jamie-bitflight/optimize-claude-md

Optimize AI-Facing Files

Orchestrate multi-phase optimization of AI-facing documentation with measurement, delegation, verification, and comprehensive reporting.

Invocation

/optimize-claude-md <path>

Where <path> is one of:

• A single file (e.g., ./CLAUDE.md, .claude/skills/my-skill/SKILL.md, .claude/agents/my-agent.md)
• A skill directory (e.g., .claude/skills/my-skill/) — optimizes SKILL.md and all reference files
• A plugin directory (e.g., plugins/my-plugin/) — optimizes CLAUDE.md, all skills, and all agents

Process

<user_provided_target>$ARGUMENTS</user_provided_target> <PWD> !pwd </PWD>

Phase 1: Validate Target

If <user_provided_target> is empty, ask the user for a target path before proceeding.

If <user_provided_target> is not an absolute path, prepend the value of <PWD> to produce the absolute path. Use that absolute path for all subsequent operations.

Verify the resolved absolute path exists. Determine scope (single file, skill directory, or plugin directory).

Phase 2: Measure Baseline

For all files:

• Determine file type (CLAUDE.md, SKILL.md, agent definition, reference file)
• Measure token count: uvx skilllint@latest check --check <file>
• Record baseline token count

For SKILL.md files only:

• Run completeness score evaluation (8-category assessment from /plugin-creator:audit-skill-completeness)
• Record baseline completeness score (format: X/24)

Record metrics for reporting.

Phase 3: Delegate to @contextual-ai-documentation-optimizer

Spawn the optimization agent via Agent tool with enhanced delegation template (see below). Pass file-type-specific context, baseline metrics, and constraints.

<delegation_template>

TARGET: {resolved path(s)}
FILE TYPE: {CLAUDE.md | SKILL.md | agent definition | reference file}
BASELINE TOKEN COUNT: {N tokens}
BASELINE COMPLETENESS SCORE: {X/24} (SKILL.md only)

TASK:
1. Run RT-ICA pre-check — verify file type, intent, audience, constraints
2. Enable the prompt-optimization skill
3. Read the complete target file(s)
4. Analyze against the 8 optimization principles:
   - Positive framing (replace prohibitions with directives)
   - Motivation (explain why rules exist)
   - Concrete examples (show correct and incorrect patterns)
   - Front-loaded priorities (critical info first)
   - Concise language (economy without ambiguity)
   - Explicit format control (structure instructions clearly)
   - Strategic XML tagging (semantic boundaries for complex prompts)
   - Structural enforcement (decision flows, tables, checklists for determinism)
5. Apply transformations — preserve original intent, improve execution economy
6. Run CoVe post-check — generate falsifiable verification questions, answer independently
7. Report token impact for each transformation
8. Signal completion status: DONE or BLOCKED

CONSTRAINTS:
- Preserve all original intent and functional behavior
- Maintain file structure conventions (frontmatter format, heading hierarchy)
- Apply compression only where it improves clarity — brevity is not the sole goal
- Verify technical terms are exact (tool names, file paths, command syntax)
- Report token impact for each transformation
- For SKILL.md: evaluate against 8 completeness categories, keep description <1024 chars, no YAML multiline indicators
- For agent files: preserve required frontmatter fields (name, description)
- For CLAUDE.md: front-load critical instructions, use decision flow diagrams for complex logic
- For CLAUDE.md: read `.claude/skills/optimize-claude-md/references/claude-rules-extraction.md` before analyzing; perform rules extraction phase after optimization analysis, before CoVe
- Signal DONE when optimization complete, BLOCKED when missing required inputs

OUTPUT STRUCTURE:
- RT-ICA Pre-Check Results
- Analysis of Optimization Opportunities
- Optimized Content (complete file)
- Changes Applied with Principle Citations
- Token Impact Per Transformation
- CoVe Verification Results
- Status: DONE or BLOCKED (with blocking reason if BLOCKED)

</delegation_template>

Routing within contextual-ai-documentation-optimizer:

• Optimize existing content (improve clarity, fix structure, apply Anthropic prompt engineering principles) → plugin-creator:contextual-ai-documentation-optimizer (this skill uses this path)
• Audit quality (read-only, no writes, score against completeness categories) → /plugin-creator:audit-skill-completeness skill directly
• Sync content against upstream docs (add NEW/fix STALE from live sources) → general-purpose agent with drift report until skill-content-updater lands (backlog #1899)
• Write/rewrite description field only → /plugin-creator:write-frontmatter-description skill directly

Phase 4: Handle Agent Response

If agent signals BLOCKED:

• Present the blocking reason to the user
• Ask for resolution (missing inputs, clarifications, or constraints)
• Wait for user input
• Re-delegate with additional context once blocker is resolved

If agent signals DONE:

• Proceed to Phase 5 (Independent Verification)

Phase 5: Independent Verification

Spawn a SECOND agent (general-purpose, NOT the same agent that optimized) to verify optimization quality.

