# Ai Fluency

> Analyze how the developer collaborates with AI coding agents (Claude Code, Cowork, Codex/ChatGPT app, Cursor — combined into ONE score and ONE profile) and produce an "AI fluency" skill map — the four AI-fluency competencies (Delegation, Description, Discernment, Diligence), the five measured dimensions, per-tool sub-scores, and practical growth moves that rewrite the user's own prompts. Use when…

- **Type:** Skill
- **Install:** `agentstack add skill-feloguarin-ai-fluency-ai-fluency`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [Feloguarin](https://agentstack.voostack.com/s/feloguarin)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [Feloguarin](https://github.com/Feloguarin)
- **Source:** https://github.com/Feloguarin/ai-fluency/tree/main/.claude/skills/ai-fluency
- **Website:** https://feloguarin.github.io/ai-fluency/

## Install

```sh
agentstack add skill-feloguarin-ai-fluency-ai-fluency
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# AI Fluency Analysis — one command, full run

You produce a reliable AI-fluency **skill map** for this developer from their real
coding-agent transcripts — **every tool on the machine, combined into one score and one
profile**: Claude Code, Cowork (Claude desktop), Codex (incl. the ChatGPT desktop app),
and Cursor. One run, three parts:

1. **Measure (deterministic).** `insight.py --source all` parses every source,
   de-contaminates and scrubs them, and computes the numbers — rate-based,
   confidence-hedged, archive-backed so it sees **more than Claude Code's 30-day window**.
   Each dimension blends only from the tools that can observe it, so a tool never gets
   blamed for a habit it can't record.
2. **Explore (Sonnet 4.6).** Parallel explorers read the evidence, one per AI-fluency competency.
3. **Analyze (Opus 4.8).** A senior assessor writes the skill map, **grounded in the bundled
   AI Fluency framework**, then verifies it is evidence-grounded.

The skill is self-contained: the engine and the framework are bundled next to this file at
`~/.claude/skills/ai-fluency/`, and all working files land in `~/.claude/insight/`.

## Step 1 — Measure + emit evidence

These working files live at fixed, reused paths, so first delete any leftovers from a
previous run (or a different person on a shared machine) — a stale `analysis.json` must
never survive into this run and get merged as if it were this user's:

```bash
rm -f ~/.claude/insight/evidence.json ~/.claude/insight/analysis.json
```

Then measure (use `--quiet` so the score is NOT surfaced yet — this is one run that should
end in a single finished report, not a score now and a report later):

```bash
python3 ~/.claude/skills/ai-fluency/insight.py --source all --evidence ~/.claude/insight/evidence.json --no-open --quiet -o ~/.claude/insight/ai_fluency_report.html $ARGUMENTS
```

(`--source all` reads every tool's standard location and can't take an explicit path — if
the user passed a PATH in `$ARGUMENTS`, drop `--source all` and run single-source on that
path instead, in BOTH this step and Step 3.)

This computes the de-contaminated evidence bundle and writes a fallback deterministic
report. **Do not report the score, archetype, or any result to the user yet** — keep going
to Steps 2–3 and only present the final, AI-personalized report. If it reports no
transcripts, tell the user to pass their transcript directory as `$ARGUMENTS` (default
`~/.claude/projects`). The evidence bundle carries a `meta.run_fingerprint` that binds any
analysis built from it back to this exact run.

## Step 2 — Run the two-model analysis workflow

Print the absolute paths the workflow needs (it reads them with its own Read tool):

```bash
python3 -c "import os; print(os.path.expanduser('~/.claude/insight/evidence.json')); print(os.path.expanduser('~/.claude/skills/ai-fluency/reference/ai-fluency-framework.md'))"
```

Then call the **Workflow** tool with:
- `name`: `ai-fluency`
- `args`: `{ "evidence": "", "framework": "" }`

The workflow returns the analysis as a JSON object (overall_read, skill_map of the four
competencies, top_growth, strengths). **Sonnet 4.6** explores, **Opus 4.8** analyzes +
verifies — model selection is baked into the workflow.

## Step 3 — Render the final report

Only do this if Step 2 actually returned an analysis. Write the workflow's returned JSON to
`~/.claude/insight/analysis.json` (absolute path; the directory exists from Step 1), then
merge it — passing the evidence bundle it was built from so the engine can confirm the
analysis belongs to this exact run:

```bash
python3 ~/.claude/skills/ai-fluency/insight.py --source all --analysis ~/.claude/insight/analysis.json --analysis-evidence ~/.claude/insight/evidence.json -o ~/.claude/insight/ai_fluency_report.html $ARGUMENTS
```

This Step-3 run is the FIRST time the score is printed (Step 1 was `--quiet`), so the user
sees one finished, AI-personalized report — not a score up front and a report later. The
engine fingerprints this run's data and compares it to the evidence bundle's
`run_fingerprint`; if they don't match (a stale or foreign analysis), it prints a note and
renders the deterministic report instead — so one run's verdict can never leak into another.
On success the report carries Opus's tailored, framework-grounded skill map AND your
highest-leverage growth moves (each rewriting one of your real prompts) on top of the
deterministic numbers. Point the user to `~/.claude/insight/ai_fluency_report.html`.

## Step 4 — Narrate (don't re-derive)

Only now, after the final report exists, give a short, encouraging read in chat: the
**one overall score + band + archetype** in one sentence, the **single highest-leverage
growth move** grounded in one of their real prompts, and their **strongest competency** as
the foundation. If the report is multi-source and the per-tool sub-scores differ sharply,
name the contrast in one sentence (e.g. "notably stronger in Claude Code than Cursor") —
it's often the most interesting fact in the report. Keep it to a paragraph or two; the
report has the depth.

## Fallbacks

- **No Workflow capability available?** The deterministic report from Step 1 is complete on
  its own — skip steps 2–3. Since Step 1 ran `--quiet`, read the numbers from
  `~/.claude/insight/evidence.json` (or re-run Step 1 without `--quiet`) to narrate, then
  open the report. It will say plainly that the AI skill-map stage didn't run.
- **Explicit path given?** Pass it as `$ARGUMENTS` in steps 1 and 3 (archiving is skipped
  for explicit paths by design).

## Notes

- Original transcripts are never modified. They're copied into an archive
  (`~/.claude/insight-archive`) so history outlives Claude Code's 30-day cleanup.
- Scores measure observable behavior, not intent; thin signals are flagged "low data" and
  hedged — don't over-claim on those.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Feloguarin](https://github.com/Feloguarin)
- **Source:** [Feloguarin/ai-fluency](https://github.com/Feloguarin/ai-fluency)
- **License:** MIT
- **Homepage:** https://feloguarin.github.io/ai-fluency/

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-feloguarin-ai-fluency-ai-fluency
- Seller: https://agentstack.voostack.com/s/feloguarin
- Browse the marketplace: https://agentstack.voostack.com/browse

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