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Wjs Evaling Voicedrop Prompts

Community
jianshuo
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".

Overview

Publisherjianshuo
Repositoryclaude-skills
Skill namewjs-evaling-voicedrop-prompts
Stars
137
Forks
22
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jianshuo on GitHub. Read the source before you install it.

Installation

Install the Wjs Evaling Voicedrop Prompts AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jianshuo/claude-skills/tree/main/wjs-evaling-voicedrop-prompts
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jianshuo/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/wjs-evaling-voicedrop-prompts .claude/skills/wjs-evaling-voicedrop-prompts
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wjs Evaling Voicedrop Prompts in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wjs Evaling Voicedrop Prompts on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wjs Evaling Voicedrop Prompts is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Wjs Evaling Voicedrop Prompts AI skill do?

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".

Why use Wjs Evaling Voicedrop Prompts on TypingMind?

Because you install it once and use it with any model. Wjs Evaling Voicedrop Prompts is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wjs Evaling Voicedrop Prompts in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jianshuo/claude-skills/tree/main/wjs-evaling-voicedrop-prompts. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Wjs Evaling Voicedrop Prompts?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wjs Evaling Voicedrop Prompts?

As many as you like. As long as a model supports skills, you can use Wjs Evaling Voicedrop Prompts with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wjs Evaling Voicedrop Prompts AI skill free?

Yes. It is published on GitHub by jianshuo under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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