Agent Workflow Skills

Anthropic Claude Agent SDK for autonomous agents and multi-step workflows. Use for subagents, tool orchestration, MCP servers, or encountering CLI not found, context length exceeded errors.

Integrate MDMA into an application and build features with it — wire up parsing, the runtime store, React rendering, LLM streaming, custom components, prompts, and CI validation. Use this skill when the user asks to add MDMA to an app, build a chat that streams MDMA, author or maintain a custom prompt, validate MDMA documents, register a custom component, or expose MDMA to an agent via MCP. Generates focused, correct wiring that uses the right packages for the job instead of reinventing them.

Diagnose Basecamp CLI, authentication, and agent-plugin health.

| Rates any SKILL.md on a 0-100 scale across 10 dimensions with a SHIP / REWORK / SCRAP verdict. Evaluates trigger precision, instruction clarity, output predictability, edge case coverage, anti-hallucination guardrails, developer experience, composability, open-source readiness, wow factor, and real-world utility. No flattery — calibrated against Anthropic's own skill-creator rubric. Use this skill whenever someone says "score this skill", "rate my skill", "is this skill good", "skill review", "skill audit", "roast my SKILL.md", "grade this", "will this skill work", "evaluate my skill", "how good is this", or pastes a SKILL.md and asks for feedback. Also trigger when comparing two skills, benchmarking a skill collection, or asking "what's wrong with this skill" — even without the word "score".

Guide users through creating, reviewing, and fixing custom skills for Claude — both command skills (invoked via /slash) and context skills (auto-loaded by tech stack). Use when the user asks to create a skill, build a skill, make a new slash command skill, add a coding standards skill, review an existing skill, update a skill, or fix a skill that doesn't trigger.

Chinese legal workflow skills for lawyers, legal counsel, litigation, criminal defense, labor disputes, bankruptcy, contract review, compliance, legal research, and legal document drafting.

把包豪斯（Bauhaus）视觉语言拆成媒介模块，识别用户意图后编译成一条确定性、可直接用于图像模型的提示词。用户提到包豪斯风格封面、文章配图、栏目/活动海报、Bauhaus prompt、包豪斯视觉主图，或上传室内照片要求改成包豪斯风格工作室/空间时使用。只输出一条成品提示词，不展示模块菜单。不用于普通设计史问答或其他设计流派。

把文章主题、AI/技术概念、Agent 工作流编译成「赛博终端示意图」风格生图提示词：纯黑底 + 单一霓虹绿荧光，节点-连线拓扑图作主视觉，HUD 边框（角落数据面板、图例、标尺、系统状态行），等宽字巨型标题，外加一层可调强度的 CRT 屏幕质感（横向扫描线、荧光颗粒、辉光溢出、屏幕弧度、刷新条、烧屏残影）。用户提到终端风、赛博终端、HUD、拓扑图、节点图、schematic、示意图海报、扫描线、CRT、显像管、老屏幕质感、荧光屏、绿色终端，或要按这个风格批量产文章封面和插图时使用。封面锁 5:2 横板，插图锁 16:9。先判封面/插图 + 质感强度档，再编译单条确定性提示词。要蓝底白线的工程图纸、三视图、爆炸图、标题栏那一套，用 engineering-blueprint-sheet，不用本 skill。

为城市旅行明信片、城市拼贴海报生成可直接用于图像模型的完整提示词。用户提到城市明信片、旅行拼贴、城市视觉海报、生图提示词，或在视觉创作上下文中输入城市、城市+国家、节日、季节、活动时，应使用本 skill；即使用户只给出城市名，也要在上下文明确为视觉创作时使用。不用于旅游攻略、行程规划或普通城市事实问答。

Write or update a HANDOFF.md so a fresh agent can continue this work. Use when the user says "handoff", "compact this", "context is full", or "/clear and continue".

OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent handoffs, function tools, guardrails, sessions, streaming, or tracing with the `openai-agents` / `agents` Python package — including Azure OpenAI via LiteLLM. Triggers on imports from `agents`, uses of `Runner.run_sync`/`Runner.run_streamed`, `@function_tool`, `AgentOutputSchema`, `SQLiteSession`, or questions about the openai-agents-python SDK.

Creates new skills, modifies and improves existing skills, and measures skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

> 📈 SEO & Content Marketing skill suite derived from VoltAgent/awesome-agent-skills. Keyword research, content audits, SERP analysis, technical SEO and content strategy. Provides 10 specialised commands for seo, content, marketing workflows.

