Typer Annotated syntax, or Rich terminal output — or when refactoring legacy typing imports or elif chains to modern equivalents.

Use when reviewing Python code for modernization opportunities, writing new Python 3.11+ code to ensure modern patterns, or refactoring legacy code to current idioms. Covers proper types, DRY, SRP, framework patterns, and idiomatic Python improvements.

Dispatches four parallel perspective reviewers (Security, Performance, Quality, Accessibility) against a diff via TeamCreate and dh:task-worker. Creates an ephemeral SAM plan with four tasks, collects structured verdicts via SendMessage, prints one summary line per perspective, and exits non-zero if any perspective returns REJECT. SKIP is a passing outcome.

Combines multiple summaries into coherent synthesis. Activates when user requests combine these summaries, synthesize results, merge findings, or multi-source synthesis. This is the reduce step after individual sources have been summarized. Enforces deduplication, conflict surfacing, confidence scoring across sources, and attribution to all sources.

Use when writing delegation instructions to subagents, authoring CLAUDE.md files, rules, skills, or agent definitions, or auditing existing AI-facing content for bloat. Activates on "write a rule for", "add to CLAUDE.md", "create an agent", "update memory", or any request to author AI-facing instruction content. Removes discoverable data, explained-away knowledge, invented constraints, and stale cached facts.

Optimize existing CLAUDE.md, SKILL.md, agent definitions, and other AI-facing files for Claude comprehension and economy. Scope: optimization of existing content only — not upstream sync, not read-only auditing. Measures baseline metrics, delegates to @ai-doc-optimizer agent with file-type-specific context, runs independent verification via second agent, measures post-optimization metrics, and presents comprehensive before/after report. Supports iterative mode for large targets. Use when improving prompt effectiveness, reducing token waste, or rewriting instructions for LLM consumption. Invoke with /optimize-claude-md <file-or-directory>.

Use when orchestrating a Python development task via specialized agents. Activates on "build a Python CLI", "add a feature", "write tests", "refactor Python code", "debug Python", "code review", or any multi-agent Python workflow. Invoke as /orchestrate with a task description or alone to use conversation context.

Use when implementing a Python feature, adding CLI commands, writing pytest suites, reviewing Python code, debugging, or refactoring. The primary Python engineering workflow orchestrator — routes to SAM track (multi-step feature additions, work spanning 2+ agents or files, durable progress tracking) or Direct track (single-focused tasks: bug fix, tests for one file, one-shot refactor, code review). Delegates to python-cli-architect (implementation), python-pytest-architect (tests), code-reviewer (review), python-cli-design-spec (architecture). Triggers on any Python task requiring specialist agent coordination or multi-agent execution.

Provides agent selection criteria, workflow patterns (TDD, feature addition, code review, refactoring, debugging), quality gates, and python-cli-architect vs stdlib-scripting routing for Python engineering tasks. Activated by python-engineering:orchestrate at Step 1 before any task is routed. Also activates when an orchestrator needs to select the correct Python specialist agent or chain agents across a multi-step Python workflow.

Master multi-agent orchestration using Claude Code's swarm system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns. This facade loads specialist skills for primitives, spawning, operations, and patterns.

Facade skill for multi-agent swarm orchestration in Claude Code — routes to specialist skills covering primitives, spawning, operations, and patterns. Use when coordinating multiple agents in parallel, building pipeline workflows with dependencies, running parallel code reviews, creating self-organizing task queues, designing divide-and-conquer workloads, or choosing between TeamCreate and Agent tool approaches. Loads swarm-primitives (team lifecycle and message flow), swarm-spawning (agent types and backends), swarm-operations (tool API and error handling), and swarm-patterns (six orchestration recipes with complete workflows).

Orchestrator context window discipline enforcement. Prevents the orchestrator from reading source files it will not edit, running diagnostic commands that waste context, and rationalizing delegation bypasses. Use when setting up orchestrator guardrails, reviewing delegation discipline, or diagnosing context window waste in multi-agent workflows. Activates PreToolUse hooks that surface decision points before source file reads and diagnostic command execution.

Orchestrator discipline plugin documentation index. Load when needing to read about orchestration patterns, delegation rules, or context window management.

This skill should be used when the user asks to install Perl modules, use cpanm, create a cpanfile, manage Perl dependencies, set up Carton, configure local lib, or mentions CPAN, cpanminus, module installation, or Perl package management.

This skill should be used when the user asks to "write a Perl script", "create Perl code", "modern Perl best practices", "Perl 5.30+", "use strict warnings autodie", or mentions Perl pragmas, subroutines, error handling, or scripting patterns. Provides comprehensive Perl 5.30+ development guidance.

This skill should be used when the user asks to "install perlbrew", "set up Perl environment", "install Perl version", "manage Perl versions", "switch Perl version", "install plenv", or mentions Perl version management, development environment setup, or multiple Perl installations.

This skill should be used when the user asks to lint Perl code, run perlcritic, check Perl style, format Perl code, run perltidy, or mentions Perl Critic policies, code formatting, or style checking.

