Skill: ngmeyer/llm-as-judge (version 1.2.0)

--- name: llm-as-judge description: Cross-model verification for complex tasks. Spawn a judge subagent with a different model to review plans, code, architecture, or decisions before execution. Use when working on "architecture", "system design", "complex feature", "security review", "production deployment", financial/trading systems, or when stuck after 3+ attempts. NOT for simple edits, config changes, or routine tasks. --- # LLM-as-Judge **Core principle:** Same model = same blind spots. Different model = fresh perspective. Cross-model review catches ~85% of issues vs ~60% for self-reflection. ## Activation Criteria **Use this pattern when:** - Architecture or system design decisions - Multi-file changes affecting >5 files or >500 LOC - Security-critical code (auth, payments, crypto/DeFi) - Financial/trading systems (market making, quant strategies) - Planning documents that will drive weeks of work - Stuck after 3+ failed attempts on same problem **Skip when:** - Simple edits, config tweaks, bug fixes with obvious cause - Documentation updates - Single-file changes under 100 LOC - Tasks where self-review is sufficient ## The Pattern ``` Executor (Model A) → Output → Judge (Model B) → Verdict → Action ``` **Verdicts:** APPROVE | REVISE (with specific feedback) | REJECT (restart) ## Model Pairing Use a different provider than the executor to avoid shared blind spots: - **Executor: Claude** → Judge: `kimi` or `grok` or `gemini-pro` - **Executor: Kimi/Gemini** → Judge: `opus` - **Principle:** Different provider, similar capability tier ## Judge Prompt Templates ### Plan/Architecture Review See `references/judge-prompts.md` for full templates covering: - Plan completeness, feasibility, risk, testing strategy - Architecture review with scoring (0-10 per dimension) - Code review checklist (correctness, design, safety, maintainability) ## Integration Points - **With adversarial review:** This IS the formalized version of "spawn a separate model to review" - **With planning-protocol:** Judge reviews the plan before the Execute phase - **With coding workflows:** Code → cross-model review → fix findings → test → build → push ## Quick Decision ``` Simple task? → Self-review Complex / high stakes? → LLM-as-Judge Stuck after retries? → LLM-as-Judge (fresh perspective) Financial/security? → LLM-as-Judge (mandatory) ``` ## Gotchas - **Same provider defeats the purpose** — Claude Opus judging Claude Sonnet shares the same training distribution. Use a different provider (Grok judging Claude, Gemini judging GPT, etc.). - **Vague judge output is useless** — If the judge says "looks good" without specifics, the prompt is too weak. Always require the judge to produce scored dimensions + specific actionable items, even if approving. - **Judge scope creep** — Judges sometimes rewrite the entire plan instead of reviewing it. Constrain the verdict to APPROVE / REVISE / REJECT with specific feedback, not a replacement solution. - **Approval rate drift** — If the judge approves >80% of submissions, the model pairing is too similar or the prompts are too lenient. Target 60-70% approval rate. - **Don't judge trivial tasks** — A 50-line CSS fix doesn't need cross-model review. Use the activation criteria in this skill strictly.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)