> How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers). Covers the scout SKILL.md anatomy, the emit contract, the dedupe + scratchpad-memory conventions, the per-team skills-store path vs the canonical in-repo path, and the emit-and-inspect test loop (with dry-run as an optional safety net). Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>".

Guides agents through creating and safely sizing a Replay Vision scanner: choosing the scanner type (monitor/classifier/scorer/summarizer), shaping the RecordingsQuery that selects sessions, and — crucially — estimating observation volume and checking the org's monthly quota before creating, so a broad scanner doesn't exhaust the budget on its first scheduled sweep.\nTRIGGER when: user asks to create, set up, or configure a Replay Vision scanner, OR when you are about to call vision-scanners-create, OR when widening an existing scanner's query or sampling_rate via vision-scanners-update.\nDO NOT TRIGGER when: only reading scanners or observations, deleting a scanner, or running an existing scanner against a single session on demand (vision-scanners-scan-session).

> Focused Signals scout for PostHog setup health. Reads the project's active health issues — the deterministic findings of PostHog's own health checks (no live events, outdated SDKs, missing reverse proxy, absent web vitals, ingestion warnings, failing data-warehouse models, and more) — and decides which are genuinely worth surfacing. Unlike a one-signal-per-issue push, it bundles kind-clusters into a single finding, weights by real blast radius (cross-referencing actual event volume and reach), and prioritizes issues an agent can resolve via the MCP. Emits only above the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

> Focused Signals scout for PostHog projects using session replay. Watches two promises recording volume vanishing while site traffic doesn't), and that the friction evidence inside recordings gets seen (rage-click / dead-click clusters concentrating on a page or element, error-after-interaction cohorts, recurring replay vision themes nobody aggregates). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet.

>- Discover and use shared team skills stored in PostHog. Use when the user asks to list, browse, load, or manage "shared skills", "team skills", or references the "skills store" / "skill store".

> Create PostHog error tracking suppression rules to drop high-volume, low-value errors at ingestion. Use when the user asks "stop capturing this error", "drop browser extension errors", "ignore ResizeObserver loops", "suppress bot-driven errors", or wants to reduce ingestion cost from noisy unactionable errors. Identifies suppression candidates, scopes the filter tightly, decides between full suppression and sampling, and confirms the rule before creating it. Suppressed errors are dropped permanently — this skill defaults to caution.

>- Best practices for agents managing PostHog skills via the MCP `llma-skill-*` tools — how to discover, read, create, update, and refactor skills efficiently, especially large skills with many bundled files. Use whenever you are about to call any `llma-skill-*` tool, asked to author or edit a shared skill, or troubleshoot why a skill write was rejected. Pairs with `skills-store` (which covers the raw tool surface) by adding the decision-tree, efficiency, and pitfall guidance.

The AlterLab front door and multi-agent launcher — routes a task to the right AlterLab skill(s) when the user invokes the suite without naming one, and for a multi-stage goal (or on the keyword 'alterflow', aliases 'alterresearch' / 'ultralab') it CLARIFIES the goal with a few questions, SELECTS the skills the task needs, and runs a dynamic multi-agent workflow composing them (via alterlab-workflow-orchestration, alterlab-research-pipeline, or alterlab-ssci-orchestrator). Triggers on 'use AlterLab skills', 'which AlterLab skill for X', 'is there an AlterLab skill for…', a multi-stage research goal, 'alterflow …', or any generic AlterLab request where the user does not know skill names. It always asks clarifying questions before executing a multi-step run. Use when someone references AlterLab generically, describes a multi-stage goal, or fires the alterflow keyword; when the user already names a specific skill, defer to that skill directly. Part of the AlterLab Academic Skills suite.

Composes existing AlterLab skills into multi-agent agentic workflows using current Claude Code subagent and Claude Agent SDK orchestration patterns: parallel subagent fan-out, sequential pipelines, judge panels, adversarial verification, and loop-until-clean review cycles. Maps each pattern onto real skills (alterlab-research-pipeline, alterlab-deep-research, alterlab-citation-verifier, alterlab-paper-reviewer, alterlab-peer-review) with copyable delegation prompts, agent-definition frontmatter, and SDK query() snippets. Use when the request mentions multi-agent, subagents, agent team, parallel agents, orchestration, pipeline of skills, judge panel, adversarial verification, devil's advocate, loop until clean, chaining skills, dispatching agents, or composing skills into a workflow. Part of the AlterLab Academic Skills suite.

