Claude Insight

See how you actually build with AI. Claude Insight reads your local Claude Code transcripts and turns them into one self-contained HTML report: a fluency score, your builder archetype, a 4-competency skill map, and the few highest-leverage things to change next — with the "before/after" rewrites drawn from your own prompts.

It all runs on your machine. Your transcripts never leave it, and the originals are never touched.

⚡ Install in 5 seconds

curl -fsSL https://raw.githubusercontent.com/Feloguarin/claude-insight/main/install.sh | bash

That drops the /ai-fluency skill into Claude Code. Then, inside Claude Code (any folder), just run:

/ai-fluency

One command, one finished report at ~/.claude/insight/ai_fluency_report.html. Requires Python 3.8+ and Claude Code.

🚀 What you get

• A fluency score (0–100) with a band — Operator → Developing → Proficient → Advanced → Expert — and what it means.
• Your builder archetype — Autonomous Agent, Architect, Debugger, Collaborator, or Sprinter — picked from your behavior, not from keywords.
• A 4-competency skill map — Delegation · Description · Discernment · Diligence (the AI Fluency framework) — each placed on a 1–5 level with one concrete next move.
• Five measured dimensions behind the map — Briefing, Verification, Context-setting, Iteration, Toolcraft — each a defensible rate, not a vanity count.
• What / Where / How — your top growth levers, each tied to real moments in your transcripts and (when you run the full skill) a rewrite of one of your own prompts.
• Full transparency on the data: how many prompts you really typed (vs. tool output, subagent turns, and injected noise), projects, MB, and hands-on time — across more than the 30 days Claude Code keeps on disk (see below).

🎯 How the score works (and what it won't do)

The whole point is to measure skill, not activity — so a few things are deliberate:

• Everything is a rate, then saturated. Each dimension is a per-prompt or per-opportunity rate run through min(1, rate / target). Doing more of the same thing doesn't move the number — only doing it better does.
• Thin data is hedged, not faked. When you have little history, each dimension is pulled toward a neutral 50 in proportion to how few opportunities it had. So your first runs read conservatively and then firm up over your first few dozen prompts as the tool gets confident, and settle (each dimension reaches full confidence at its own count — e.g. ~60 briefing-prompts, ~15 edit-bursts). If an early score creeps up run-over-run, that's the hedge lifting toward your real level — it plateaus once there's enough data, and from then on only changing your habits moves it. Thin dimensions are flagged low data.
• The score rates the collaboration; the archetype rates you. The fluency score is the quality of you-and-Claude together — and that includes habits Claude often does on its own, like reading a file before editing (Context-setting) or running tests (Verification), which are ~44% of the weight. Your archetype is built from a separate, agency-weighted vector that discounts those Claude-driven habits, so it reflects how you drive. The two can differ on purpose: a thorough agent lifts the collaboration score more than it lifts the archetype.
• Noise is stripped before anything is scored. Tool results, subagent (sidechain) turns, slash-command stubs, injected system text, and pasted walls of text (over ~6k chars) don't count as your prompts. Idle gaps longer than 5 minutes are excluded from "active time," so it's hands-on time, not wall-clock.

Both the raw and the confidence-adjusted scores are shown in the report, and the full methodology is in an appendix at the bottom of it.

🧠 One command, the full analysis

/ai-fluency runs the complete pass as one pass that ends in a single finished report — it won't flash a score first and a report later:

1. Measure — insight.py de-contaminates and scrubs your transcripts and computes every number (rate-based, confidence-hedged, archive-backed so it sees more than the 30-day window). This step is silent on purpose.
2. Explore — Claude Sonnet 4.6 — four explorers run in parallel, one per AI-fluency competency, reading only your de-contaminated evidence.
3. Analyze — Claude Opus 4.8 — a senior assessor writes the skill map and your growth levers grounded in reference/ai-fluency-framework.md, then a verifier checks every claim against your evidence and repairs anything ungrounded.

The numbers are always computed deterministically; the models add judgement and direction on top and never change the math. It runs on your existing Claude Code session — no separate API key — and the models are pinned per stage in .claude/workflows/ai-fluency.js.

Two guarantees worth calling out:

• Your "how to grow" cards are written from your real prompts — the "before" is something you actually typed and the "after" is Opus's tailored rewrite of it, not a stock example.
• An analysis can't leak across runs or people. The evidence bundle carries a fingerprint of the exact run it came from; the report engine refuses to merge an analysis whose fingerprint doesn't match, and falls back to the deterministic report (and says so) instead.

If the Workflow capability isn't available, the skill still produces the complete deterministic report — scores, archetype, dimensions, and skill levels — with generic, clearly-labeled growth examples instead of the Opus-written ones.

