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/aeonfun/aeon/github-trendingnpx skills add aeonfun/aeon --skill github-trendinggit clone --depth 1 https://github.com/aeonfun/aeonWhat 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 | $0.00043 | $0.05336 |
| Opus 5 | $0.00022 | $0.02668 |
| Sonnet 5 | $0.00009 | $0.01067 |
| Haiku 4.5 | $0.00004 | $0.00534 |
Grade A, and why
github-trending scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Fetch the daily trending page via **WebFetch** (it renders the HTML for you; `curl` works too — there is no network sandbox): Copies of this mod
1 near-identical copy found in the catalogue:
- github-trending — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
${var} — Source selector plus optional sub-scope:
- empty or
github→ GitHub trending, all languages (default)github:<lang>— or a bare language token likepython,typescript,rust(backward-compatible with the old GitHub var) → GitHub trending filtered to that languagehforhuggingface→ Hugging Face trending across models + datasets + spaceshf:models/hf:datasets/hf:spaces(alsohuggingface:models, etc.) → Hugging Face trending scoped to a single resource type
This skill covers two neighbouring layers of where developer/AI attention is moving today: the repo layer (GitHub trending) and the artifact layer (Hugging Face Hub — the models, datasets, and spaces that ship alongside, and frequently before, the paper). Both branches share the same contract: don't dump the top 10 (the source's own front page already does that) — deliver a curated slate of 5–8 picks a busy reader would actually want to click, grouped by category, with a one-line "why notable" and a momentum tag per pick.
Shared preamble (run for every invocation)
Read memory/MEMORY.md for context.
Read the last 3 days of memory/logs/ to dedupe items you've already featured (the GitHub branch dedupes against the last 2 days, the Hugging Face branch against the last 3 — see each branch's filter step).
Read soul/SOUL.md + soul/STYLE.md if populated to match voice.
Parse ${var} into a source + optional sub-scope (deterministic):
- If
${var}is empty → GitHub branch, no language filter. - Otherwise trim + lowercase and split on the first
:intoheadand optionaltail. head∈ {hf,huggingface} → Hugging Face branch. Iftailis present it must be one ofmodels/datasets/spaces(that becomes the resource sub-scope); any othertail→ exitHF_TRENDING_BAD_VAR(no notify). Notail→ pull all three resource types.head==github→ GitHub branch. Iftailis present, it's the language filter.- Any other value (no colon,
headnothf/huggingface/github) → GitHub branch, treating the whole${var}as the language filter (e.g.rust).
Then jump to the matching branch below and run it end to end.
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.
- 2d ago First seen · 339 lines · 43 tokens per session scan A 737e884def0b
github-trending is a skill published in the GitHub repository aeonfun/aeon (706 stars, last pushed 2d ago), licensed MIT. It adds 43 tokens to every session and 5,336 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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