Enforces results-reporting transparency as a discipline gate built on the Iron Law "NO RESULTS CLAIM WITHOUT REPORTING EVERY ANALYSIS RUN" — a numbered Gate Function (IDENTIFY the claim, LIST every test actually run including the ones that did not "work", CHECK assumptions were reported, CHECK effect size with 95% CI is present, CHECK pre-registration deviations are disclosed, ONLY THEN write the sentence), plus an Excuse-vs-Reality table and Red-Flags-STOP list for selective reporting, cherry-picking, and bare p-values. Use when writing up Results, claiming a finding from a subset of analyses, reporting a p-value without an effect size or confidence interval, dropping outliers post hoc, or omitting analyses that did not pan out. Orchestrates alterlab-statistical-analysis (tests, effect sizes), alterlab-preregistration-discipline (the frozen plan), and alterlab-open-science (TOP, disclosure); it does not run the tests itself. Part of the AlterLab Academic Skills suite.

Builds agent-based models of social systems with Mesa 3 — the current AgentSet API (model.agents.shuffle_do('step'), auto-assigned unique_id, mandatory super().__init__(seed=...)), cell spaces (mesa.discrete_space OrthogonalMooreGrid / classic mesa.space grids), the DataCollector, batch_run parameter sweeps, and SolaraViz — for emergence, segregation, diffusion, opinion dynamics, and cooperation models. It uses the Mesa 3.x API (the old mesa.time schedulers like RandomActivation are removed) and treats the model as a generative theory to be validated, not just run. Use when the request mentions an agent-based model, Mesa, simulating interacting agents, or emergent macro behavior from micro rules. For discrete-event (queueing/process) simulation prefer alterlab-simpy; for reinforcement learning prefer alterlab-stable-baselines3. Part of the AlterLab Academic Skills suite.

Orchestrates matplotlib, seaborn, and plotly with opinionated publication styles to produce journal-ready figures. Use when preparing journal-submission figures that need multi-panel layouts with bold panel labels, statistical significance annotations, error bars, colorblind-safe palettes (Okabe-Ito), or specific journal formatting (Nature, Science, Cell). Does NOT cover raw low-level plotting or fine-grained control of individual plot elements; for building custom plots from scratch or tuning every artist and rcParam prefer alterlab-matplotlib instead. Part of the AlterLab Academic Skills suite.

Review Kafka schema changes (Avro, Protobuf, JSON Schema) for compatibility and evolution best practices using the Lenses MCP server. Detects breaking changes, missing defaults, schema drift and naming issues. Use when user says "review schema changes", "check schema compatibility", "will this schema break consumers" or asks about schema evolution. Do NOT use for creating new schemas from scratch or registering them in the cluster.

Analyse Kafka consumer group lag using the Lenses MCP server. Diagnoses lag causes (throughput bottlenecks, rebalancing, partition skew, stalled consumers) and suggests remediation. Use when user says "check consumer lag", "why are consumers slow", "lag report" or asks about consumer group health or offset progress. Do NOT use for resetting offsets or managing consumer groups.

Review Kafka Connect connector configurations for common misconfigurations using the Lenses MCP server. Checks error handling, DLQ setup, converters, transforms, task count and task health. Use when user says "review connectors", "check connector configs", "why is my connector failing" or asks about Kafka Connect configuration. Do NOT use for creating, deploying or controlling connectors.

Review dead letter queue implementations for completeness using the Lenses MCP server. Checks DLQ topic existence, configuration, monitoring, metadata preservation, retry logic, reprocessing paths and connector DLQ alignment. Use when user says "review dead letter queues", "check DLQ setup", "DLQ audit" or asks about error handling, message failures or reprocessing. Do NOT use for reprocessing DLQ messages or managing consumer offsets.

Audit all Kafka topic configurations against production best practices using the Lenses MCP server. Checks replication factor, retention, partitions, compaction, naming conventions, orphaned topics and missing metadata. Use when user says "audit my topics", "check topic configs", "topic health check" or asks about retention, replication or partition settings. Do NOT use for creating, deleting or modifying topics.

