Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jianshuo/claude-skills --skill wjs-mining-voicedropgit clone --depth 1 https://github.com/jianshuo/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jianshuo/claude-skills/wjs-mining-voicedrop)<a href="https://agentmods.dev/skills/jianshuo/claude-skills/wjs-mining-voicedrop"><img src="https://agentmods.dev/badge/skills/jianshuo/claude-skills/wjs-mining-voicedrop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jianshuo/claude-skills/wjs-mining-voicedrop"><img src="https://agentmods.dev/badge/skills/jianshuo/claude-skills/wjs-mining-voicedrop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00105 | $0.02646 |
| Opus 5 | $0.00053 | $0.01323 |
| Sonnet 5 | $0.00021 | $0.00529 |
| Haiku 4.5 | $0.00011 | $0.00265 |
Grade A, and why
wjs-mining-voicedrop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wjs-mining-voicedrop
VoiceDrop 收件箱（jianshuo.dev/files 上的 VoiceDrop-*.m4a）→ 逐条转写 → 交给 wjs-mining-articles 出公众号草稿。这是 VoiceDrop iOS app（开口即录、停即上传）的 Mac 端闭环。
本 skill 自身的产出 = ① 公众号草稿（~/code/wechat-publish/）+ ② 本地音频/SRT 存档（~/code/voicedrop/archive/）+ ③ R2 上的处理标记（articles/<stem>.json 或 .empty）+ ④ 一份批次报告（处理几条、各出几篇、哪些标了无语音及原因、还剩几条未处理）。 完整接口契约见 agents/interface.yaml。
Core Principle
复用，不重写。 本 skill 只做两件本身没有的事：收件箱的进出（列/下载/标记）和逐条编排。转写交 wjs-transcribing-audio，成文交 wjs-mining-articles，一行都不重写。
R2 永不删，用标记文件表示处理状态。 音频一直留在 R2，直到用户自己在 app 里删。「未处理」= 还没有 articles/<stem>.json（已成文）也没有 articles/<stem>.empty（无语音）标记的 VoiceDrop-*.m4a；list 已自动只列未处理的。一条成功成文后写 mark-done，没语音/损坏写 mark-empty——两者都让这条不再被重复处理。绝不 delete（delete 只留给用户在 app 里手动清理）。
When This Skill Fires
- 用户说「处理 VoiceDrop 录音」「把新录音挖成文章」「处理一下我的口述」
- 用户跑
/wjs-mining-voicedrop
When NOT to use
- 已经有 SRT → 直接
wjs-mining-articles - 音频不在 R2 收件箱（本地散文件）→ 直接
wjs-transcribing-audio出 SRT，再wjs-mining-articles - 桶里是别的机器传的非录音文件 → 本 skill 只认
VoiceDrop-*.m4a前缀，其余不碰
前置
~/code/.env里有FILES_TOKEN（收件箱鉴权）和火山 ASR creds（VOLC_ASR_*/VOLC_TTS_*，转写用）。set -a; source ~/code/.env; set +a。
Workflow
唯一的新增代码：scripts/voicedrop-inbox.sh（list / download / mark-done / mark-empty / delete，token 运行时从 ~/code/.env 读，绝不落代码）。list 只列未处理；mark-done/mark-empty 写处理标记；delete 只给手动清理用，成文流程不调它。
Step 0 · 定位脚本 + 载入环境（不依赖当前目录）
INBOX=~/.claude/skills/wjs-mining-voicedrop/scripts/voicedrop-inbox.sh
set -a; source ~/code/.env; set +a # FILES_TOKEN + 火山 ASR creds
用绝对路径 $INBOX 调脚本——不要写成 scripts/voicedrop-inbox.sh，那依赖「人恰好在 skill 根目录」这个隐藏假设，换目录就崩。
Step 1 · 列收件箱
"$INBOX" list # 打印未处理的 VoiceDrop-*.m4a，一行一个
- 命令非零退出（网络不通 / token 失效）→ 报「收件箱连不上或 FILES_TOKEN 失效，检查
~/code/.env」并停，不进入循环。 - 输出为空 → 报「收件箱没有新录音」结束。
- 非空 → 拿到这一批文件名。
Step 2 · 逐条闭环（串行，一条跑完再下一条）
串行。批次韧性：单条任何一步失败 → 记录原因、跳到下一条、绝不中止整批、绝不漏标。 每条录音最终必须落到三个终态之一：已成文（mark-done）/ 无语音（mark-empty）/ 失败（不标，留待下次）——绝不「处理了却什么都没标」。对每个 <name>：
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 114 lines · 105 tokens per session scan A da09c8677a9c
wjs-mining-voicedrop is a skill published in the GitHub repository jianshuo/claude-skills (129 stars, last pushed 23d ago), licensed MIT. It adds 105 tokens to every session and 2,646 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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