andrej-karpathy-skills
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.
git clone --depth 1 https://github.com/dmae97/open-multi-agent-kit /tmp/andrej-karpathy-skills && cp -r /tmp/andrej-karpathy-skills/templates/skills/kimi/andrej-karpathy-skills ~/.claude/skills/andrej-karpathy-skillsSKILL.md
# andrej-karpathy-skills Source basis: forrestchang/andrej-karpathy-skills at commit 2c606141936f1eeef17fa3043a72095b4765b9c2. This OMK skill is a compact adaptation, not a vendored copy of upstream prompts or code. ## Use when - Any non-trivial code change needs tight scope and proof. - The request is ambiguous enough that silent assumptions could be wrong. - A refactor or bugfix risks drive-by edits. - You need to turn an imperative request into verifiable success criteria. ## OMK workflow 1. State assumptions and tradeoffs only when they affect the implementation path. Ask if ambiguity blocks safe progress. 2. Define success as observable checks: tests, typecheck, lint, build, screenshots, replay, or exact output. 3. Make the smallest change that satisfies the goal. Avoid speculative abstractions and features not requested. 4. Touch only files directly tied to the request. Preserve existing style and avoid unrelated cleanup. 5. Remove only dead code introduced by your own change unless the user asked for broader cleanup. 6. Verify, inspect the diff, and report remaining risks honestly. ## Output contract Return: - assumptions that mattered - success criteria and checks - changed files - commands run - pass/fail status - risks or blocked items ## Guardrails - Do not broaden scope to improve adjacent code. - Do not hide confusion behind a confident implementation. - Do not claim completion without evidence.
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.
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.
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.
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.