ai-collab-profile

AI Collab Profile

Builds a gamified analytical portrait of the user's AI collaboration style from their local Claude Code JSONL logs: real metrics on a FIXED scale (comparable between people) plus fun statuses — a title, a level, achievements with rarity — and evidence-based prompt-engineering recommendations. It is NOT an RPG character sheet: no classes, no stat blocks; the analytics are the product, the statuses are the garnish.

The scale is a contract. All formulas and thresholds live in scripts/analyze.py (SCALE v1) and references/scale.md. NEVER adjust formulas, thresholds, rank mappings, or achievement conditions per user — comparability is the whole point. If a metric looks unflattering, it stays. Report numbers only from profile.json, never invent or soften them.

Workflow

1. Run the analyzer (Python 3, stdlib only, read-only — it never modifies logs):

`` python scripts/analyze.py -o profile.json # all projects python scripts/analyze.py --project <dir-name> -o profile.json # one project ` Default logs location: ~/.claude/projects. Use --projects-dir to override. Try python3 if python` is missing.

1. Read `profile.json`.

- {"error": "not_enough_data"} → tell the user at least 30 of their own messages are needed; stop. - "low_confidence": true (< 100 messages) → include a visible "small sample" caveat.

1. Render the analytical card following references/widget.md. One card, one

product: the substance is the analytics (volume, imperatives, tone markers, activity hours, strengths/weaknesses, recommendations); the gamified layer (title, achievements, rage gauge) is flavor woven into it — never the other way around. - If an inline widget tool is available in this environment, render it inline. - Always also write a self-contained ai-profile.html to the working directory and tell the user the path.

1. Explain the profile in the user's conversation language (the widget carries both

RU and EN labels from profile.json). Lead with what the numbers say — notable metrics, strengths and weaknesses; then the flavor: title (epithet + rank + level), achievements. Numbers verbatim from profile.json.

1. Give recommendations using references/recommendations.md: pick the entries whose

trigger conditions match the profile, present 3-6 of them, each with its evidence note. Do not moralize about tone — research shows aggregate quality impact of tone is near zero; frame tone findings as fun facts plus practical notes (see recommendations.md).

Rules

• •Read-only with respect to user logs. The only files written: profile.json, ai-profile.html.
• •Respond in the user's language; RU/EN both supported end to end.
• •Fixed scale: same formulas for everyone, always cite SCALE v1 version in the card footer.
• •Privacy: everything runs locally; nothing leaves the machine.
• •If the user asks to compare with friends: they run the same skill; identical scale

version = comparable results. Different scale versions are not comparable.