Découvrez les skills Tests et sécurité, comparez leurs dépôts et consultez leurs instructions.
Standardized templates and patterns for integrating skills into agent prompts. Reduces token overhead through reusable skill reference syntax, action verbs, and progressive disclosure usage guidelines.
Documentation consistency enforcement - prevents drift between README.md and actual codebase state. Auto-activates when updating docs, committing changes, or working with skills/agents/commands.
Git best practices, commit conventions, branching strategies, and pull request workflows. Use when working with git operations, commits, branches, or PRs.
Standardized output formats for research, planning, implementation, and review agents. Use when generating agent outputs or parsing agent responses.
Validates all documentation references - file paths, links, line numbers, code examples
GAIK (Generative AI Knowledge Management Toolkit) development guidance. Use when working with: structured data extraction from documents/PDFs/audio, schema generation, document parsing (VisionParser, PyMuPDFPar...
Thin orchestrator for Task Decomposition Protocol v2. Supports two modes - /plan (decompose spec into tasks) and /execute (run tasks via subagents). Delegates all state management to the tasker CLI.
Transform Tasker task definitions into rich, self-contained Beads issues. Bridges ephemeral execution units with persistent planning artifacts.
Emit info-level logs to file during task execution, similar to logger.info() calls in code
Show memory system status and tracked projects. Use to check memory system health and list projects.
Repair common issues with the memory system. Cleans up orphan temp files, stale sessions, lock files, and old logs.
Two-phase cleanup - normalize long filenames, then consolidate semantic duplicates using AI
AI-powered validation of knowledge notes against current codebase. Detects stale content by comparing documented knowledge with actual files.
Fix issues detected by mem-audit. Supports dry-run mode to preview changes before applying them.
Search the Claude Code knowledge base for errors, solutions, decisions, and patterns. Use this to find information from your memory system.
Explicitly save knowledge, patterns, decisions, or learnings to the knowledge base. Use when you want to document something for future reference.
Scan the knowledge base for structural issues like broken links, orphan notes, missing indexes, and optionally AI-powered content staleness detection.
Write specifications at the right depth for any project. Progressive disclosure from quick Linear issues to full AI feature specs. Embeds Linear Method philosophy (brevity, clarity, momentum) with context engin...
Plan the v1→v2→v3 agency progression for AI features. Walk through mapping how autonomy increases over time, define promotion criteria, and generate artifacts for stakeholder alignment. Based on CC/CD framework...
Expert system for designing and architecting AI agent workflows based on proven Meta methodologies. Use when users need to build AI agents, create agent workflows, solve problems using agentic systems, integrat...
Shape work using the Shape Up methodology (Ryan Singer, Basecamp). Walk through the 4-step shaping process to create pitches ready for betting. Distinguishes between established product mode (fixed time, variab...
[ARCHIVED] Full 4D Context Canvas reference. For new AI features, use /spec --ai. For debugging, use /ai-debug. For quality checks, use /context-check.
Full 5-stage PRD framework for complex features. Use for deep PRD work via /spec --deep full-prd. For quick feature specs, use /spec --feature instead.
Post-launch AI feature calibration workflow. Document error patterns, review eval performance, and decide on agency promotion. Based on CC/CD framework for continuous calibration of AI products.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.