Star Trajectory MCP server

star-trajectory

<!-- mcp-name: io.github.ardev-lab/star-trajectory -->

A transparent, dependency-free GitHub star-trajectory classifier. One Python file, no token, no install — point it at a repo and get its growth phase and a calibrated projection of whether it will reach a target (default 100★ in 48h), with every rule explained.

$ python3 classify.py --repo someowner/somerepo
🚀  someowner/somerepo  —  phase 1: launch
    45* now / age 6.5h / pushed 1.0h ago
    v_avg 6.95 / v_recent 11.19 pt/h / accel x1.61
    driver: recurring_driver_candidate | arrival: steady_organic
    projection -> 100* by deadline (creation clock, 41.5h left, decel x0.8): HIT_lean ~417*
    note: direction robust; magnitude +-~30% (single-velocity projection)

JA — GitHub repo の star 成長を phase (launch / accel / sustain / maturity) に分類し、「作成+48時間で100★に届くか」を予測する、透明・依存ゼロのツールです。 トークン不要、1ファイル、すべての判定根拠を表示します。確率値ではなく方向(HIT/ BORDERLINE/MISS)で出し、外れも含めて公開実績で自己採点します。

We grade ourselves in public

This isn't just a tool — it runs as a public prediction engine. Every day it picks young, still-undecided repos, predicts their 48h fate before it's known, and scores itself once the deadline passes. The running track record — including the misses — is here:

→ PREDICTIONS.md — open predictions + scored history + measured accuracy

Raw, machine-readable: predictions.json (the ledger) and calibration.json (our measured direction accuracy). A forecast you can't verify is marketing; this one you can.

What makes it different

Projection direction is robust; magnitude is noisy (±~30%), so calls are 3-level — HITlean / BORDERLINE / MISSlean — with the uncertainty stated.

• Honest about uncertainty. It never prints a fake-precise probability.

or any environment variable, and never writes files.

• A public, self-scoring track record, not a one-off claim (see above).
• Zero dependencies. Pure Python standard library. No pip install.
• No token, no account. Anonymous GitHub API. Never reads your GITHUB_TOKEN

a named, inspectable rule.

• One file. Copy classify.py anywhere and run it.
• Transparent. No ML black box. Every phase boundary and projection factor is

It pairs with its sibling fake-star-audit: star-trajectory asks where is this repo headed?, fake-star-audit asks is the growth even real? A HITlean built on purchased stars is noise — so the prediction engine runs every candidate through fake-star-audit and excludes HIGH-risk repos** from the track record.

Quick start

CLI

# no install needed — just the one file
python3 classify.py --repo facebook/react
python3 classify.py --repo facebook/react --json          # machine-readable
python3 classify.py --repo owner/name --target-stars 250 --deadline-hours 72
python3 classify.py --repo owner/name --prior "6.7,4.1,2.8"  # past velocity readings

Or install from PyPI (pip install star-trajectory) and run star-trajectory-cli. Note: the bare star-trajectory command is the MCP server (below), not the CLI.

Claude Code skill

Drop the skill/ folder into ~/.claude/skills/ (see skill/SKILL.md), then ask Claude Code "is github.com/owner/repo still taking off?".

MCP server (Claude Desktop, Cursor, …) — optional

An optional MCP wrapper exposes the classifier as the classifyrepo tool over stdio** (your client launches it locally; it opens no network server and reads no environment variables).

Published on PyPI as star-trajectory and in the MCP Registry as io.github.ardev-lab/star-trajectory:

{
  "mcpServers": {
    "star-trajectory": {
      "command": "uvx",
      "args": ["star-trajectory"]
    }
  }
}

Shortened. The full README is on GitHub.