Skill Registry

Workflow for analyzing bug reports, tracing root causes, and generating structured bug-fix implementation plans with rollback strategies.

Language-agnostic workflow for code reviews and security audits against Clean Code/SOLID principles, generating formal refactoring plans.

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

Generates formal, structured, and executable implementation plan documents based on specifications.

Explicit activation required: do not invoke or load this skill from an implicit match; wait for explicit user or operator invocation or a source-authorized parent-route selection. Delegate one bounded child route through quest-passport law, local coding-agent execution defaults, hard gates for progression, self-agent, and stress posture, and governed return into reviewed closeout. Use when a parent route already has a real anchor and named outputs and the honest next move is a narrower child reviewer, evaluator, verifier, or leaf helper rather than a larger orchestration layer. Do not use when the route lacks an anchor, the outputs are unnamed, the work is `d3+` and still needs a split, or remote delegation is being used to bypass proof, approval, or closeout posture.

Fetch a web page (URL) and return clean Markdown via local trafilatura, with Exa MCP as a fallback for JS-rendered or anti-bot pages. Use when the user asks to read, fetch, scrape, summarize, or quote a URL — prefer this over the built-in WebFetch tool. Don't use for binary files (PDFs, images, archives) or for fetching API/JSON endpoints.

Hands-on playbook for macOS disk cleanup, dev-machine optimization, and proactive health alerting. Use when the Mac is full or slow, when a kernel panic / watchdog timeout / vm-compressor-space-shortage / Jetsam event happened, when the user asks to free disk space, audit storage, set up disk/memory alerts, or restore the same monitoring on a new Mac. Built around Mole (`mo` CLI) for safety guards plus a custom LaunchAgent-based alerter for active warnings. Covers Apple Silicon laptops with heavy AI/Docker workloads. Not for general macOS support, hardware diagnostics, networking issues, GUI / window-manager bugs, Time Machine recovery, broken app installs, or app-specific performance problems unrelated to disk or memory pressure.

Deep research over the Semantic Scholar Graph API. Covers endpoints missing from allenai's lookup skill — paper references (backward citations), recommendations, batch paper lookup (up to 500 IDs), snippet search, and multi-hop citation graph traversal (BFS forward/backward). Use when the user asks to build a citation graph, expand a literature seed, find related work, run a reference network traversal, explore what a paper cites or what cites it beyond simple lookup, or batch-resolve many DOI/arXiv/S2 IDs. For multi-step research questions, delegate to the deep-paper-researcher subagent to keep the main context clean. Not for single paper-by-ID lookups (use semantic-scholar-lookup) or topical discovery (use web_search_advanced_exa).

Orchestrate the a11y accessibility lifecycle: scout → plan → critique → [perspective audit] → test → critique. Dual-mode: full lifecycle automation or step-by-step dispatch. Spawns specialist agents at depth-1 from the main session.

Use when the user wants to record an architectural decision — drafting an Architecture Decision Record (ADR) or RFC, documenting a design choice and its trade-offs, or capturing why an approach was taken. Detects the repo's existing ADR location and template style (MADR or Nygard) and matches it; if none exists, sets one up. Writes the record; it does not change code.

Use when the user wants a commit message written for currently staged changes. Reads `git diff --cached`, recent log style, and CLAUDE.md, then outputs a conventional-commit-style message — type(scope) summary + why-focused body.

Use when the user wants prose, comments, docs, or a file's text to read like a human engineer wrote it instead of an AI. Strips AI tells (em-dashes, filler openers, chatbot scaffolding) by rewriting, then runs klaussy's deterministic scrubber as a guaranteed backstop. Never touches code.

Use when the user wants to run, start, or launch this project's app — to watch a change work end-to-end, reproduce behavior in the real app, or smoke-test locally. Finds the run command from CLAUDE.md and drives the app; it does not write features or fix bugs.

Use when the user explicitly wants to turn clean, human prose INTO maximal AI slop — as a joke, a demo of what AI tells look like, or to stress-test the humanize skill by feeding it the worst input imaginable. The evil twin of humanize — it adds every tell humanize strips. For laughs and demos only; never run it on a real deliverable.

Use when the user wants to record an architectural decision — drafting an Architecture Decision Record (ADR) or RFC, documenting a design choice and its trade-offs, or capturing why an approach was taken. Detects the repo's existing ADR location and template style (MADR or Nygard) and matches it; if none exists, sets one up. Writes the record; it does not change code.

Use when the user wants a commit message written for currently staged changes. Reads `git diff --cached`, recent log style, and CLAUDE.md, then outputs a conventional-commit-style message — type(scope) summary + why-focused body.

Use when the user wants prose, comments, docs, or a file's text to read like a human engineer wrote it instead of an AI. Strips AI tells (em-dashes, filler openers, chatbot scaffolding) by rewriting, then runs klaussy's deterministic scrubber as a guaranteed backstop. Never touches code.

Use when the user wants to run, start, or launch this project's app — to watch a change work end-to-end, reproduce behavior in the real app, or smoke-test locally. Finds the run command from CLAUDE.md and drives the app; it does not write features or fix bugs.

Use when the user explicitly wants to turn clean, human prose INTO maximal AI slop — as a joke, a demo of what AI tells look like, or to stress-test the humanize skill by feeding it the worst input imaginable. The evil twin of humanize — it adds every tell humanize strips. For laughs and demos only; never run it on a real deliverable.

Vast.ai Python SDK — high-level API for GPU instances, volumes, serverless endpoints, and billing.

Challenge, push back, play devil's advocate on AI output. Use when: challenge this, are you sure, push back, prove it, what if you're wrong, devil's advocate, stress test, poke holes, second opinion, sanity check, too confident, really?, question this decision. Subcommands: anchor (committed too fast), verify (facts wrong?), framing (wrong problem?), deep (full devil's advocate in separate context).

Sense-making before action. Classify problem using Cynefin triangulation (3 tests + decomposition) to route to the right skill chain. Use when: frame, what approach, how should I start, which skill, where to begin, unsure what to do. NOT for known tasks — just do them.

Query and display available GitHub Copilot AI models with their capabilities, context limits, and features. Use when: "what models are available", "show copilot models", "list github models", "check model capabilities", "switch models". Examples: - user: "What models can I use with GitHub Copilot?" → fetch and display available models - user: "Show me models with vision support" → filter models by capability - user: "Which model has the largest context window?" → compare model specifications - user: "List all GPT-5 models" → filter by model family

Converts a PRD or requirements document into a structured, phased implementation plan with individual phase files and granular per-task files written to .context/plans/. Also restructures existing monolithic planning documents into digestible, hierarchical directory structures. Creates a root plan index summarising all phases, a numbered phase file per phase, and a numbered task file per task inside each phase directory. Use when the user asks to create an implementation plan, break down a PRD, convert requirements to tasks, structure project phases, generate a roadmap, plan a project in sprints, organise task breakdown, split a monolithic planning doc, or decompose a spec into phases and tasks.