Productivity Claude Skills

Proposing new features leveraging existing data/logic as Markdown specifications. Use when brainstorming new features, product planning, or feature proposals are needed. Does not write code.

Channeling the documented thinking of named notable figures (an 'itako' medium) — applies their mental models, heuristics, and decision-style to your problem as advisory lenses. Don't use for decisions/verdicts (Magi), synthetic user personas (Cast), or fixed founder-mentor coaching (Sage).

Delivering working code fastest via build-first product delivery. Routes through minimum agent chains scoped to the task. S/M scopes skip planning and build immediately. Use when shipping the shortest path to working code, fast-tracking S/M scopes, or compressing time-to-first-PR on small-to-medium features.

Designing scheduling and time-aware logic for cron, timezone/DST, retry/backoff, and business-calendar systems. Use when schedule design is needed.

Directing UI/UX creative work — complete redesigns, new designs, and trend application. Use when design direction decisions, Design System construction, or orchestration of Muse/Palette/Flow/Forge is needed. Does not write code. Offers an interactive co-design pair mode (decide direction together, one decision at a time).

Document decisions, not just implementations. ADRs for architectural choices, inline docs for non-obvious code, and runbooks for operational knowledge.

Detects and mitigates LLM hallucinations in production pipelines. Validates AI-generated facts, code, and decisions before they reach end users or downstream systems.

Структурированный мозговой штурм перед любой творческой работой. Используй ПЕРЕД созданием контента, скилов, автоматизаций, проектов. Триггеры: 'давай подумаем', 'мозговой штурм', 'brainstorm', 'придумай', 'нужна идея', 'как лучше сделать', 'варианты'.

Google Workspace CLI for Gmail, Calendar, Drive, Contacts, Sheets, and Docs. Use when user asks about email, calendar events, Google Drive files, contacts, spreadsheets. Triggers on 'почта', 'gmail', 'календарь', 'calendar', 'google drive', 'контакты', 'таблица', 'sheets'.

Generate a complete professional event plan from guest background material. Takes guest bio / CV / intro copy as input, runs a multi-turn clarification dialog (activity type, audience, duration, tone), and outputs a cohesive plan with title + promo copy, minute-level rundown, tiered host question set, and gift / takeaway suggestions. Trigger when user mentions "活动策划", "策划案", "嘉宾对谈", "沙龙策划", "主持人问题", "活动流程", "event planning", "host prep", "salon plan", or pastes a guest bio asking for an event plan.

Vector search indexing and querying workflows using MCP Vector Search, including setup, reindexing, auto-index strategies, and MCP integration.

Emergency release workflow for critical bug fixes and security patches. Use when production issues require fast-track deployment.

PR quality checklist for ensuring comprehensive, well-documented pull requests. Use before submitting PRs to improve review efficiency and code quality.

Compare tradeoffs and recommend architectural patterns — dependency injection, service-oriented architecture, repository, domain events, circuit breaker, and anti-corruption layer. Use when choosing between design patterns, planning microservices boundaries, evaluating system design alternatives, or asking 'which pattern should I use' for a specific coupling or resilience problem.

Interactive idea refinement using Socratic method to develop fully-formed designs

> Use this skill whenever the user wants to FIND new prospects for their managed IT services a prospect list, researching a specific business as a potential client, figuring out who the decision maker or "IT person" is at a company, or planning how to approach a prospect (email vs call vs professional network vs mail, and when). Trigger on "find leads", "find prospects", "who should we go after in [area]", "research this business", "build a lead list", "is [company] a good fit", "who do I contact at [company]", "sweep [town] for candidates", or any mention of generating leads, prospecting an area, or door-knocking researches prospects and plans the approach; msp-sales owns the ICP definition and all outreach copy; msp-marketing owns broad marketing channels. Apply alongside msp-brand and msp-sales.

