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/dianel555/dskills/github-trending-analyzernpx skills add Dianel555/DSkills --skill github-trending-analyzergit clone --depth 1 https://github.com/Dianel555/DSkillsWrote 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/dianel555/dskills/github-trending-analyzer)<a href="https://agentmods.dev/skills/dianel555/dskills/github-trending-analyzer"><img src="https://agentmods.dev/badge/skills/dianel555/dskills/github-trending-analyzer.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 | $0.00065 | $0.02905 |
| Opus 5 | $0.00032 | $0.01452 |
| Sonnet 5 | $0.00013 | $0.00581 |
| Haiku 4.5 | $0.00006 | $0.00291 |
Grade A, and why
github-trending-analyzer 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Trending Analyzer
A workflow protocol for tracking GitHub trending repositories with LLM-powered analysis. Fetches trending projects, enriches each with structured Chinese insights (what/analogy/help/who), classifies by themes, compares against historical snapshots, and generates reports in two modes — a compact brief (default) or a detailed report with per-project analysis (opt-in).
Trigger Signals
- GitHub trending analysis
- Weekly tech trend report
- Repository discovery automation
- Incremental analysis refresh
- Theme-based repo categorization
Preconditions
- HTTP access to github.com/trending (no auth required for public trending)
- LLM backend capable of JSON-structured output (for the 4-field analysis schema)
- File system access for memory cache and report output
- HTML parsing capability (regex or DOM parser)
Strategy
Run the five-step pipeline in order.
Step 1: Fetch trending HTML
Construct the URL with time range and optional language filter:
https://github.com/trending[/{language}]?since={daily|weekly|monthly}
Fetch with a browser User-Agent to avoid bot detection. Parse the HTML to extract:
name(org/repo)url(full GitHub link)desc(one-line description from the page)lang(primary language)stars(total stargazers count)today_stars(increment for this period)
Regex patterns (reference from source):
- Project name:
<h2[^>]*>.*?<a href="/([^"]+)" - Description:
<p class="[^"]*col-9[^"]*"[^>]*>\s*(.*?)\s*</p> - Language:
<span itemprop="programmingLanguage">([^<]+)</span> - Stars: parse from
/stargazerslink text after stripping HTML tags - Today increment:
([\d,]+)\s*stars?\s*(?:this|today)(case-insensitive)
Step 2: Batch LLM analysis
For each batch of 5 projects (to avoid token limits), send this prompt to your LLM:
Analyze the following {N} GitHub Trending projects. Output strict JSON array.
Each project needs 4 fields:
- what: What it is (≤30 Chinese characters)
- analogy: Life analogy (one sentence)
- help: What it helps you do (2 items, each ≤40 chars, array)
- who: Who needs it (one sentence, ≤30 chars)
Project list:
1. org/repo (Language) — description...
2. ...
Output ONLY the JSON array, no other text. Example:
[{"name":"org/repo","what":"...","analogy":"...","help":["...","..."],"who":"..."}]
What ships with it
3 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.
- 5d ago First seen · 257 lines · 65 tokens per session scan A 41da4da8147d
github-trending-analyzer is a skill published in the GitHub repository Dianel555/DSkills (64 stars, last pushed yesterday), licensed MIT. It adds 65 tokens to every session and 2,905 once invoked, about $0.0003 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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