🎯Skills35
Interviews the user relentlessly about every aspect of a plan or design until reaching shared understanding, resolving each branch of the decision tree one by one. Provides recommended answers for each question and explores the codebase when answers can be found there.
An agent skill from Matt Pocock's engineering toolkit that helps improve your codebase architecture. Part of a composable skill set designed to fix common AI coding agent failure modes through structured, experience-based workflows.
A skill that runs a grilling session to challenge your plan against an existing domain model, sharpen terminology by building a shared language in CONTEXT.md, and document architectural decisions in ADRs before coding begins.
Enforces test-driven development with a strict red-green-refactor loop using vertical slices (one test then one implementation at a time), emphasizing behavior verification through public interfaces rather than implementation-coupled testing.
A skill that turns the current conversation context and codebase understanding into a structured PRD (Product Requirements Document) and submits it as a GitHub issue, with modular architecture and user story generation.
A skill that guides AI agents through a disciplined diagnosis loop for hard bugs and performance regressions, following a structured sequence of reproduce, minimise, hypothesise, instrument, fix, and regression-test steps.
Breaks a plan, spec, or PRD into independently-grabbable GitHub issues using tracer-bullet vertical slices. Each issue is a thin end-to-end path through every integration layer that can be demoed or verified on its own.
A meta-skill that guides the process of creating new agent skills with proper SKILL.md structure, progressive disclosure, and bundled resources. It helps gather requirements, draft skill files, and write effective descriptions.
Instructs the AI agent to step back from detailed code and provide a higher-level perspective, mapping all relevant modules and callers to help developers understand how a section of code fits into the broader architecture.
Initial setup skill for Matt Pocock's engineering skills collection that configures your backlog manager, triage labels, and documentation paths for AI coding agents.
Ultra-compressed communication mode that reduces token usage by approximately 75% by dropping filler words, articles, and pleasantries while preserving full technical accuracy in responses.
A composable skill for triaging issues and tickets using configurable labels, designed to fix common failure modes in AI coding agents by providing structured backlog management workflows.
A collection of small, composable engineering skills designed for real development workflows, featuring grilling sessions for requirement alignment, issue tracking integration, and task triage with customizable labels.
Composable, model-agnostic agent skills for real engineering workflows, featuring /grill-me for requirement alignment, /prototype for rapid iteration, and task management tools designed to fix common failure modes in AI coding agents.
A collection of composable engineering skills by Matt Pocock designed to fix common AI coding agent failure modes: misalignment via grilling sessions, verbosity through shared domain language (CONTEXT.md), code quality with TDD red-green-refactor loops, and codebase entropy with architecture improvement workflows.
Based on the "Design It Twice" philosophy, this skill spawns multiple parallel sub-agents to generate radically different interface designs for a module, then compares them on simplicity, depth, flexibility, and ease of use.
A skill that interviews the developer about a refactoring problem, explores the codebase to verify assertions, evaluates alternative approaches, checks test coverage, and creates a detailed GitHub issue with a plan of tiny, incremental commits.
Extracts a DDD-style ubiquitous language glossary from the current conversation, flags ambiguities and synonyms, proposes canonical terms with aliases-to-avoid and an example dialogue, and saves it to `UBIQUITOUS_LANGUAGE.md` in the working directory.
Sets up Claude Code PreToolUse hooks that intercept and block dangerous git commands (push, reset --hard, clean -f, branch -D, checkout/restore .) before execution, with configurable project-level or global scope installation.
Interactive QA session skill — the user reports bugs conversationally while the agent clarifies, explores the codebase in the background to learn domain language, decides whether to file a single issue or break it into dependency-ordered sub-issues, and creates each one via `gh issue create` using a durable, user-focused template (no file paths, no line numbers).
Searches, creates, and manages notes in Matt Pocock's Obsidian vault at `/mnt/d/Obsidian Vault/AI Research/` — Title Case filenames, flat layout, `[[wikilinks]]`, and Index notes that aggregate related topics.
A code review skill from Matt Pocock's engineering skill suite, designed to fix common failure modes in AI-powered development through grilling sessions for alignment, shared domain language via CONTEXT.md, test-driven development loops, and codebase architecture improvement.
A edit article skill from Matt Pocock's TypeScript skills for advanced type-level programming.
A skill that sets up Husky pre-commit hooks with lint-staged for Prettier formatting, type checking, and test running, automatically detecting the package manager and configuring the complete pre-commit pipeline.
A skill from Matt Pocock's "Skills For Real Engineers" collection that scaffolds coding exercises. Part of a composable, model-agnostic skill set designed for real engineering workflows with grilling sessions and structured development practices.
A migrate to shoehorn skill from Matt Pocock's TypeScript skills for advanced type-level programming.
A composable agent skill from Matt Pocock's "Skills For Real Engineers" collection, designed to fix common AI coding agent failure modes through grilling sessions, documentation-first workflows, and practical engineering practices that work with any model.
A composable engineering skill by Matt Pocock designed to fix common AI coding agent failure modes, featuring grilling sessions for alignment, documentation-driven development, and practical workflows based on decades of engineering experience.
A composable agent skill from Matt Pocock's "Skills For Real Engineers" collection, designed to fix common AI coding agent failure modes through grilling sessions, documentation-first workflows, and practical engineering practices that work with any model.
Guides the creation of a PRD (Product Requirements Document) through a structured interview process covering problem definition, solution options, scope negotiation, deep module extraction, and user story generation, then submits the result as a GitHub issue.
Breaks a PRD into independently-grabbable GitHub issues using tracer-bullet vertical slices — each a thin end-to-end path (schema, API, UI, tests), tagged HITL or AFK, created via `gh issue create`.
A skill that converts a PRD (Product Requirements Document) into a multi-phase implementation plan using tracer-bullet vertical slices, saving the output as a local Markdown file.
Triages a reported bug by exploring the codebase to find the root cause, then opens a GitHub issue (`gh issue create`) with a TDD fix plan structured as RED-GREEN cycles, acceptance criteria, and a refactor step — describes behaviors and contracts (not file paths) so the issue survives refactors.
Triages GitHub issues through a label-based state machine with interactive grilling sessions. Use when reviewing incoming bugs or feature requests, preparing issues for an AFK agent, or managing issue workflow via the `gh` CLI.
An interactive grilling session that stress-tests your plan against the existing domain model, sharpens fuzzy terminology, cross-references code for contradictions, and updates CONTEXT.md and ADR documentation inline as decisions crystallize.