This skill should be used when the user asks to write Perl tests, test Perl code, use Test More, run prove, create test suite, mock Perl, or mentions Perl testing, TAP, Test Class, Test Deep, or test-driven development in Perl.

This skill should be used when the user asks to "validate Perl script", "check Perl syntax", "verify Perl code", "/perl-validate", or mentions script validation, compile check, security review, or best practice compliance for Perl code.

Runs information completeness pre-pass before task decomposition and plan generation. Use when grooming backlog items, generating plans, decomposing tasks under uncertainty, or working in brownfield and refactor scenarios. Localizes missing inputs to affected tasks only — does not block plan generation. Produces completeness summary (APPROVED-FOR-PLANNING, APPROVED-WITH-GAPS, or BLOCKED-FOR-PLANNING), missing input report with dependency mapping, required unblock actions, and planning annotations for downstream tasks. Non-blocking sister to dh:rt-ica — use dh:rt-ica at the S2 implementation gate where missing inputs must halt execution.

Use when Stage 1 Discovery is complete and design must begin — transforms the ARTIFACT:DISCOVERY into an actionable ARTIFACT:PLAN via RT-ICA prerequisite verification. Produces approach, components, success criteria, acceptance tests, and risks. Blocks on missing prerequisites before design proceeds.

Use when creating a new Claude Code plugin from scratch — orchestrates prerequisite check, user discussion, parallel research, design with verification, atomic implementation, multi-layer validation, documentation, and final verification. For existing plugin improvement, use /plugin-creator:plugin-lifecycle instead.

Plugin creator documentation index. Load when needing to read about plugin validation error codes, skill creation, agent creation, or plugin structure.

Orchestrate the full plugin development lifecycle from blank canvas to marketplace-ready. Use when creating a new plugin, improving an existing plugin, fixing validation errors, or taking a plugin through assessment, research, design, creation, debugging, optimization, and verification. Complements /plugin-creator:plugin-creator which provides the detailed new-plugin creation workflow with discussion capture, parallel research, and atomic implementation.

Per-project plugin configuration via .local.md files — covers the .claude/plugin-name.local.md pattern for storing user-configurable settings with YAML frontmatter and markdown body. Use when implementing plugin settings, reading YAML frontmatter from hooks, creating configuration-driven behavior, managing agent state files, or adding per-project plugin configuration. Covers file structure, parsing techniques, common patterns (temporarily active hooks, agent state management, configuration-driven behavior), security considerations, and best practices.

Configures and implements git hooks using pre-commit or prek (Rust-based drop-in replacement) for automated code quality checks, formatting, linting, and commit message processing. Use when setting up .pre-commit-config.yaml, implementing prepare-commit-msg or commit-msg stage hooks, designing .pre-commit-hooks.yaml hook definitions for distribution, troubleshooting hook installation or execution, or managing hook stages across multi-language projects.

Use when setting up automated code quality checks on git commit, configuring .pre-commit-config.yaml, implementing git hooks for formatting or linting, creating prepare-commit-msg hooks, or distributing a tool as a pre-commit hook. Covers pre-commit and prek for multi-language projects.

Transforms validated /linear-walkthrough artifacts into one presentation-ready deck outline per major codebase component. Use when the user asks to create a presentation, prepare a walkthrough deck, build an onboarding deck, produce an architecture review deck, or summarize a codebase for a technical audience. Requires walkthrough directory output -- reads unified walkthrough, per-section files, validation reports, coverage maps, entry points, and open questions -- then orchestrates four parallel agent phases to produce slide outlines with speaker notes, evidence references, and suggested visuals.

Optimize CLAUDE.md files and Skills for Claude Code CLI. Use when reviewing, creating, or improving system prompts, CLAUDE.md configurations, or Skill files. Transforms negative instructions into positive patterns following Anthropic's official best practices.

Generates professional PyPI-compliant README files in Markdown or reStructuredText. Use when creating a Python package README for PyPI publication, converting between README.md and README.rst formats, validating markup with twine check before publishing, configuring the readme field in pyproject.toml, integrating sphinx-readme to generate PyPI-compatible RST from Sphinx docs, troubleshooting rendering errors on PyPI, or previewing README rendering locally with grip or docutils.

Use when creating a README for a Python package, preparing for PyPI publication, fixing README rendering errors found by twine check, choosing between README.md and README.rst, or configuring the readme field in pyproject.toml. Generates professional, PyPI-compliant README files.

Guided workflow for adding new features to Python projects. Use when planning a new feature implementation, when adding functionality with proper test coverage, or when following TDD to build features incrementally.

Executes a four-phase feature addition workflow (Discovery, Planning, TDD Implementation, Verification) for Python projects. Use when adding a new feature end-to-end — discovering project structure and integration points, drafting a feature spec with MoSCoW-prioritized requirements and BDD acceptance criteria, implementing via test-first TDD cycles, then verifying with ruff lint, ty type checks, and 100% coverage on new code.

Debug functional issues in Python code using specs, logs, and observed behavior. Use when a feature isn't working as specified, when investigating runtime errors, or when scoping a problem before implementing a fix.