Generate a ShadowTraffic configuration to populate a Kafka topic with realistic synthetic data. Discovers the target topic, its key and value schemas, and the correct serializers from the live cluster via any attached Kafka MCP server, then writes a ready-to-run `shadowtraffic-config.json` and Docker command. Use when the user says "populate my Kafka topic with test data", "set up ShadowTraffic for topic X", "generate synthetic events into my topic", "I need fake data flowing into Kafka", "seed my topic with data", or "mock data for my Kafka topic". Do NOT use for creating topics, reviewing schemas, or building Kafka consumers.

> 🤖 Data Science & AI/ML skill suite derived from BehiSecc/awesome-claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.

> 🔒 Security & Compliance skill suite derived from VoltAgent/awesome-agent-skills. Security audits, vulnerability management, GDPR/SOC2/ISO27001 compliance and incident response. Provides 10 specialised commands for security, compliance, gdpr workflows.

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

Create multi-assistant squads in Vapi with handoffs between specialized voice agents. Use when building complex voice workflows that need multiple assistants with different roles, like triage-to-booking or sales-to-support handoffs.

Create, improve, or audit Vapi voice agent and Squad system prompts for production phone and web based voice agents. Use when the user wants help designing a Vapi assistant prompt, multi-assistant Squad prompt set, refining an existing prompt, creating prompt sections, building an intake or handoff workflow, improving tool-use instructions, adding guardrails, or optimizing voice-agent behavior for brevity, turn-taking, error handling, caller data collection, escalation, handoffs, and spoken formatting.

Alignment conversation starting from a user's rough idea. Co-decides with the user whether the idea should be acted on directly in the current session, or fixed into a formal Task by producing four documents in `.task/` for independent dispatch. Handles lightweight 'do it now while we talk', heavyweight 'define precisely, run later', and 'just help me think about this' — all on the same skill. Use when the user describes a task or intent they want to align on. Trigger phrases include 'help me plan this', 'let's think this through', 'I want to explore X', 'I have an idea', '/task-alignment'. Also use proactively when a user jumps into a complex task without defining scope or success criteria — pause, align, and help them pick the right vessel (this session vs. a task).

Autonomous task execution driven by documents under `.task/<MMDD_slug>/` (produced by /task-alignment). Reads task.md as the goal, decomposes work, delegates to subagents when appropriate, runs independent verification, and delivers results. Acts as a UserProxy Agent — the human's representative during autonomous execution. Use when a task subdirectory exists in `.task/` and the user wants to start execution, or right after completing /task-alignment. Trigger phrases include '/task-implement', '/task-implement <slug>', 'start the task', 'go ahead and implement', 'execute the plan', or when the user confirms alignment documents and says something like 'looks good, go'.

> 🤖 Data Science & AI/ML skill suite derived from VoltAgent/awesome-agent-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.

> research prompt. The orchestrator's agent-driven flow runs this re-ranker on Opus subagents; a standalone run uses the in-script Claude Sonnet API fallback. Rates relevance 1-10, tags each paper as theoretical/empirical, identifies methodology, and classifies relationship to user's work. Only use this skill when explicitly requested -- e.g., the user says "run lit-screen", "lit-screen", or "/lit-screen". Do NOT auto-trigger on general literature review requests.

Run the lit-review orchestrator keyless agent-driven web search channel that uses WebSearch and WebFetch outputs normalized through websearch_ingest.py. Use when the user invokes the web search channel, asks for Stage 4d open-web literature discovery, or needs a Claude Code web-search fallback without SearchAPI, Gemini, or Undermind credentials.

> Spin off parallel cloud Devin sessions. Hand off a task to a fresh cloud Devin session that runs in the background — each gets its own VM with shell, browser, and full repo access — so you can fan out several sessions at once and keep working locally while they run. Use for parallel or long-running work and CI work, browser automation, migrations, or large refactors. Also an API for reading and interacting with current sessions — given a session URL or ID, the check/poll commands return its PR, status, branch, and latest message.

| Creates production-grade, reusable skills that extend Claude's capabilities. This skill should be used when users want to create a new skill, improve an existing skill, or build domain-specific intelligence. Gathers context from codebase, conversation, and authentic sources before creating adaptable skills.

| Validates skills against production-level criteria with 9-category scoring. This skill should be used when reviewing, auditing, or improving skills to ensure quality standards. Evaluates structure, content, user interaction, documentation, domain standards, technical robustness, maintainability, zero-shot implementation, and reusability. Returns actionable validation report with scores and improvement recommendations.

> 🤖 Data Science & AI/ML skill suite derived from alirezarezvani/claude-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.