assess-team-ai-adoption

name	assess-team-ai-adoption
description	assess AI tool adoption and usage patterns in the team

Soul

Analyze AI tool adoption patterns — who's using AI assistants, how effectively, quality of AI-generated code.

Critical: Permission Gate Restrictions

DO NOT USE these bash patterns:

• find ... -exec ...
• xargs ...
• Complex shell pipelines

AI Usage Signal Detection

Look for these patterns in code and commits:

Code Patterns (indicators of AI generation)

• Consistent formatting across unrelated files (auto-format)
• Similar variable names in boilerplate code
• Structured comments: // TODO:, // Generated by, // AI-Generated
• Uniform indentation in large files (no organic variation)
• Similar abstraction patterns across different modules
• Boilerplate density: repeated template code

Commit Patterns (indicators of AI usage)

• [pi] or [ai] markers in commit messages
• Very short time between commits on different files (< 5 min)
• Large PRs with consistent style
• Commit messages with "generated", "created", "added tests"

Time-Based Patterns

• Rapid file creation/change cycles
• Multiple files touched in quick succession
• Batching of similar changes

Analysis Protocol

Phase 1: Code Style Analysis

Search for AI indicators:

search pattern: "// TODO:|# TODO:|// Generated"
search pattern: "// AI|// Artificial"

Check for formatting consistency:

search pattern: "const\\s+\\w+\\s*="
search pattern: "function\\s+\\w+\\s*\\("

Phase 2: Commit Pattern Analysis

Use bash git commands (simple patterns):

git log --format="%ae|%s" -n 200 | grep -i "ai\|pi\|generated\|auto"
git shortlog -sn | head -10

Phase 3: AI Effectiveness Assessment

Evaluate:

• Code quality consistency
• Boilerplate vs organic code ratio
• Test coverage in AI-assisted files

Output Format

Save to: docs/assessment/team-<YYYY-MM-DD>-ai-adoption.md

# Team Assessment: AI Adoption

## AI Adoption by Author
| Author | AI Signals | Code Quality | Effectiveness |
|--------|------------|--------------|--------------|
| ... | Low/Med/High | A-F | A-F |

## AI Signal Examples
| Pattern | Files | Author | Interpretation |
|---------|-------|--------|----------------|
| ... | N | ... | ... |

## Adoption Distribution
[Analysis of how AI tools are being used]

## Key Findings
1. [Finding with evidence] [confidence: HIGH/MEDIUM/LOW]
2. [Finding with evidence] [confidence: HIGH/MEDIUM/LOW]

## AI Tool Integration
- CI/CD AI checks: Yes/No
- Code review AI tools: ...
- AI attribution policy: Yes/No

## Score: A-F (one line rationale)

## Recommendations
1. [Priority]
2. [Secondary]

Return Summary

Return max 5 lines:

• AI adoption rate
• Top AI user
• Score: A-F with one-word rationale
• One recommendation