Use when building CLI applications with Typer and Rich — creating commands with Annotated parameter syntax, defining arguments and options, composing subcommands, async concurrent CLI tasks with semaphores, testing with CliRunner, PEP 723 shebang scripts, progress bars, Rich terminal output, or non-TTY display width handling.

Activates on any Python task involving *.py files, uv, ruff, ty, pytest, or pyproject.toml — establishes Python 3.11+ coding standards, SOLID design guidance, strict typing policy, testing defaults (pytest + pytest-mock), tooling expectations (uv, ruff, ty, hatchling), and code smell detection as design signals. Routes to specialist skills for TDD, CLI, web, data, async, or constrained environments.

Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.

Use when building Python 3.11+ CLI apps (Typer/Rich), writing pytest test suites, fixing ruff linting or ty/mypy type errors, configuring pyproject.toml, creating portable scripts, or reviewing Python code. Activates on all Python implementation tasks — routes to specialist agents for CLI architecture, test design, packaging, and code review. Authoritative reference for modern Python 3.11-3.14 patterns and TDD workflows.

Python 3 development plugin documentation index. Load when needing to read about Python implementation patterns, task file conventions, or stdlib scripting.

Configures pyproject.toml and Python packaging using PEP 517/518/621/660/723 standards. Use when creating or updating pyproject.toml, selecting a build backend (hatchling/setuptools/flit), configuring ruff, ty, mypy, pytest, or coverage tool sections, setting up dependency constraints or optional extras, defining CLI entry points, configuring pre-commit hooks, establishing src-layout directory structure, or preparing a package for PyPI publishing.

Configure pyproject.toml and Python packaging for distribution. Use when setting up a new Python package, when configuring build tools and dependencies, or when preparing a project for PyPI publishing.

Set up CI/CD pipeline for Python package publishing to PyPI. Use when preparing to publish a package, when setting up automated releases, or when configuring GitHub Actions or GitLab CI for Python projects.

Configures CI/CD pipelines for automated Python package publishing to PyPI or GitLab Package Registry. Use when creating GitHub Actions or GitLab CI release workflows, setting up trusted publishing or API token-based PyPI authentication, configuring version management with git tags and hatch-vcs, writing pyproject.toml publishing metadata, testing packages against TestPyPI, or documenting the release process for a Python project.

Comprehensive Python code review checking patterns, types, security, and performance. Use when reviewing Python code for quality issues, when auditing code before merge, or when assessing technical debt in a Python codebase.

Use when building dependency-free Python 3.11+ scripts for airgapped, stdlib-only, or restricted environments where third-party package installation is prohibited — triggers on "stdlib-only", "airgapped", "no dependencies", "no internet", "restricted environment", or confirmed environments where external packages cannot be installed.

Guides test-driven development for Python using a five-phase red-green-refactor cycle. Use when asked to write tests first, apply TDD, do test-first implementation, or follow red-green-refactor — designs typed interfaces and Protocol classes, writes failing pytest tests (RED), implements minimal passing code (GREEN), verifies with prek or ruff plus pytest-cov, and enforces a quality gate requiring all tests pass with no lint or type errors and coverage at or above 80 percent.

Use when designing pytest test suite architecture, planning test coverage strategy, or reviewing test structure for Python 3.11+ projects. Activates on "design a test strategy", "plan test coverage", "create test architecture", or when TDD/BDD/property-based testing patterns are mentioned. Guides fixture design, parametrization, async testing, and mutation testing coverage decisions.

Guides pytest test suite architecture and coverage strategy for Python 3.11+ projects. Activates when designing test architecture, planning test pyramid distribution, choosing between unit/integration/property-based/BDD strategies, structuring fixture hierarchies, configuring branch coverage thresholds, or applying mutation testing to critical code paths.

Pytest testing patterns for Python — fixtures (session/module/function/factory), AAA structure, behavioral naming, coverage targets by code type, property-based testing with Hypothesis, and mutation testing with mutmut. Use when writing tests, designing fixtures, configuring coverage, or applying parametrize, async testing, or property-based strategies.

Use when working with Python tooling — uv package management, Hatchling build backend, ty or mypy type checker configuration, ruff linting, pre-commit hook setup, TOML read-write with tomlkit or tomllib, or PyPI packaging and release workflows. Routes to standalone specialist skills for deep dives on any single tool.

Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.

Python web and API development enforcing strict route/domain/data layer separation, Pydantic v2 strict request-response models, edge-resolved auth, and async-safe HTTP clients. Use when working with FastAPI, Starlette, Django, Flask, HTTP endpoints, request models, authentication flows, async handlers, or any Python web framework task.

Use when the python-cli-architect agent needs project structure reference and task completion quality gates for Python CLI projects. Loaded automatically by the python-cli-architect agent — covers package layout, Hatchling configuration, and implementation quality criteria.

