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 CHENyiru3/AI-Skills-Collections --skill skill-seekersgit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote 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/chenyiru3/ai-skills-collections/skill-seekers)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/skill-seekers"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/skill-seekers/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/chenyiru3/ai-skills-collections/skill-seekers"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/skill-seekers.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 | $0.00103 | $0.00653 |
| Opus 5.5 | $0.00041 | $0.00261 |
| Sonnet 5.5 | $0.00021 | $0.00131 |
| Haiku 4.5 | $0.00010 | $0.00065 |
Grade A, and why
skill-seekers 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 6d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Seekers
Skill Seekers is a CLI and MCP-oriented workflow for converting source material into structured AI knowledge assets. Use this skill to keep the interaction focused on source selection, packaging target, and the smallest command sequence that gets the user to a usable output.
Language Selection
- Default to references/english.md.
- If the user asks in Chinese or wants Chinese instructions, use references/chinese.md.
- Do not load both reference files unless translation or comparison is part of the task.
Core Workflow
- Identify the source type.
- Identify the target output.
- Recommend the shortest viable Skill Seekers flow.
- Expand into config, enhancement, or multi-source workflows only if the user needs them.
Keep the first answer concrete. Prefer commands over long explanations.
Source Types To Recognize
- Documentation sites
- GitHub repositories
- Local projects
- PDF, Word, EPUB, PowerPoint, and HTML files
- Jupyter notebooks
- OpenAPI specs
- RSS or Atom feeds
- Videos
- Confluence, Notion, Slack, or Discord exports
Common Targets
- Claude skill packages
- Gemini skill packages
- OpenAI or Custom GPT packages
- LangChain or LlamaIndex documents
- Haystack documents
- Markdown for vector databases
- IDE context or rule files for tools such as Cursor, Windsurf, Continue, or Cline
Response Pattern
When helping the user:
- State the assumed source and output target.
- Give the minimum install and command sequence first.
- Mention optional enhancement or packaging presets only when they materially help.
- If the task is broad or underspecified, ask for the source, target platform, and whether they want quick commands or a reusable config.
Quick-Start Bias
Start with the direct flow:
pip install skill-seekers
skill-seekers create <source>
skill-seekers package output/<name> --target <platform>
Only move to config-heavy or multi-source guidance after the simple path is clear.
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.
- 6d ago First seen · 82 lines · 103 tokens per session scan A 871456484389
skill-seekers is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 103 tokens to every session and 653 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-02.
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