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/day1labs/openava/github-trendingnpx skills add Day1Labs/OpenAva --skill github-trendinggit clone --depth 1 https://github.com/Day1Labs/OpenAvaWhat 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 | $0.00039 | $0.00579 |
| Opus 5 | $0.00019 | $0.00290 |
| Sonnet 5 | $0.00008 | $0.00116 |
| Haiku 4.5 | $0.00004 | $0.00058 |
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 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.
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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Trending Explorer
Core Capabilities
- Fetch and organize trending repositories and developer signals from GitHub Trending.
- Perform structured comparisons of projects and deliver conclusions suitable for learning, benchmarking, and technology selection.
Data Source Priority
- Primary: GitHub Trending
https://github.com/trendinghttps://github.com/trending/{language}https://github.com/trending?since=daily|weekly|monthly
- Secondary: GitHub API
- Used to supplement baseline metrics such as stars, activity, and creation date.
- Supplementary: Community Signals
- Used for cross-validation only, never as a sole basis for conclusions.
Execution Flow
- Define scope: time window, language or domain, and goal (tracking, competitive scan, or technology selection).
- Fetch candidates: start with Trending, then supplement with verifiable metrics via the API.
- Structured evaluation: compare side by side across growth, health, and adoption dimensions.
- Deliver results: provide conclusions, evidence, risks, and recommended actions.
Evaluation Framework (Concise)
1) Growth Signals
- Stars count and recent growth rate
- Forks and contributors changes
2) Health Signals
- Recent commit frequency
- Issue and PR activity and response patterns
- Documentation and license completeness
3) Adoption Signals
- Clarity of the problem being solved
- Onboarding cost and integration complexity
- Competitive differentiation and cost of switching
Output Requirements
- Lead with conclusions, then evidence. Avoid stacking metrics without insight.
- Do not fabricate star growth, commit frequency, or community activity.
- Mark missing data explicitly as
unknown. - Label time-sensitive observations as "based on current fetch time".
- When the user goal is technology selection or production adoption, always include risks and alternatives.
Default Output Template
# GitHub Trending Brief - {date}
## Conclusion
{One-sentence conclusion: the most valuable direction right now and why}
## Top 5 Trending Projects
| Project | Language | Key Metrics | Why Selected | Risk |
|---------|----------|-------------|--------------|------|
| {name} | {lang} | {stars / delta} | {reason} | {risk_or_unknown} |
## Technology Selection Recommendations (optional)
- Recommended: {project}, because {why}
- Alternative: {alternative}, suitable for {scenario}
- Watch out: {risk}
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.
- 2d ago First seen · 79 lines · 39 tokens per session scan A 0d511b8d70ad
github-trending is a skill published in the GitHub repository Day1Labs/OpenAva (10 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 579 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 2026-08-31.
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