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 agentmods add skills/cth9191/morning-intel/github-trendingnpx skills add cth9191/morning-intel --skill github-trendinggit clone --depth 1 https://github.com/cth9191/morning-intelWrote 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/cth9191/morning-intel/github-trending)<a href="https://agentmods.dev/skills/cth9191/morning-intel/github-trending"><img src="https://agentmods.dev/badge/skills/cth9191/morning-intel/github-trending.svg" alt="Measured on agentmods" 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 | $0.00092 | $0.00723 |
| Opus 5 | $0.00046 | $0.00362 |
| Sonnet 5 | $0.00018 | $0.00145 |
| Haiku 4.5 | $0.00009 | $0.00072 |
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 5d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Trending
Mechanical fetch — no AI judgment needed. Four metrics, ranks by stars / star-growth, flags AI/dev relevance from topics + description, writes markdown.
The four metrics
- This Week — repos
created:>7d, sorted by stars (REST search API). - This Month — repos
created:>30d, sorted by stars (REST search API). - Fastest Growing (24h) — top AI/dev repos by stars gained today, scraped from
github.com/trending?since=daily. Catches same-day spikes. - Fastest Growing (30d) — top AI/dev repos by stars gained this month, scraped from
github.com/trending?since=monthly. The durable breakouts.
Metrics 3 & 4 are the velocity pair. Unlike 1 & 2 (which filter on
created:<date> and so only ever see brand-new repos), they surface a repo of
any age that is suddenly ripping. A pure stars-sorted API query cannot do
velocity — the top of that list is all mega-repos, so a single-digit-k sleeper
never appears. The trending page is GitHub's own growth ranking.
Output
<VAULT>/inbox/research/github-trending/YYYY-MM-DD-trending.md — VAULT from
AGENTIC_OS_VAULT in ~/.claude/.env, default ~/the-vault.
Sections: Top 10 This Week, Top 5 This Month, Top 10 Fastest Growing (24h),
Top 10 Fastest Growing (30d), Content Radar (AI-pick summary). Each repo block:
stars, language, created date, topics, description, [AI/DEV] flag. If a
scrape fails, that section degrades gracefully and the others still render.
Manual invocation
python ~/.claude/skills/github-trending/scripts/fetch.py
AI/Dev classifier
Substring match (case-insensitive) on topics OR description against:
ai, llm, gpt, claude, anthropic, agent, mcp, prompt, embedding, rag, deepseek, kimi, gemini, openai, copilot, codex, fine-tun, transformer, ollama, langchain, chatgpt
Not perfect — false positives on "DarkGPT"-style spam, false negatives on AI projects with vague descriptions. Good enough for a glance.
Rate limit
Per run: 2 Search API calls + 2 trending-page GETs + up to ~1 enrich call per
trending repo scanned (GET /repos/{full}). Unauthenticated Search = 10
req/min; core REST = 60/hr per IP — fine for a few runs/day, but bursty manual
re-runs can exhaust it. Set GITHUB_TOKEN in ~/.claude/.env to lift both to
5000/hr.
What ships with it
1 file 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.
- 5d ago First seen · 55 lines · 92 tokens per session scan A c4f01d1a1d69
github-trending is a skill published in the GitHub repository cth9191/morning-intel (6 stars, last pushed 26d ago), licensed MIT. It adds 92 tokens to every session and 723 once invoked, about $0.0005 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 2026-08-31.
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