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Found 2,007 Skills
Provides automated, context-aware code reviews focusing on logic errors and style violations. Use during Pull Request cycles to identify potential bugs and maintain high code quality standards.
Use when creating new skills, editing existing skills, or verifying the validity of skills before deployment
Socratic discovery and design exploration before planning. Activates when starting non-trivial work — asks clarifying questions, explores alternatives and tradeoffs, produces a design document for approval. Pulls context from Linear issue description, linked docs, and existing CLAUDE.md learnings. Simple bugs and fixes skip this automatically.
Correct naming for a PR
Human Made engineering principles and code quality standards. Apply when writing code, reviewing code, planning implementations, or discussing architecture. Covers code quality priorities, simplicity over complexity, and avoiding over-engineering.
Create a new Architecture Decision Record with sequential numbering and AgentDB registration
Prepare code for a pull request by orchestrating sync, format, lint, test, review, and doc updates. Use when user says 'create a PR', 'prepare pull request', 'get ready for PR', 'validate my changes', 'prepare for review', 'pre-merge checks', or 'is this ready to merge'. Do NOT use for only running tests, only reviewing (use review-diff), only syncing with main (use merge-main), or actually opening the PR on GitHub — this skill stops at "ready."
Use when the user wants to bootstrap a target codebase for AI-driven development with Claude Code. Generates a concise CLAUDE.md grounded in the actual stack (build tools, test runner, code style), creates a docs/ folder skeleton (designs/, prd/, plans/), and seeds conventions (conventional commits, plan-checkbox format, where designs and PRDs live). Triggers on "init Claude in this repo", "set up CLAUDE.md", "bootstrap docs folder", "prepare this project for Claude Code", "scaffold AI dev workflow", "/init this project".
Manage project learnings. Review, search, prune, and export what gstack has learned across sessions. Use when asked to "what have we learned", "show learnings", "prune stale learnings", or "export learnings". Proactively suggest when the user asks about past patterns or wonders "didn't we fix this before?"
Generate a high-level overview of a file, directory, or project — answering "what is this and how is it organized." Produces a single summary entry covering purpose, structure, entry points, and dependencies. Re-running on the same target overwrites the existing overview.
Use when writing a new agent skill for the dogfooded-skills library — covers frontmatter spec, section structure, quality criteria, and antipatterns.
Analyze a GitHub issue and create a detailed technical specification