Use when writing Python scripts that must run on Windows, Linux, and macOS — especially when Rich or Typer output breaks on Windows, when dealing with Unicode/encoding errors, ANSI escape handling, terminal detection, path separators, or console color support. Provides verified cross-platform patterns covering stdout/stderr encoding guards, Windows console quirks, terminal capability detection, and portable I/O for CLI, TUI (Rich/Textual), and GUI environments.

Badge design and selection knowledge base for shields.io badges in README files. Use when writing or updating READMEs, choosing badge layouts, selecting badge styles, adding project health indicators, or picking Simple Icons logo slugs. Covers shields.io URL encoding rules, static vs dynamic badge selection, style variants (flat/flat-square/for-the-badge/social/plastic), layout patterns (two-tier/inline/centered), project-type badge sets for Python/JS/Rust/Claude plugins, color reference, non-obvious logo slugs, and common anti-patterns to avoid.

Strategic rebase with mandatory pre-analysis. Use when asked to rebase a branch onto main (or any target). Runs a file-level diff of both sides before touching git, produces a per-file disposition plan (KEEP/MERGE/DROP/REWRITE), and only then executes the rebase. Prevents surprise conflicts and silent data loss from rebasing without knowing what changed on both sides. Triggers: 'rebase', 'rebase onto main', 'rebase this branch', 'rebase and merge', 'update branch from main'.

Start a complete plugin refactoring workflow that analyzes plugin structure, creates a refactoring plan with tasks, and guides through execution. Use when you need to refactor an entire plugin — triggers assessment, design, planning, and parallel agent execution phases.

Assess and refactor oversized or multi-domain skills. First determines whether splitting or references/ extraction is appropriate — then executes the correct action. Use when a skill exceeds token thresholds (SK006/SK007) or covers multiple independent domains. Performs candidate assessment before any structural changes; cohesive single-intent skills are redirected to references/ extraction instead of splitting. When splitting is warranted — domain analysis gate, split plan, new SKILL.md generation, validation, and backwards-compatible facade conversion.

Bulk-refresh research entries in ./research/ using parallel research-curator agents. Use when /refresh-research is invoked, stale research needs updating, or bulk re-verification of research entries is requested. Inventories entries by review date and age, runs RT-ICA pre-flight, spawns agents in waves of 5, updates README and Freshness Tracking, lints and commits. Supports --all, --stale, --category, --layer, and --dry-run flags.

Orchestrate research entry lifecycle in ./research/ — create, batch-import, refresh stale entries, and validate structure. Use when asked to add a tool, research a URL, document a library, refresh research, validate entries, or given any tool or library URL. Supports --batch (parallel multi-URL), --rerun (refresh one or all entries), and --validate (structural check with auto-fix of error-severity issues).

Synthesis step in the multi-angle technical research pipeline. Receives structured outputs from all four research angles (api-state, ecosystem-research, impact-measurement, codebase-auditor), applies cross-angle signal weighting and conflict resolution, and produces a single synthesized Research section. Invoked by the technical-researcher orchestrator after all angle skills complete. Returns content to the orchestrator — does not write to the backlog.

Reviews Python code across 9 dimensions — type safety, error handling, security, performance, modern patterns, design clarity, typed-boundary compliance, test quality, and documentation. Use when performing code review, PR review, pre-merge quality checks, or assessing Python for security vulnerabilities, bare except clauses, Any usage outside boundaries, or missing input validation at system boundaries.

Configure Claude Code permissions — tool approval rules, permission modes, managed policies, and sandboxing. Use when setting up permission rules, configuring allow/deny/ask policies, debugging permission prompts, deploying managed settings for organizations, or controlling Bash/Read/Edit/WebFetch/MCP/Agent tool access.

Use when writing Python code with the Rich library — console output with markup, tables, progress bars, syntax highlighting, pretty printing, logging, or tracebacks. Covers Console, markup syntax, renderables (Panel/Table/Tree), Progress, Live, RichHandler, and the __rich_console__ protocol.

Scan Claude Code session transcripts to find the strongest user reactions to assistant instruction-following failures, reconstruct the triggering assistant output, and render a shareable terminal-style PNG artifact. Use when you want to surface and share a moment where the assistant completely missed what was asked — captures what they were doing, what Claude said, and how the user reacted. Triggers on: "rtfp", "read the fucking prompt", "find my worst AI moment", "make a rage screenshot from this session".

Read The Fucking Prompt — finds the strongest user reaction to an AI instruction-following failure in a chosen session, reconstructs what the assistant did wrong, and renders a shareable terminal-style PNG. Use when asked to find rage moments, generate a rage receipt, or capture a frustration incident from a session.

Use before creating plans, delegating to agents, or defining acceptance criteria — performs Reverse Thinking Information Completeness Assessment (RT-ICA) to surface missing prerequisites and block planning until all required inputs are verified. Activates on specs, PRDs, tickets, RFCs, architecture designs, and multi-step engineering tasks. Integrates with CoVe-style planning pipelines.

Use when facing unknowns, debugging without a clear cause, or making architecture decisions — enforces hypothesis-driven scientific reasoning through observation, hypothesis formulation, prediction, experiment design, and evidence-based conclusion. Use when previous attempts have failed or the problem space involves uncertainty.

