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 huangjia2019/sdd-in-action --skill github-trendinggit clone --depth 1 https://github.com/huangjia2019/sdd-in-actionWrote 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/huangjia2019/sdd-in-action/github-trending)<a href="https://agentmods.dev/skills/huangjia2019/sdd-in-action/github-trending"><img src="https://agentmods.dev/badge/skills/huangjia2019/sdd-in-action/github-trending.svg" alt="Measured on agentmods" 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 | $0.00048 | $0.00304 |
| Opus 5 | $0.00024 | $0.00152 |
| Sonnet 5 | $0.00010 | $0.00061 |
| Haiku 4.5 | $0.00005 | $0.00030 |
Grade A, and why
github-trending 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 9d 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
GitHub Trending · AI Filter
抓 github.com/trending 的 Top 50 · 过滤 AI 相关。
流程
- 读
specs/github-trending-skill.md里的FILTER_TOPICS列表 - WebFetch
https://github.com/trending(HTML) - 解析 top 50 repo 的 [name, url, stars, topics, description]
- 过滤 topics 命中 FILTER_TOPICS 任一的 repo
- 输出 JSON 到 stdout · schema 见 spec
Failure Mode
- HTTP 超时（>10s）· 返回空数组
[]· 不抛异常 - HTML 结构变化（解析失败）· 日志 warn · 返回空数组
- 绝不输出非 JSON 内容
Time Budget
≤ 10s total。if HTTP timeout, abort immediately with empty result.
自测
- "给我今天的 AI trending" → 应触发
- "帮我读 README" → 不应触发
- "上周热门" → 不应触发（本 skill 只做今日）
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.
- 9d ago First seen · 34 lines · 48 tokens per session scan A 7d0784d63ea9
github-trending is a skill published in the GitHub repository huangjia2019/sdd-in-action (147 stars, last pushed 14d ago), licensed MIT. It adds 48 tokens to every session and 304 once invoked, about $0.0002 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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