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 ChianW/C31 --skill c31-cvideogit clone --depth 1 https://github.com/ChianW/C31Wrote 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/chianw/c31/c31-cvideo)<a href="https://agentmods.dev/skills/chianw/c31/c31-cvideo"><img src="https://agentmods.dev/badge/skills/chianw/c31/c31-cvideo/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/chianw/c31/c31-cvideo"><img src="https://agentmods.dev/badge/skills/chianw/c31/c31-cvideo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00029 | $0.01064 |
| Opus 5 | $0.00015 | $0.00532 |
| Sonnet 5 | $0.00006 | $0.00213 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
C31-cvideo scanned grade A with 1 finding 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST https://youtube-transcript.ai/mcp \ How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
C31-Video-Ingest Skill
触发词
帮我看, 帮我总结, 看一下, 帮我总结这个视频, 看一下这个视频, 看这个视频
功能
接收任意 YouTube 链接 → 自动提取完整文字稿 → 保存到知识库 → 生成结构化分析
执行流程
Step 1: 接收链接
用户发送 YouTube 链接(支持多种格式):
https://www.youtube.com/watch?v=XXXXhttps://youtu.be/XXXXhttps://www.youtube.com/shorts/XXXX- 裸 video ID:
XXXX
Step 2: 提取 Video ID
用正则表达式从各种 URL 格式中提取 11 位 video ID。
Step 3: 获取文字稿(主路径)
调用 youtube-transcript.ai MCP 服务器:
curl -X POST https://youtube-transcript.ai/mcp \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"name": "get_youtube_transcript",
"arguments": {"video": "VIDEO_ID", "lang": "en"}
},
"id": 1
}'
特点:
- 免费,无需 API key
- 秒级返回
- 支持多语言字幕选择(lang 参数)
- 返回带时间戳的完整文字稿
Step 4: 兜底路径(无字幕视频)
如果 MCP 返回 TranscriptsDisabled 或 NoTranscriptFound:
- 检查环境变量
OPENAI_API_KEY - 用
yt-dlp提取音频(需ffmpeg) - 调用 OpenAI Whisper API 转录($0.006/分钟)
Step 5: 保存与归档
保存到: memory/moc/video_{video_id}_{timestamp}_{source}.md
Frontmatter 包含:
- video_id
- source (mcp / whisper)
- fetched_at
- url
- duration
- word_count
Step 6: 分析输出
自动分析并输出:
- 核心信息: 主角、产品、成绩
- 具体背景: 起点、早期失败、关键转折
- 方法论: 步骤拆解、工具使用
- 引爆案例: 具体帖子/内容、数据结果
- 可应用方式: 映射到用户当前项目
依赖
| 工具 | 必需 | 安装命令 |
|---|---|---|
youtube-transcript.ai MCP |
✅ | 无需安装,直调 API |
python3 |
✅ | 系统自带 |
yt-dlp |
⚠️ 仅兜底 | pip install yt-dlp |
ffmpeg |
⚠️ 仅兜底 | apt-get install ffmpeg |
OPENAI_API_KEY |
⚠️ 仅兜底 | 环境变量 |
脚本位置
~/.openclaw/scripts/c31-video-ingest.py
用法示例
# 直接给 video ID
python3 ~/.openclaw/scripts/c31-video-ingest.py 96jN2OCOfLs
# 给完整 URL
python3 ~/.openclaw/scripts/c31-video-ingest.py "https://www.youtube.com/watch?v=XXXX"
# 指定语言
python3 ~/.openclaw/scripts/c31-video-ingest.py XXXX --lang zh-CN
已知限制
- YouTube 直接访问在中国大陆环境可能超时(MCP 服务器在海外,通常可访问)
- MCP 返回的文字稿可能含重复行(YouTube 自动字幕的重复片段问题)
- 视频若无字幕且未配置 OPENAI_API_KEY,会失败
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.
- 8d ago First seen · 111 lines · 29 tokens per session scan A 40cd768f74a9
C31-cvideo is a skill published in the GitHub repository ChianW/C31 (1 stars, last pushed 13d ago), licensed MIT. It adds 29 tokens to every session and 1,064 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
compare-harnesses
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oia-manifest
Emit .harness/oia-manifest.json declaring layer alignment with the OIA v0.1 9-layer reference architecture. Self-describes the harness's MCP wiring, witness signing, audit log, identity posture (always 'none' at v0.1). --check verifies an existing manifest, --dry-run prints without writing, --json emits to stdout.
create-harness
Scaffold your own focused AI agent harness — pick host (Claude Code, Codex, pi.dev, Hermes), template, agents, skills, and ship a npm-publishable harness with its own npx CLI. Use when a user asks to "create my own agent harness", "scaffold a harness", "make a custom Claude Code plugin like ruflo", or "build a…
diag-harness
Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.
example-harness
Scaffold a ready-made AI agent harness in one command from the 19 published @metaharness/ example packages — 9 host integrations (Claude Code, Codex, Hermes, pi.dev, OpenClaw, RVM, Copilot, OpenCode, GitHub Actions) + 10 vertical pods (devops, research, trading, support, legal, coding, education, sales, gaming…
repo-genome
7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.