Use when searching a codebase by behavior, intent, or natural language description rather than exact identifiers. Activates the CocoIndex Code MCP server for semantic code search — finding implementations without knowing exact names, exploring unfamiliar codebases, or locating code by concept.

Look up prior Claude Code sessions when context is lost or forgotten. Use when asked what was done before, what happened in the last session, or any request to recall past conversation history, prior decisions, experiments, or outcomes. Indexes and searches raw JSONL transcripts from ~/.claude/projects/ via DuckDB. Returns verbatim user messages, summarizes AI actions and sub-agent outcomes, detects tool errors and user frustration signals, and reports tool usage statistics. Summaries cached at ~/.claude/kaizen/session-summaries/.

Use when installing, updating, or troubleshooting the dasel v3 binary — runs the install script, verifies installation, and diagnoses PATH and download issues

Use when creating or updating the project skill discovery config — generates or regenerates .dh/skill_discovery.yaml by scanning the repo to infer tech stack, inventorying installed skills via npx skills list, loading candidate skill content before suggesting, and writing a config-driven skill injection file. Triggers on /dh:setup-skill-discovery invocations and programmatic --auto calls from add-new-feature Phase 3.

Walks a user from one rough content idea to a finished post, platform-adapted variants, and repurposed follow-on assets using a 7-step sequential prompting workflow. Use when the user wants to turn a half-formed idea, frustration, story, or client situation into publishable content quickly and consistently — covering idea extraction, hook generation, outline, draft, humanizer pass, platform adaptation (LinkedIn, Reddit, X/Twitter, newsletter, Telegram), and repurpose engine.

Validates and corrects Python shebangs and PEP 723 inline script metadata by applying four shebang-selection rules. Use when auditing or fixing shebangs in Python files — choosing between plain python3 and the uv shebang for standalone scripts with external dependencies, enforcing correct uv flag ordering (--quiet before run subcommand), adding or removing PEP 723 metadata blocks to match actual import requirements, checking execute bit presence, or avoiding redundant transitive dependencies when typer is declared (typer bundles rich and shellingham automatically).

Validate Python shebangs and PEP 723 inline script metadata. Use when checking if Python files have correct shebangs based on their dependency requirements, when fixing incorrect shebang patterns, or when adding PEP 723 script blocks to standalone scripts with external dependencies.

Use when creating a new skill or updating an existing skill that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations. Activates on "create a new skill", "add a skill to plugin", or "update existing skill".

Builds comprehensive Claude Code skills using parallel research agents — categorization, parallel documentation gathering, anti-hallucination checkpoints, and final validation. Use when building a skill from official docs, when "research for skill" or "create comprehensive skill" is requested, or when extensive multi-source documentation gathering is needed before skill creation.

Sync a skill's content against the upstream library it covers — check whether the API descriptions, version references, and SOURCE: citations inside SKILL.md and its reference files still reflect what the library actually does today. Use when a library has released updates that may have changed the APIs the skill covers, when a skill references an old library version, when asked to sync, refresh, update, or check a skill against upstream docs, when the skill's content may have drifted from current library behavior, or when SOURCE: citations are stale. Accepts a SKILL.md path, skill directory, or plugin directory.

Executes the implementation phase of the python-engineering stinkysnake modernization workflow. Use when stinkysnake phases 1-8 are complete — modernization plan reviewed, interfaces designed, and failing tests written. Implements functions in dependency order (types, data structures, utilities, core logic, integration, entry points) applying modern Python patterns (Protocol, dataclass, Pydantic, modern type annotations, httpx, orjson). Runs iterative pytest loops until all tests pass, then verifies with static analysis via prek or ruff. Success criteria — all tests pass, no type errors, no lint errors, coverage meets project threshold.

Implementation phase for stinkysnake workflow. Use when tests are written and plan is ready. Implements functions following the modernization plan, runs tests until passing.

Routes Python engineering tasks to specialist skills by matching trigger patterns before any architecture, plan, or code is written. Use when working with Typer CLI frameworks, Rich or Textual terminal UIs, CLI UI/UX design, questionary prompts, FastMCP/MCP servers, ty type checker, uv package manager, Hatchling build backend, TOML editing, pre-commit/prek hooks, async Python, PyPI packaging, complex linting, technical debt modernization, testing workflows, feature development, or stdlib-only scripting.

Use as the routing layer for Python development tasks — matches task descriptions against trigger lists and activates specialist skills before starting work. Covers Typer, Rich, Textual, FastMCP/MCP, ty type checker, uv, Hatchling, TOML editing, pre-commit/prek, async Python, PyPI packaging, complex linting, and technical debt modernization.

Shared Python 3.11+ development standards covering type safety (ty, native generics, Protocol, TypeIs), layered architecture, error handling, performance, identifier naming, UI/CLI patterns (Rich/Typer), testing requirements (pytest, 80% coverage, TDD), and quality gates. Activates when any Python skill or agent needs to apply shared standards for implementation, code review, refactoring, or test authoring.

