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 ardev-lab/star-trajectory --skill skillgit clone --depth 1 https://github.com/ardev-lab/star-trajectoryWrote 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/ardev-lab/star-trajectory/skill)<a href="https://agentmods.dev/skills/ardev-lab/star-trajectory/skill"><img src="https://agentmods.dev/badge/skills/ardev-lab/star-trajectory/skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ardev-lab/star-trajectory/skill"><img src="https://agentmods.dev/badge/skills/ardev-lab/star-trajectory/skill.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00082 | $0.00718 |
| Opus 5.5 | $0.00033 | $0.00287 |
| Sonnet 5.5 | $0.00016 | $0.00144 |
| Haiku 4.5 | $0.00008 | $0.00072 |
Grade A, and why
star-trajectory 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 1mo 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill classifies a GitHub repository's star-growth trajectory and projects a
target/deadline outcome. It wraps classify.py, a single-file, zero-dependency,
anonymous-API tool (no token, no file writes).
When to use
Trigger when the user asks anything like:
- "is
owner/repostill taking off, or has it plateaued?" - "will this new repo hit 100 stars in its first 48 hours?"
- "what growth phase is this trending repo in?"
How to run
classify.py lives at the repository root (one directory above this skill). Run:
python3 /path/to/star-trajectory/classify.py --repo <owner>/<name> --json
- Requires only Python 3.10+ and outbound HTTPS to
api.github.com. - Uses the anonymous GitHub API (60 req/h). Each call costs 2–3 requests.
- Optional:
--target-stars N,--deadline-hours H,--prior "v1,v2"(past velocity readings to tell an oscillation trough from a terminal stall).
How to interpret the JSON
Key fields:
phase(1–4) +phase_label: launch / accel / trajectory (sustain) / maturity.projection.lean:HIT_lean/BORDERLINE/MISS_lean/LOW_CONFIDENCE. Always citeprojection.uncertainty_note— direction is robust, magnitude is noisy (±~30%).driver_vs_burst: whether velocity is sustained by active development or is a decaying burst.discovery_onset: if a repo sat dormant then "launched", the deadline clock is re-anchored to launch, not creation.metrics: stars, age, v_avg, v_recent, accel_ratio.
How to respond to the user
Give a short, plain-language read, then the reason:
Likely yes (HIT_lean). It's in an accel phase — the most recent stars are arriving ~1.6× faster than its lifetime average, and there was a push an hour ago. Projected ~417★ by the 48h mark. (Direction is reliable; the exact number is noisy.)
Always state it's a heuristic projection, not a guarantee. If LOW_CONFIDENCE,
say why (e.g. too few recent dated stars, or a one-off sharing spike).
Pair with authenticity
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.
- 1mo ago First seen · 67 lines · 82 tokens per session scan A 3841c4acd2bf
star-trajectory is a skill published in the GitHub repository ardev-lab/star-trajectory (0 stars, last pushed 3mo ago), licensed MIT. It adds 82 tokens to every session and 718 once invoked, about $0.0003 per session on Opus 5.5. 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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