Optimize Project Skill

User-invocable workflow to audit, fix, and bootstrap token-efficient project instructions.

Modes

• --audit: analysis only, no file changes
• --fix: apply safe auto-fixes after audit
• --setup: bootstrap optimize-project baseline for new repositories

When to Use

• Existing repository has verbose instruction/docs churn
• CLAUDE.md and agent files have drift or redundancy
• Comments are consuming too many tokens
• New repository needs compact defaults from day one

Workflow

Step 1: Detect project type and language

Collect project signals from root:

• Node/TS: package.json, tsconfig.json
• Python: pyproject.toml, requirements.txt
• Go: go.mod
• Rust: Cargo.toml
• Bash-heavy: scripts/*.sh, hooks, no language manifest

Report detected stack, dominant language, test command, and docs/instruction locations.

Step 2: Run project audit script

Run the standard audit first (machine-first, JSON output):

${CLAUDE_HOME:-.claude}/scripts/project-audit.sh --project-root . --json

Use this output as baseline counts for debt, risk, and comment-density flags.

Step 3: AI analysis (token efficiency + quality)

Analyze audit output plus repository files for:

1. CLAUDE.md quality
   • conflicting rules
   • duplicated sections
   • verbose prose where compact tables/checklists are better
2. Agent file redundancy
   • duplicated guidance across AGENTS.md, CLAUDE.md, skill files
   • repeated workflow text better centralized in one reference file
3. Comment token waste
   • comments that restate obvious code behavior
   • long explanatory blocks with no decision context
   • stale comments that no longer match code

Comment-density severity:

• P2 if comment density is > 10%
• P1 if comment density is > 20%

Step 4: Generate optimization report

Return a structured report:

## Optimize Project Report: {project}

### Mode
{--audit|--fix|--setup}

### Detection
- Type: {type}
- Language: {language}
- Framework: {framework}

### Audit Summary
| Severity | Count |
|----------|-------|
| P1       | {n}   |
| P2       | {n}   |
| P3       | {n}   |

### AI Findings
| # | Severity | Area | File | Issue | Suggested Action |
|---|----------|------|------|-------|------------------|

### Token Savings Estimate
- Estimated current instruction/comment tokens: {current}
- Estimated optimized tokens: {optimized}
- Estimated savings: {saved} ({percent}%)

Savings estimate should separate:

• instruction/documentation compaction
• redundant agent content removal
• comment cleanup impact

Step 5: Apply safe auto-fixes (--fix mode)

Only safe, low-risk fixes are allowed automatically:

1. .gitignore hygiene:
   • add missing common local/runtime artifacts
   • avoid removing existing ignore patterns without evidence
2. Comment cleanup:
   • remove trivial comments that restate obvious code
   • keep comments that explain why, invariants, or non-obvious constraints
   • never change executable logic during cleanup

After fixes, rerun:

${CLAUDE_HOME:-.claude}/scripts/project-audit.sh --project-root . --json

Report before/after deltas and updated token-savings estimate.

Step 6: Setup mode for new projects (--setup)

Bootstrap optimize-project baseline:

1. Detect stack and choose matching minimal templates
2. Create compact CLAUDE.md baseline with no duplicated policy blocks
3. Add starter AGENTS.md + skill references with clear ownership boundaries
4. Add/update .gitignore with language-appropriate defaults
5. Run project-audit.sh --json and return initial health report

--setup must prefer concise templates and avoid adding verbose boilerplate.

Guardrails

• --audit never writes files
• --fix writes only safe auto-fixes listed above
• --setup creates baseline files but does not overwrite user-authored docs without explicit diff review
• All outputs must be deterministic and machine-readable where possible

References

• Pattern baseline: ${CLAUDE_HOME:-.claude}/skills/review-pr/SKILL.md
• Audit script: ${CLAUDE_HOME:-.claude}/scripts/project-audit.sh
• Hardening context: ${CLAUDE_HOME:-.claude}/skills/hardening/SKILL.md