Transitions a GitHub milestone from planning to active execution. Use when the team is ready to begin a sprint or release cycle — bulk-transitions open issue labels from status:needs-grooming to status:in-progress, updates GitHub Projects V2 Status to In Progress, and confirms before applying changes. Requires milestone number as argument. Use after /group-items-to-milestone.

Start or complete a specific refactoring task from a task file. Use when a sub-agent needs to pick up a refactoring task, update its status, implement acceptance criteria, and run verification steps.

Use when executing a SAM task — claims the task via MCP to set it IN PROGRESS, writes active-task context for hooks, loads task-level skills, implements against acceptance criteria, and marks complete via --complete flag. Triggers on task execution within the implement-feature loop or when an agent picks up a specific task from a plan file.

LAST RESORT for stdlib-only Python 3.11+ scripts in CONFIRMED restricted environments (airgapped systems, no uv, no internet access). Creates portable dependency-free scripts using argparse, logging, config management (JSON/TOML/INI), and cross-platform patterns. Use ONLY when environment restrictions prevent Typer+Rich with PEP 723. Triggers on "stdlib-only script", "no dependencies", "airgapped", "restricted environment", "portable script no network". For standard CLI development, use python-cli-architect with Typer+Rich instead.

Progressive Python quality improvement with static analysis, type refinement, modernization planning, plan review, and test-driven implementation. Use when addressing technical debt, eliminating Any types, applying modern Python patterns, or refactoring for better design.

Nine-phase Python quality improvement system for file paths passed as arguments. Runs prek/ruff/ty static analysis with auto-fixes, inventories Any types and typing gaps, plans Protocol/Generic/TypeGuard/TypedDict/dataclass modernization, forks a code-reviewer agent to critique the plan, refines the plan, discovers documentation changes, designs interfaces first, forks python-pytest-architect for failing tests, then hands off to snakepolish for implementation. Use when eliminating Any types, addressing technical debt, applying modern Python 3.11+ patterns, modernizing library usage (httpx, orjson), or refactoring for stronger type safety.

Global contract for all specialist subagents — enforces role boundaries, scope discipline, and DONE/BLOCKED status signaling. Use when loading any agent that should operate as a bounded specialist following supervisor delegation patterns.

Analysis criteria, transformation patterns, output format, and validation checklist for refactoring Claude Code agent prompt files. Load this skill when preparing to run the subagent-refactorer agent or when reviewing agent prompt files for structural, model optimization, or instruction quality improvements.

Route summarization requests to the correct methodology and enforce fidelity rules. Activates on summarize, tl;dr, give me the highlights, what's important in this, break down this, what does this code do, explain this file, describe this image, read and summarize. Routes files, URLs, images, and multi-source content to type-specific strategies. Enforces anti-hallucination rules — read before summarizing, extract before abstracting, preserve counts, distinguish absence from nonexistence, prevent lossy re-summarization chains.

Parse a markdown file's unchecked checkbox items (- [ ]) and generate a self-organizing Claude Code swarm task pool. Use when you have a todo.md, checklist.md, or any markdown file with checkbox items and want to dispatch them as parallel swarm tasks using TeamCreate + TaskCreate + worker agents. Skips checked items (- [x] / - [X]) automatically.

API reference for Claude Code multi-agent swarm tools -- TeamCreate, SendMessage, TeamDelete, TaskCreate/Update/List/Get, and Agent tool parameters. Use when looking up tool signatures, message schemas, shutdown sequences, error handling patterns, or debugging swarm operations. Covers direct messages, broadcasts, plan approval flows, graceful shutdown sequences, crashed teammate recovery, and common error causes.

Recipes and patterns for Claude Code multi-agent swarms. Use when building parallel specialist reviews, pipeline workflows, self-organizing swarms, research-then-implement flows, plan approval gates, coordinated multi-file refactoring, or any divide-and-conquer orchestration pattern requiring TeamCreate, TaskCreate, SendMessage, or Agent tool coordination.

Conceptual foundation for Claude Code multi-agent orchestration -- defines teams, teammates, leaders, tasks, inboxes, messages, and backends and shows how they connect. Use when starting to build a swarm workflow, understanding the swarm lifecycle and file layout, reading team config structure, or learning the task dependency system before writing TeamCreate or Agent tool calls.

Covers how to create agents in Claude Code swarms -- subagents vs teammates, built-in agent type catalog (Bash, Explore, Plan, general-purpose, claude-code-guide), plugin agent types from compound-engineering, spawn backend selection (in-process, tmux, iterm2) with auto-detection logic, and environment variables injected into spawned agents. Use when choosing how to spawn an agent, selecting the right agent type, configuring or troubleshooting spawn backends, or passing env vars to teammates.

Decomposes a contextualized plan into atomic, independently executable task files with complete embedded context. Use after SAM Stage 3 Context Integration produces the contextualized plan artifact — when the plan is ready for TASK file generation with CLEAR ordering, CoVe checks, and dependency graphs for parallel execution.

