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ai-fluency-assessment-skill

A Claude Code skill for quarterly AI fluency assessment using Anthropic's 4D Framework and Yegge's 8-Stage Developer-Agent Evolution Model

n-pillai/ai-fluency-assessment-skillcommunitylistedunreviewed

Set up

Skills are installed by placing their folder where your agent loads skills.

The download comes from github.com/n-pillai/ai-fluency-assessment-skill itself, not from our server, so what you get is exactly what its author published.

Install instructions come from the README

Skills do not use an MCP config. Follow the steps in the README below.

README

synced Oct 17, 2026

AI Fluency Assessment Skill

A Claude Code skill for quarterly assessment of AI fluency across two frameworks:

  • Anthropic's 4D AI Fluency Framework — Delegation, Description, Discernment, Diligence
  • Yegge's 8-Stage Developer-Agent Evolution Model — Stage 1 (zero AI usage) through Stage 8 (own orchestrator)

Tracks progress over time with structured JSON history and generates dated .docx reports.

Installation

Copy the full skill directory into your Claude Code skills folder — the skill needs its companion files (REFERENCE.md, templates/, assessments/), so copying SKILL.md alone will not work:

# macOS/Linux
cp -r . ~/.claude/skills/ai-fluency-assessment/

# Windows
xcopy /E /I . %USERPROFILE%\.claude\skills\ai-fluency-assessment\

Install the Python dependency for .docx report generation:

pip install python-docx

Usage

Ask Claude Code to run your AI fluency assessment (e.g. "assess my AI fluency" or "run a fluency check") — the skill is picked up by description match. The flags below can be given in the same request (e.g. "run a quick fluency assessment").

CommandWhat it does
/assess-fluencyFull assessment: gather data, score frameworks, diff against last, generate report
/assess-fluency --quickOutput data-gathering prompts only — no scoring or report
/assess-fluency --compareDiff the two most recent assessments without new data

What the Assessment Covers

The skill guides you through three data-gathering prompts (run in separate sessions):

  • Prompt A — Claude Code self-assessment: projects, autonomy levels, automations, Yegge stage estimate
  • Prompt B — Other AI tools: automations, integrations, and AI-generated artifacts (ChatGPT, Copilot, Gemini, n8n, Zapier, etc.)
  • Checklist C — Claude.ai manual notes: delegation patterns, error-catching, format specifications

Then scores each 4D competency (Developing → Practitioner → Advanced → Expert → Exceptional), assigns a Yegge stage, reviews development edges from previous assessments, and generates a .docx report.

File Structure

ai-fluency-assessment/
├── SKILL.md                        # Main skill definition (copy to ~/.claude/commands/)
├── REFERENCE.md                    # Framework definitions, rubrics, stage descriptions
├── README.md                       # This file
├── assessments/                    # Your assessment history (gitignored — stays local)
│   └── YYYY-MM-DD.json             # One file per assessment run
├── templates/
│   └── generate_report.py          # Generates .docx report from assessment JSON
└── example/
    └── example-assessment.json     # Anonymized example showing the JSON schema

Assessment History

Each assessment is saved as assessments/YYYY-MM-DD.json. This folder is gitignored in the public repo — your assessment data stays local. The --compare mode reads the two most recent files in this folder to produce a progress diff.

See example/example-assessment.json for the full schema with sample data.

Frameworks

Anthropic 4D AI Fluency Framework

DimensionWhat it measures
DelegationWhat you hand off to AI vs. retain; task decomposition and scoping
DescriptionQuality of prompts; specificity, constraints, examples, format direction
DiscernmentCritical evaluation of outputs; error-catching, pushing back, not using blindly
DiligenceVerification habits; checking facts, testing outputs, reviewing for bias

Full behavioral indicator rubric: see REFERENCE.md.

Yegge's 8-Stage Developer-Agent Evolution Model

StageLabel
1Zero AI usage
2Occasional AI queries
3Regular copilot usage
4Context-aware prompting
5Agentic workflows
6Multi-agent coordination
7AI-augmented systems design
8Own orchestrator

Full stage definitions with observable behaviors: see REFERENCE.md.

License

MIT

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