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 AllenS0104/skill-browser --skill summarygit clone --depth 1 https://github.com/AllenS0104/skill-browserWrote 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/allens0104/skill-browser/summary)<a href="https://agentmods.dev/skills/allens0104/skill-browser/summary"><img src="https://agentmods.dev/badge/skills/allens0104/skill-browser/summary/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/allens0104/skill-browser/summary"><img src="https://agentmods.dev/badge/skills/allens0104/skill-browser/summary.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 | $0.00033 | $0.00257 |
| Opus 5.5 | $0.00013 | $0.00103 |
| Sonnet 5.5 | $0.00007 | $0.00051 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
summary 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 1mo 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
Summary skill
This is the shortest English alias for:
content-summarycontent-search-summarization
It is best when you want to:
- search public content platforms
- rank the top N relevant results
- summarize each item with links and confidence notes
Primary guidance
- prefer
openclifor supported platforms - fall back to public-page scraping when needed
- rank by relevance first, popularity second
- keep summaries conservative when based on metadata
Quick invocation template
Use /summary to find the top 5 results for <topic> on <platform> and summarize each item with links and confidence labels.
请用 /summary 在 <平台> 搜索 <主题>，筛选 Top 5，并输出带链接与置信度的摘要。
Pointer
For the full detailed playbook, see:
skills/content-search-summarization/SKILL.mdin this repository
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.
- 1mo ago First seen · 42 lines · 33 tokens per session scan A 741e2cdc522d
summary is a skill published in the GitHub repository AllenS0104/skill-browser (5 stars, last pushed 7mo ago), licensed MIT. It adds 33 tokens to every session and 257 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
auto-research
Research uncertain questions with an explicit, user-approved web search or ChatGPT consultation, then present options and wait for implementation approval.
social-campaign
Turn one source artifact (release, README, post, changelog) into a reviewable, platform-native distribution campaign under human control.
image-generation
Plan, describe and register campaign media — platform-sized visuals with alt text and provenance in the media manifest.
content-writing
Turn the campaign content graph into genuinely platform-native drafts — voice, hooks, structure per platform culture.
post-linkedin
Adapt and publish campaign items on LinkedIn — professional narrative posts.
post-reddit
Adapt and publish campaign items on Reddit — community-native self-posts with subreddit-rule discipline.