Establishes a dual-hypothesis investigation mindset for every test failure — treating failures as diagnostic signals that may indicate a real bug OR an incorrect test, never defaulting to automatic code changes or test dismissal. Use when encountering failing tests, debugging test errors, running a test suite that shows regressions, or any request involving "test failure analysis", "why is this test failing", or "should I fix the test or the code". Loads a 5-step protocol covering failure reading, implementation tracing, requirement context, reasoned decision-making, and learning extraction. Works alongside analyze-test-failures for detailed per-failure analysis and comprehensive-test-review for full suite review.

Use when encountering failing tests, diagnosing test errors, or establishing a systematic approach to test failure investigation. Activates on "test failure analysis", "debugging tests", or "why tests fail" requests. Establishes the mindset that treats test failures as valuable diagnostic signals requiring root-cause investigation — not automatic code fixes or test dismissal.

Use when encountering failing tests, diagnosing test errors, or establishing a systematic approach to test failure investigation. Activates on "test failure analysis", "debugging tests", or "why tests fail" requests. Establishes the mindset that treats test failures as valuable diagnostic signals requiring root-cause investigation — not automatic code fixes or test dismissal.

Use when building terminal UI apps with the Textual framework — creating widgets, screens, layouts, handling events, managing reactive attributes, testing with Pilot, snapshot testing with pytest-textual-snapshot, or running background workers. Covers App lifecycle, CSS styling, screen stack, custom messages, actions, bindings, and the Worker API.

Use when building Textual TUI apps — creating widgets, laying out screens, handling events, managing reactivity, testing with Pilot, or running background workers. Covers App lifecycle, CSS styling, screen stack, reactive attributes, custom messages, actions, bindings, and the Worker API.

Use when auditing docs vs code drift, syncing docs after changes, optimizing CLAUDE.md or SKILL.md for AI consumption, validating GLFM and Markdown, or summarizing files/URLs/images — routes each task to the correct specialist agent via /rwr:audit, /rwr:optimize, or /rwr:author

Use when reading or writing pyproject.toml or .toml config files in Python, editing TOML while preserving comments and formatting, designing configuration file formats for Python tools, working with tomlkit or tomllib, or implementing atomic config file updates.

Handles TOML configuration file operations in Python using tomlkit for comment-preserving read-modify-write cycles. Use when reading or writing pyproject.toml or any .toml config file, selecting between tomlkit and tomllib, modifying TOML while preserving comments and whitespace, implementing atomic config file updates, integrating TOML with Python dataclasses, handling TOML parse errors, or applying XDG base directory patterns for config file locations.

This skill should be used when analyzing Claude Code session transcripts, reviewing agent performance, finding anti-patterns or tool misuse, detecting user frustration signals, mining workflow patterns, running kaizen analysis, debugging agent behavior, or performing session forensics. Provides JSONL schema (kaizen-analysis get_transcript_jsonl_schema or MCP resource kaizen://session-log/schema or references/jsonl-schema.md), arbitrary DuckDB SQL over JSONL via kaizen-duckdb execute_query, cookbook query patterns, 10 analysis dimensions, and PM4Py process mining methodology.

Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.

Use when working with ty — running Python type checks, configuring ty.toml or pyproject.toml, suppressing diagnostics, interpreting error codes, targeting Python versions, or integrating ty with editors and CI. Covers CLI flags, configuration schema, rule severity, suppression comments, environment discovery, module resolution, and all installation methods.

Use when working with ty — the Astral Python type checker. Activates for running type checks, interpreting diagnostic error codes, suppressing ty errors with inline comments, configuring ty.toml or pyproject.toml, resolving unresolved imports, targeting Python versions, and integrating ty into editors or CI. Covers CLI flags, configuration schema, rule severity, environment discovery, module resolution, and all installation methods including uvx and uv add --dev.

Use when building CLI applications with Typer — creating commands, defining arguments and options, composing subcommands, testing with CliRunner, or using advanced features like callbacks and autocompletion. Covers app creation, parameter types, subcommand composition, testing patterns, and output utilities.

Use when building CLI applications with Typer — creating commands, defining arguments and options with enum restrictions, path validation, date and UUID types, composing subcommands, testing with CliRunner, or using advanced features like colored output, progress bars, shell autocompletion, and version callbacks.

Use when building or debugging Typer/Rich CLI applications. Activates on Rich table rendering, console output in non-TTY environments, CliRunner testing with Rich output, snapshot testing, Typer command wiring, exception chain prevention with AppExit/AppExitRich patterns, table width at 80-column wrapping, Progress/Live in non-interactive contexts, stderr/stdout separation, or force_terminal vs width configuration. Grounds AI-generated CLI code in verified correctness patterns and prevents known Typer/Rich integration mistakes.

Use when building Typer/Rich CLI applications or reviewing existing CLI code for correctness. Activates on requests involving Rich table rendering, console output handling, testing Rich-formatted output, or Typer command wiring. Prevents common AI mistakes — Rich table wrapping in non-TTY contexts, incorrect stderr/stdout separation, and integration pitfalls. Load alongside the typer and rich API reference skills.

