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Found 3,150 Skills
Audit and consolidate documentation for healthcare engineering systems. Supports two modes — analyze (coverage audit — writes only .health-docs/analysis.md) and document (consolidate existing docs + fill gaps). Detects applicable regulatory regimes (HIPAA, ONC, FDA SaMD) from codebase signals, composes existing skills as subagents for deep-dimension analysis, and produces a structured handoff artifact consumed by document mode.
Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.
Documentation-as-code conventions for team repos. Defines a docs/ taxonomy (ADR, RFC, guide, system, incident, audit), naming rules, templates, and boundaries. Use when creating, moving, or organizing documentation files.
This skill should be used when the user invokes "/describe" to look up Emacs documentation via emacsclient.
Maintains persistent codebase knowledge across sessions through a structured knowledge graph stored in a local Obsidian vault (.doctrack/). Use this skill whenever you have just made meaningful code changes (new features, modified components, refactoring, bug fixes) to update the project's documentation. Also use it when the user asks to document code, update docs, sync documentation, initialize documentation for an existing project, or when you want to understand the existing codebase structure at the start of a session. This skill should be used proactively after any significant code modification — don't wait for the user to ask. If you changed code, update the docs. Think of it as your long-term memory system: read before working, write after changing. Also use this when a user says "doctrack init", "doctrack refresh", "refresh docs", "update docs", "sync docs", "initialize docs", "document this project", or wants to bootstrap documentation for a codebase that has no .doctrack/ vault yet.
Progressive Domain Crystallization (PDC) — a skill for building and maintaining a living domain knowledge base for any custom business application. Use this skill whenever the user is developing a business application and wants the AI to accumulate understanding of internal terminology, entities, relationships, and business rules over time — especially when that knowledge is not fully defined upfront and grows across sessions. Trigger on any of: "remember how our system works", "learn our domain", "track business entities", "build domain knowledge", "understand our terminology", "grow AI context over time", "domain model", "business rules documentation", or whenever a user says the AI doesn't understand their business-specific language or data model. Also use at the start of any session where a DOMAIN.md file exists in the project — always read it before doing any work.
Generates comprehensive operational runbooks for any system or process. Reads codebase, infrastructure config, and deployment scripts to produce structured runbook.md files formatted for on-call engineers. Use when you need operations documentation, incident response guides, deployment procedures, or disaster recovery plans.
Analyzes code comments for accuracy, completeness, and long-term maintainability. Identifies misleading comments, comment rot, and documentation gaps. Triggers: After adding documentation, before finalizing a PR, when reviewing comments. Examples: - "Check if the comments are accurate" -> verifies comments match code behavior - "Review the documentation I added" -> analyzes new comments for quality - "Analyze comments for technical debt" -> finds outdated or misleading comments - "Are my docstrings correct?" -> validates documentation accuracy
Phase 3 of the feature workflow – Complete the acceptance closed-loop. Four tasks: 1. Check layer by layer against {slug}-design.md to verify if the implementation deviates from the plan; fix any deviations on the spot instead of just "noting them" in the report. 2. Incorporate this feature into the project's overall architecture documentation. 3. If this feature changes the user story or boundaries of the corresponding requirement, update the requirement doc accordingly. 4. If this feature originated from a roadmap item, change the status of the corresponding entry in roadmap items.yaml to done and sync it with the main document. Finally, produce a {slug}-acceptance.md as the closed-loop proof for the entire workflow. Prerequisite: cs-feat-impl is completed. Trigger scenarios: User says "The feature is done, let's accept it", "Do the final check", "Prepare for merge", "Generate the acceptance report".
Audit a skill repository or installed skill collection for global consistency, lifecycle coverage, routing quality, documentation drift, memory writeback coverage, stale future-skill references, broken helper paths, and validation readiness. Use this skill whenever the user asks for a global consistency audit, skill taxonomy review, lifecycle audit, cross-skill routing audit, README or AGENTS inventory consistency check, or maintenance pass over a collection of agent skills.
Add `///` doc strings to public objects in Motoko modules so that `mo-doc` produces useful HTML/Markdown documentation. Covers placement rules, formatting, code examples, common pitfalls, and a verification workflow.
Create, update, or refactor Storybook stories following the project's standard patterns. Use when adding stories for new components, updating existing stories, or fixing Storybook-related issues. Don't use for component implementation itself, design-system token changes, end-to-end browser tests, or non-Storybook documentation.