> questioning a ticket's priority, response and resolution targets, "the client says everything is down", escalation, after-hours or emergency requests, security incident intake, triage order, your PSA (ticketing) system workflow, time entries, or ticket categories. Also trigger when drafting anything that describes support levels or response times to a client, and when training new hires on the service desk workflow (client onboarding is msp-onboarding). Apply alongside msp-client-comms (the messages clients receive during incidents), msp-legal (the SLA schedule lives in each client's Order; security incidents have contract implications), msp-pricing (after-hours billing multipliers), msp-maintenance (alerts that escalate into tickets, and the on-call rotation behind the after-hours line), and msp-metrics (ticket data feeds every business number).

> gives notice, says they are not renewing, is switching providers, or asks to cancel; you decide to exit a client (see msp-metrics for that decision); or anyone mentions data return, removing our access, transitioning to a successor provider, a final invoice, or "offboarding" a client. Also trigger when drafting termination acknowledgments or closure letters. Apply alongside msp-legal (the signed MSA and Order govern notice, fees, and data handling; this is a legal-adjacent workflow), msp-brand (every message), msp-client-comms (the letters), and msp-metrics (log the loss and the reason).

> Guided first-run setup for the MSP Operations Kit. Use this skill whenever the user wants to set customize these skills", "make these skills mine", "replace the placeholders", "finish setup", "resume setup", "am I ready to use this with clients", or any first-run intent after purchase. Also trigger when another msp skill surfaces an unfilled placeholder token or an unreviewed example default, since that means setup is incomplete. Works through msp-brand first, then msp-pricing, then every skill's Setup Decisions, and finishes with a readiness check.

Linear CLI for issue tracking and project management. Use when developers mention: (1) Linear issues or tickets, (2) issue tracking or task management, (3) WDY team issues, (4) closing, updating, or triaging tickets, (5) linking PRs to issues, (6) issue states (triage, backlog, started, completed).

| 雅思听力错题分析 + 精听训练 + 题型追踪。逐题拆解错因，Section 得分分析，推荐精听任务。 触发方式：/ielts-listening、「听力」「错题」「精听」「听力怎么练」

Deliver a large implementation plan, architecture migration, refactor, or feature roadmap as right-sized, coherent subtasks that are meaningful to code, review, test, and recover independently. Use only when the user explicitly requests this skill or workflow, or explicitly accepts it after an optional proposal during a user-initiated brainstorming or planning session before coding. Do not use for ordinary coding requests with clear requirements and no prior user-requested brainstorming or discussion. This skill does not authorize subagents; before any spawn, explain the usage impact and obtain explicit user approval for the proposed agent count and scope.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

> Plan and control time-step policies for transient simulations — couple CFL and physics-based stability limits with adaptive stepping, ramp initial transients through sharp gradients or phase changes, schedule output intervals and checkpoint cadence, and plan restart strategies for long-running jobs. Use when choosing dt for a new simulation, diagnosing adaptive time-step oscillations, deciding checkpoint frequency to minimize lost work, or setting up output schedules aligned with physical time scales, even if the user only says "my run is too slow" or "how often should I save."

> Identify computational bottlenecks, analyze parallel scaling, estimate memory requirements, and generate optimization recommendations for materials simulations — parse timing logs to find dominant phases (solver, assembly, I/O), evaluate strong and weak scaling efficiency, profile memory from mesh and field parameters, and detect bottlenecks with actionable fix suggestions. Use when a simulation is running slower than expected, investigating MPI scaling efficiency, planning HPC resource allocation, deciding whether to tune the preconditioner or reduce I/O frequency, or estimating if a problem fits in available RAM, even if the user only says "my simulation is too slow" or "how many nodes do I need."

> Explore and optimize simulation parameters via design of experiments (DOE), sensitivity analysis, and optimizer selection — generate Latin Hypercube, quasi-random, or factorial sample plans, rank parameter influence with sensitivity scores, recommend Bayesian optimization, CMA-ES, or gradient- based methods based on dimension and budget, and fit surrogate models for expensive evaluations. Use when calibrating material properties against experimental data, planning a parameter sweep, performing uncertainty quantification, or choosing an optimization strategy for a simulation with a limited evaluation budget, even if the user only says "which parameters matter most" or "how do I calibrate my model."