Creative reasoning framework for ill-defined problems where conventional solutions are suboptimal. Use when the problem has ambiguous goals, vast solution space, no single correct answer, or requires innovation. Implements three paradigms — combinational (novel combinations of familiar ideas), exploratory (expand solution space boundaries), transformative (alter fundamental constraints). Do not use for well-defined problems, mathematical puzzles, or tasks requiring convergent reasoning.

Summarize web content by fetching URLs, extracting key passages with quote-grounding, and producing structured output. Activates on summarize this URL, what does this page say, summarize this article, read and summarize, summarize the documentation at, tl;dr this link, give me the highlights of this page, what's important on this site. Routes to fetching strategy based on content type — documentation, articles, API references, READMEs. Reports partial accessibility explicitly.

Converts user-facing documentation (how-to guides, tutorials, API references, examples) in any format — Markdown, PDF, DOCX, PPTX, XLSX, AsciiDoc, RST, HTML, Jupyter notebooks, man pages, TOML/YAML/JSON configs, and plain text — into Claude Code skill directories with SKILL.md plus thematically grouped references/*.md files. Use when given a docs directory or mixed-format documentation to transform into an AI skill. Uses MCP file-reader server for binary formats.

Use when working with Astral's uv — the fast Python package and project manager replacing pip, pipx, pyenv, poetry, and virtualenv. Activates for project initialization, dependency management, PEP 723 inline scripts, virtual environments, Python version management, workspace and monorepo configuration, tool installation via uvx, building and publishing packages, Docker and CI/CD integration, package index configuration, SBOM export, and migrating from pip, poetry, pipx, pyenv, or conda.

Use when working with Astral's uv — managing Python project dependencies, creating PEP 723 scripts, installing tools, managing Python versions, configuring package indexes, or migrating from pip/poetry. Covers project initialization, virtual environments, workspace configuration, and CI/CD integration.

Scientific validation protocol for verifying fixes work through observation, not assumption. Use when claiming a bug fix, code change, refactoring, or implementation is complete — enforces reproduce-broken-state then define-success-criteria then apply-fix then verify-outcome. Success means observing intended behavior, not absence of errors.

Enforce mandatory pre-action verification checkpoints to prevent pattern-matching from overriding explicit reasoning. Use this skill when about to execute implementation actions (Bash, Write, Edit) to verify hypothesis-action alignment. Blocks execution when hypothesis unverified or action targets different system than hypothesis identified. Critical for preventing cognitive dissonance where correct diagnosis leads to wrong implementation.

Rigorous self-assessment checklist before marking any task as complete. Use when about to claim task completion, before final commit, when user asks "is it done?", or when transitioning from implementation to reporting. Prevents premature completion claims by requiring evidence for every assertion.

Optimize processes in a file or directory by converting prose/bullet workflows to Mermaid diagrams — delegates to the process-siren:process-siren agent. Use when given a single SKILL.md, agent file, CLAUDE.md, or rules file to convert, or a directory containing any of those. Supports --dry-run or --report for read-only planning mode.

Use when working, planning, grooming, or closing a backlog item. Bridges backlog items to SAM planning with GitHub Issue, Project, and Milestone tracking. Activates on interactive browsing with no args, loading an item from a GitHub issue reference like #N, matching by title substring to run auto-grooming plus RT-ICA gate plus GitHub sync plus SAM planning, autonomous --auto {title} mode that skips AskUserQuestion and derives data from research files while logging decisions, close {title} to dismiss an item without completion with a required reason (duplicate, out_of_scope, superseded, wontfix, blocked) per ADR-9, resolve {title} to mark DONE with an evidence trail and required summary per ADR-9, setup-github to initialize labels, project, and milestone, and --language or --stack flags that select the Layer 1 or Layer 2 profile. Stops when the item already has a Plan field or when RT-ICA returns BLOCKED.

Executes a groomed milestone with parallel kage-bunshin sessions in isolated worktrees. Use when a milestone has been groomed and /groom-milestone has produced a dispatch plan. Reads the dispatch plan, creates an integration branch, spawns one kage-bunshin (independent claude -p process) per wave item in its own worktree — each session is a full orchestrator with Agent tool and TeamCreate. Sequentially merges worktree branches, relays wave discoveries to subsequent waves, then lands the integration branch to main. Takes a milestone number as argument.

Use this skill when reviewing, designing, or improving a workshop, lesson, training session, facilitation plan, talk, or educational explanation. Use it when the user provides a topic, concept, or explanation and wants better framing questions, curiosity hooks, opening prompts, discussion questions, learner reflection prompts, or ways to make participants think before being taught.

Write or rewrite frontmatter description fields for Claude Code skills and agents. Use when creating new skills/agents, description exceeds 1024 characters, description uses forbidden YAML multiline indicators (>-, |-), description lacks trigger keywords, or when optimizing descriptions for AI tool selection. Ensures descriptions are single-line, complete, informative, third-person, front-loaded with trigger conditions.

When an application needs to store config, data, cache, or state files. When designing where user-specific files should live. When code writes to ~/.appname or hardcoded home paths. When implementing cross-platform file storage with platformdirs.