Browse Skills

Plan a huge chunk of work — more than one agent session can hold — as a shared map of decision tickets on your issue tracker, and resolve them one at a time until the way to the destination is clear.

Moves issues and external pull requests through triage states and roles. Use it to classify, verify, and prepare items with agent-ready or human-ready briefs.

Breaks a plan, spec, or conversation into tracer-bullet tickets with blocking edge declarations, published to an issue tracker.

Synthesizes the current conversation and codebase context into a spec (PRD) and publishes it to the project issue tracker with appropriate labels.

Turn approved specs and tickets into a verifiable execution goal without re-interviewing the user. Gathers evidence, selects scope, and drafts goals for coding agent sessions.

Enables an AI agent to teach you a skill or concept through a structured workspace with interactive HTML lessons, reference materials, and learning records, emphasizing long-term retention and practical application.

Guides test-driven development for AI coding agents, ensuring tests are spec-like, seams are confirmed, and the red-green loop produces maintainable tests. Use when building features or fixing bugs test-first.

Configures a project's issue tracker, triage labels, and domain documentation layout for Matt Pocock's engineering skills suite. Run once before using other skills like triage or to-tickets.

Resolves in-progress git merge or rebase conflicts by identifying primary sources, preserving intent, and running automated checks. Use when you need to finish a merge or rebase without aborting.

Investigates a question using primary sources such as official docs, source code, and specs, then writes the findings to a Markdown file in the repo. Use when you need facts gathered or reading work delegated to a background agent.

Build a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a UI should look like.

Scans a codebase for architectural friction, identifies shallow modules, and presents deepening opportunities as an interactive HTML report. Guides refactoring through a decision-tree grilling process, improving testability and AI-navigability.