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Look up NHS England genomic tests for rare and inherited diseases. Use this skill whenever a clinician asks what genetic test to order for a condition, which genes are covered for a specific diagnosis, what commissioning category a genomic test falls under (Core, Specialised, or Highly Specialised), or which tests belong to a specialty group (Neurology, Cardiology, Endocrinology, etc.). Also trigger for questions like what panel is used for a condition, whether there is an NHS test for something, what the R number is for a condition, or whether anything has changed in the genomic test directory. Source: NHS England National Genomic Test Directory for Rare and Inherited Disease v9.0 (April 2026).

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Standard operating procedure for producing a documentation page end to end — gather source material, classify, draft against the measured corpus profile, then run the reader-level and humanizer passes and both gates. Use for any page on a documentation surface, or when rewriting a page that reads as machine-written. Orchestrates technical-writing, reader-level and humanizer in a fixed order.

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Generate or repair a project's .gitignore. Composes the baseline must-ignore set (the single source of truth in baseline-ignores.json) with optional gitignore.io enrichment, and merges add-only so existing entries are never lost. Use when a repo has no .gitignore, when the commit-leak guard reports a gap, or when onboarding a new stack. Trigger: "set up gitignore", "fix my gitignore", "ignore node_modules/secrets", "gitignore for <stack>".

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Workflow Phase 3 — Research and Solution Exploration. Surfaces 2–4 candidate solution approaches with concrete tradeoffs, grounded in current library docs (fetched through the provider named in `.claude/docs-provider.json`) — never in training-data recall. Output lives at `docs/research/<slug>.md`. Candidate ranking and the memo execute in main context; doc/source gathering MAY be delegated to read-only advisory subagents (seed.md §4.2-A).

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The primary tool for code-navigation questions in ANY language (frontend or backend) — walk the structural graph from an entry point (page, route, handler, command, `main`) down through imports to the IO boundary (network call, query, file read). Use this skill, in preference to the Explore agent or global grep, whenever the user asks "where does X come from?", "what API/data populates Y?", "what renders or wraps the Z panel?", "which file for feature F?", "how is this wired up?", or any code-navigation question mapping a UI element, route, or feature concept to source files. Also reverse queries ("what uses /api/foo?", "who consumes this service?"). Keyword search routinely picks up unrelated flows sharing a domain word and produces wrong answers; the walk follows the actual graph. The JS/TS `walk.mjs`/`discover.mjs` are an optional accelerator, not required — the walk is language-agnostic.

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Split a dirty working tree into single-concern Conventional Commits. The deterministic inventory.mjs helper groups dirty paths by concern (docs, source + paired test, config, governance); main context refines the grouping and presents a commit plan. Read-only until the user approves the plan — it never stages, never commits, never touches consent paths. Disjoint from the commit skill (Phase 11 executor): the planner proposes a split; the executor lands one consented commit. Use when a working tree has accumulated heterogeneous changes that should not land as one commit.

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Dev-only review of user-facing CLI interface copy in `src/cli/tui/*.js`, `src/cli/*.js` error paths, and `bin/cli.js` help/usage text. Surfaces three failure modes — copy/behavior mismatch, jargon, missing next-action — as a punch list the maintainer addresses before `/grant-commit`. Read-only; no writes to source. Conditionally seeded by `/triage` between `/memory-sync` and `/grant-commit` when the request will touch CLI surfaces.

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Convert Android XML layouts to Jetpack Compose. Use when asked to migrate XML layouts, convert views to composables, or help with Compose migration. Handles layouts, widgets, attributes, styles, and resource references.

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Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.

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Deep-dive framework for analyzing crypto tokens — market data, liquidity health, tokenomics, unlock schedules, and risk assessment. Combines on-chain data, API queries, and multi-source validation to generate actionable investment verdicts.

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Discover and analyze trending GitHub repositories from TrendShift and other sources — evaluate usefulness, extract learnings, and identify valuable skills/tools

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Comprehensive workflow for contributing features to open-source IDE and coding tool projects

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Create production-ready public GitHub repositories with comprehensive documentation, automated setup, and quality assurance

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Generate guided Chinese software copyright application materials from a real project. Use this skill when the user asks for 软件著作权, 软著申请资料, 软著代码材料, 操作手册, 申请表信息, or wants Word/TXT materials for software copyright registration. The workflow analyzes the imported project, extracts real source code, creates Markdown drafts for user confirmation, then uses bundled DOCX tooling to produce final Word documents and TXT.

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Automated cleanup of unused AWS resources to reduce costs

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The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing.

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Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

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Find Amazon Ads search terms ready for exact-match harvesting from auto, broad, phrase, or other discovery campaigns. Use when Codex needs standalone or Rocketcart MCP-aware search-term harvesting: classify terms by harvest readiness, resolve live IDs, choose safe destination campaigns/ad groups, preflight duplicates/current negatives/destination feasibility, decide whether source negatives are justified, execute only explicitly approved rows, and read back/monitor Sponsored Products or Sponsored Brands actions without unsafe negatives, duplicate routing, stale approvals, brand-defense cuts, own-ASIN-defense cuts, launch/rank mistakes, or low-sample overfitting.

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Find the first batch of foreign-trade prospect companies from public web, LinkedIn, and user-authorized data sources such as customs exports, trade show lists, association directories, CRM/Excel files, or historical customer data, then output a conservative, structured candidate list ready for lead screening.

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OpenClaw-native version of the foreign-trade outreach email skill. Use structured operator input plus customer-intel source context to generate conservative first-touch or follow-up email drafts without overstating inferred facts or inventing recent customer events.

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Discover prospect companies from a structured OpenClaw search brief, including public search and user-authorized data sources, then output a conservative candidate list ready for lead screening.

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Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.

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Rebuild the pgvector gold index after source data changes. Invoke manually — replaces each collection, do NOT trigger automatically.

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Create a new dbt Silver model with SQL + YAML contract. Use when adding a new Silver layer model from a raw source table.

日本語の概要は準備中です。原文の説明を表示しています。