matt-pocock-skills
Real-engineering alignment, shared-language, TDD, diagnosis, and architecture-review workflow adapted from mattpocock/skills for OMK. Use before non-trivial implementation, ambiguous product work, debugging loops, test-first changes, or codebase architecture cleanup.
git clone --depth 1 https://github.com/dmae97/open-multi-agent-kit /tmp/matt-pocock-skills && cp -r /tmp/matt-pocock-skills/templates/skills/kimi/matt-pocock-skills ~/.claude/skills/matt-pocock-skillsSKILL.md
# matt-pocock-skills Source basis: mattpocock/skills at commit f304057d61d3df3c9fd992ac2b6e3833cb9325fb. This OMK skill is a compact adaptation, not a vendored copy of upstream prompts or code. ## Use when - The request is underspecified and a wrong interpretation would waste work. - Product language, domain terms, or acceptance criteria are unclear. - A bugfix or feature needs a test-first loop. - The codebase is becoming hard to reason about and needs a scoped architecture pass. ## OMK workflow 1. Grill lightly: identify goal, non-goals, impacted users, constraints, and proof required. Ask only blocking questions; otherwise state assumptions. 2. Create shared language: capture domain terms, abbreviations, and project-specific meanings in the plan or relevant docs. 3. Convert the request into verifiable outcomes: failing test, typecheck, lint, screenshot, replay, or JSON gate. 4. Work in small slices. After each slice, run the cheapest useful feedback loop before widening. 5. For debugging, reproduce first, isolate the failing boundary, patch the smallest cause, then prove the original failure is gone. 6. For architecture cleanup, map module responsibilities and seams before editing; avoid rewrites not tied to the user goal. ## Output contract Return: - clarified assumptions or questions asked - shared terms or acceptance criteria - smallest implementation slice - feedback loop commands and results - follow-up architecture risks, if any ## Guardrails - Do not turn a small task into a process-heavy spec unless ambiguity is material. - Do not invent product requirements. - Do not skip verification because the plan was detailed.
Persistent memory, recall, session replay, and memory-governance workflow adapted from rohitg00/agentmemory for OMK. Use when setting up agent memory, deciding what to remember, importing/replaying sessions, reducing repeated context, or auditing memory safety.
Minimal, goal-driven, surgical coding workflow adapted from forrestchang/andrej-karpathy-skills for OMK. Use for coding, refactoring, debugging, and review tasks where assumptions, overengineering, or broad edits could cause regressions.
Legal workflow drafting, triage, review, research planning, legal operations, law-student or clinic support, and legal AI governance adapted from Anthropic claude-for-legal. Use for commercial, privacy, product, corporate, employment, regulatory, AI governance, IP, litigation, legal-clinic, and law-student tasks. Draft-only; attorney review and current source verification required.
Managed-agent teamwork, issue assignment, progress tracking, reusable-skill compounding, and handoff workflow adapted from multica-ai/multica for OMK. Use when coordinating multiple agents, converting work into agent-ready tasks, tracking blockers, or turning repeated solutions into skills.
Review AdaptOrch, OMK, and similar DAG multi-agent orchestration frameworks. Use when assessing DAG node responsibility, dependency edges, worker write authority, fallback/retry/timeout/evidence gates, review/merge boundaries, or reproducible decision traces.
Optional read-only OMK web/social/video research workflow inspired by Panniantong/Agent-Reach. Use for web search, current social evidence, YouTube/Bilibili/Reddit/Twitter/X/RSS/GitHub public research, and Agent Reach availability checks without auto-installing or collecting credentials.
Backend API review for NestJS, Express, FastAPI, database access, validation, auth, error handling, and API contracts.
Adversarial code review for diffs, logic correctness, type safety, test coverage, and security risk.