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wjs-evaling-voicedrop-prompts

Use when 王建硕 wants to evaluate whether a change to VoiceDrop's 挖矿 system prompt is actually better than the live version — runs the local eval harness (golden fixtures × champion-vs-candidate, same input), dispatches blind pairwise judge subagents, aggregates a win-rate verdict, and on approval promotes the candidate into agent/src/prompts/mine.js. Triggers — "评估 prompt"、"挖矿 prompt 改好了吗"、"eval prompt"、"比一比两版 prompt"、"/wjs-evaling-voicedrop-prompts".

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jianshuo/claude-skills

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Files included (2)

  • SKILL.md2.8 KB
  • references/judge-rubric.md1.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

VoiceDrop 挖矿 prompt 评估

harness = 本 skill（协议）+ jianshuo.dev agent/eval/（脚本/数据）。运行时 = 本地 Claude Code。 被测对象 = agent/src/prompts/mine.js 的 MINE_SYSTEM（git 即版本库）。

何时用

用户改了挖矿 prompt（MINE_SYSTEM），想用数据判断改好了还是改坏了，而不是凭感觉看一两次。

流程（按序）

  1. 拿候选 prompt：把候选版 MINE_SYSTEM 文本写到一个临时文件（如 /tmp/cand-prompt.txt）；冠军 = 当前 mine.js 的 MINE_SYSTEM（脚本自动读）。
  2. 跑产出：cd ~/code/jianshuo.dev/agent && CLAUDE_API_KEY=$CLAUDE_API_KEY node eval/run-eval.mjs /tmp/cand-prompt.txt <runId>。产出落 eval/runs/<runId>/。先看终端有没有「确定性回退」警告——有就先停，多半是候选 prompt 破坏了 JSON 输出。
  3. 成对盲评：对每条 fixture，dispatch 一个 subagent，喂 references/judge-rubric.md + 该 fixture 的 transcript + 两份产出。A/B 顺序随机（一半 fixture 把 candidate 放 A、一半放 B，记录映射，收到结果后还原成 champion/candidate）。裁判模型用与生成（opus）不同家族的模型。收每条的 {winner, dims, reason}。
  4. 聚合：把还原后的 verdicts（winner ∈ candidate/champion/tie）+ 候选 proxyFails 喂 aggregate()，渲染 renderReport() → 写 eval/runs/<runId>/report.md。
  5. 人工终审：把胜负最接近、分歧最大的 1–2 条产出并排摆给用户。机器只筛掉明显更差的，文风最后一票是用户。
  6. 晋级：仅当 decision==="promote"（胜率 ≥70% 且无回退）且用户点「认可」——把候选写回 agent/src/prompts/mine.js 的 MINE_SYSTEM，commit（message 附 runId 与胜率），并跑 npm test 确认没破坏。否则保留报告、不动生产版。

边界

  • 只评挖矿 prompt（MINE_SYSTEM/MINE_SYSTEM_FORCE）。审核/语音编辑 prompt 是不同 eval 模式，不在本 skill。
  • 不测成本/缓存/延迟（本地缓存行为≠生产）；不做无人值守。
  • 真实金标集要 ≥10 条才可信（见 agent/eval/fixtures/README.md 的补充流程）；种子 2 条只够自测流程。

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