Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/chubbyguan/chubbyskillsnpx agentmods add skills/chubbyguan/chubbyskills/content-enrichWrote 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/chubbyguan/chubbyskills/content-enrich)<a href="https://agentmods.dev/skills/chubbyguan/chubbyskills/content-enrich"><img src="https://agentmods.dev/badge/skills/chubbyguan/chubbyskills/content-enrich/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/chubbyguan/chubbyskills/content-enrich"><img src="https://agentmods.dev/badge/skills/chubbyguan/chubbyskills/content-enrich.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 | $0.00073 | $0.00688 |
| Opus 5 | $0.00036 | $0.00344 |
| Sonnet 5 | $0.00015 | $0.00138 |
| Haiku 4.5 | $0.00007 | $0.00069 |
Grade A, and why
content-enrich 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 10d 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.
What it actually says
内容加工（Content Enrich）
采集进来的转录稿、文章往往是大段原始文本，没人愿意回头看。这个 skill 给任意采集产物 自动补上可快速消化的元信息——它是「加工层」的通用能力，配合仓库里所有采集 skill 使用。
环境要求
# 零 pip 依赖（仅 Python 标准库）
export DEEPSEEK_API_KEY="your-api-key" # 必需，用于提炼
使用方法
python scripts/enrich.py 某篇笔记.md # 就地增强单篇
python scripts/enrich.py ./vault/外部输入/ # 批量增强整个目录下的 .md
python scripts/enrich.py 某篇.md --output ./out # 不改原文件，输出到 out/
python scripts/enrich.py 某篇.md --force # 已加工过的重新加工
产出
在原 Markdown 上（原内容完整保留）：
- frontmatter 注入：
summary（一句话总结）、domain（领域）、auto_tags（标签列表）、enriched: true - 正文顶部插入
## 📝 摘要区块：一句话 TL;DR、3-5 条要点、「值得深读？」判断、标签
幂等：默认跳过已加工（enriched: true）的文件，可 --force 重做。
为什么有用
- 盘活采集产物：转录稿 / 文章一眼能看懂讲了啥、值不值得深读
- 便于知识库聚合：
auto_tags/domain进 frontmatter，Obsidian Dataview 可直接按标签 / 领域聚合 - 一处投入，惠及全部：抖音 / B站 / 小红书 / X / 公众号 …… 任何采集产物都能加工
衔接工作流
采集（任意 *-ingest / *-transcribe skill）
→ content-enrich（补摘要 + 要点 + 标签）
→ knowledge-base-management 入库（按 auto_tags / domain 聚合检索）
参考
- DeepSeek — 提炼用 LLM
What ships with it
2 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.
- 10d ago First seen · 63 lines · 73 tokens per session scan A 51f2b6c73107
content-enrich is a skill published in the GitHub repository chubbyguan/chubbyskills (665 stars, last pushed 21d ago), licensed MIT. It adds 73 tokens to every session and 688 once invoked, about $0.0004 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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