Data export & flags

python3 insight.py --json                                          # metrics + data breakdown as JSON
python3 insight.py --evidence ev.json                              # write the de-contaminated evidence bundle (carries a run fingerprint)
python3 insight.py --analysis an.json --analysis-evidence ev.json  # merge an Opus analysis, bound to this run
python3 insight.py /path/to/transcripts                            # analyze a specific directory or .jsonl file
python3 insight.py --quiet                                         # suppress the terminal summary (the skill's measure step uses this)
python3 insight.py --no-open                                       # don't auto-open the browser
python3 insight.py --archive ~/my-archive                          # keep history in a PRIVATE, per-person durable folder
python3 insight.py --no-archive                                    # analyze without copying anything new

⏳ Analyzing more than 30 days

By default Claude Code deletes transcripts older than its cleanupPeriodDays setting (default 30), so only ~30 days of history is ever on disk — that's a limit of the data, not of this tool, which reads everything available. Two things let it see more:

1. Stop the deletion. Add this to ~/.claude/settings.json so Claude Code keeps a full year (set it before more history ages out):

   copy

   { "cleanupPeriodDays": 365 }
   

2. The built-in archive (automatic). On every default run, Claude Insight copies your transcripts into a persistent archive (~/.claude/insight-archive by default) before the cleanup can remove them, then analyzes live + archive, de-duplicated. From your first run on, your history accumulates indefinitely. It only ever grows files, copies atomically, and stays 100% on your machine.

   Keep the archive private to you. A single archive folder shared between people (e.g. a synced team Dropbox) would merge everyone's transcripts into one report — so point --archive at your own location, not a shared one:

   copy

   python3 insight.py --archive ~/Dropbox/claude-archive   # your own, private folder — survives reinstalls
   # or set CLAUDE_INSIGHT_ARCHIVE in your shell. Use --no-archive to skip a run.
   

📦 Run from source (no install)

insight.py is a single, pure-standard-library file — clone and run it, nothing to pip install:

git clone https://github.com/Feloguarin/claude-insight.git
cd claude-insight
python3 insight.py                 # analyze ~/.claude/projects, then write + open the report

Requires Python 3.8+. On its own, python3 insight.py produces the complete deterministic report (scores, archetype, dimensions, growth levers with generic examples). The Opus-personalized rewrites come from running /ai-fluency inside Claude Code.

📊 Example output

  AI Fluency Score: 78/100  (Advanced)
  Archetype: 🤖 Autonomous Agent
  Based on 156 real prompts across 16 projects, 156 sessions (53.8 MB).
  Archive: 156 sessions preserved at ~/.claude/insight-archive (0 new, 1 updated this run).
  Report: ai_fluency_report.html

(Illustrative — your numbers will differ.) The HTML report adds the headline score ring, the four-competency skill map (your level and next move for each), the five dimensions, your top growth levers with before/after rewrites, archetype affinity, a "how much data this is based on" breakdown, and a methodology appendix.

🏗️ Architecture

insight.py                       # the whole engine: parse → de-contaminate → score → report
                                 #   (pure stdlib, zero install; --evidence / --analysis hooks)
reference/
└── ai-fluency-framework.md      # the 4D framework the Opus analysis stage is grounded in
.claude/
├── skills/ai-fluency/SKILL.md   # /ai-fluency — orchestrates the one-command pipeline
└── workflows/ai-fluency.js      # Sonnet 4.6 explore → Opus 4.8 analyze → verify
tests/                           # stdlib unittest (de-contamination, scoring, archive,
                                 #   analysis-provenance, personalized growth)

📈 What's measured

The four AI-fluency competencies (the skill map)

Adapted from Anthropic's AI Fluency: Frameworks & Foundations (the 4 Ds):

1. Delegation — deciding what to hand to the agent, and how to split the work.
2. Description — telling the agent what you want (goal + constraint + acceptance test).
3. Discernment — evaluating what comes back (verify, ground edits, correct precisely).
4. Diligence — being responsible: verify before it ships, tear down, own the result.

The five dimensions behind the map (with weights)

1. Briefing / Direction (24%) — how concretely you frame requests (constraint / artifact / intent rates).
2. Verification (22%) — running tests / build / app after a burst of edits.
3. Context-setting (22%) — grounding edits in a prior read, instead of blind edits.
4. Iteration (18%) — correcting precisely instead of vague rejection.
5. Toolcraft (14%) — reaching for a healthy range of tools, not forcing everything through one.

(Verification and Context-setting are largely habits Claude drives on its own — counted in the collaboration score, discounted in the archetype.)

Archetypes (from your behavior, not keywords)

• 🤖 Autonomous Agent — delegates whole, end-to-end jobs and trusts the agent to run them.
• 🏗️ Architect — plans and explores before building; reads and designs first.
• 🐛 Debugger — methodical: read to diagnose, change, verify, repeat.
• 🤝 Collaborator — works with the agent like a teammate: asks for options, gives feedback.
• ⚡ Sprinter — fast and direct, terse prompts, low ceremony; verification is the growth edge.

The archetype is the nearest match to your agency-weighted behavior vector — it counts what you do (briefing, correcting, tool choice, delegation) and discounts the read-before-edit and run-the-tests habits Claude does on its own, so it reflects you, not the agent. Near-ties are reported as a blend, never a coin-flip.

🔒 Privacy

Everything is local. Transcripts are read from ~/.claude/projects (and your archive), analyzed on your machine, and written only to the report path you choose (the /ai-fluency skill keeps everything under ~/.claude/insight/). Nothing is uploaded; there's no API key and no telemetry. The Sonnet/Opus stages run inside your own Claude Code session. Your original transcripts are never modified, and the working files that hold your real prompts (evidence.json, analysis.json, the report) are git-ignored so they can't be committed.

🔧 Development

# Run the test suite — standard library only, no pytest required
python3 -m unittest discover -s tests

📄 License

MIT License — see LICENSE.