Verification Template:

ORIGINAL FILE: {path to original}
OPTIMIZED FILE: {path to optimized version}

TASK:
Compare the original and optimized files. Verify:

1. Original intent preserved — no functional behaviors lost
2. Technical terms exact — tool names, file paths, command syntax unchanged
3. Structural conventions maintained — frontmatter format, heading hierarchy intact
4. No regressions introduced — edge cases still handled, constraints still enforced

CONSTRAINTS:
- You have NO context from the optimization process
- Base verification ONLY on comparing the two files
- Report any regressions, ambiguities, or losses of specificity
- Signal PASS if optimization preserves all original intent
- Signal REGRESSION if any functional behavior was lost or technical terms changed incorrectly

OUTPUT:
- Verification Status: PASS or REGRESSION
- Regressions Found (if any) with line number references
- Preserved Behaviors (summary)

Handle verification result:

• If PASS: proceed to Phase 6
• If REGRESSION: present regression details to user, offer to revise or keep original

Phase 6: Measure Output

For all files:

• Measure post-optimization token count using same tool
• Calculate delta: (post - baseline) / baseline * 100

For SKILL.md files only:

• Run post-optimization completeness score
• Calculate delta: post - baseline (absolute change)

Record metrics for reporting.

Phase 7: Present Comprehensive Report

Report to user with structure:

## Optimization Report: {filename}

### Baseline Metrics
- Token Count: {N tokens}
- Completeness Score: {X/24} (SKILL.md only)

### Post-Optimization Metrics
- Token Count: {M tokens} ({+/-Y%})
- Completeness Score: {Z/24} (delta: {+/-D}) (SKILL.md only)

### Changes Applied
{List of transformations with principle citations from agent report}

### CoVe Verification Results
{Agent's falsifiable verification questions and answers}

### Independent Verification
- Status: {PASS | REGRESSION}
- {Regression details if any}

### Structural Upgrade Candidates
{Sections that could benefit from decision flows, tables, checklists}

### Before/After Diff
{Diff output showing exact changes}

### Recommendation
{Proceed with optimization | Revise based on regressions | Keep original}

Phase 8: Apply on Approval

Write optimized content ONLY after user confirms. Do not auto-apply.

Iterative Mode for Large Targets

For files exceeding TOKEN_WARNING_THRESHOLD (defined in skilllint) or plugin directories, offer iterative optimization:

Pass 1: Structural Changes

• Reorganize sections for front-loaded priorities
• Split large sections to references/ subdirectory
• Add decision flow diagrams, tables, checklists
• Measure token count after structural changes

Pass 2: Content Optimization

• Apply positive framing (replace prohibitions with directives)
• Add motivations and concrete examples
• Compress verbose explanations without losing clarity
• Measure token count after content changes

Pass 3: Polish

• Optimize frontmatter (description compression, argument hints)
• Verify cross-references between files
• Ensure format consistency (code fence language specifiers, markdown links)
• Final measurement

Convergence: Terminate when completeness score stops improving between passes (delta <1 point) or token reduction plateaus (delta <2%).

Scope Expansion Rules

When target is a skill directory:

1. Optimize SKILL.md (primary)
2. Optimize each file in references/ (secondary)
3. Verify cross-references between SKILL.md and reference files remain valid

When target is a plugin directory:

1. Optimize CLAUDE.md if present (primary)
2. List all skills and agents — ask user which to include
3. Apply iterative mode: one pass per selected component
4. Verify plugin.json references remain consistent

Edge Cases

• File not found: Report exact path checked, ask user to confirm
• Binary or non-markdown file: Skip with explanation
• Already optimal: Acknowledge effectiveness, suggest only minor refinements per agent constraint
• Large file (exceeds TOKEN_WARNING_THRESHOLD): Offer iterative mode with multi-pass optimization
• Agent returns BLOCKED: Present blocking reason to user with specific questions
• Independent verification finds regression: Report regression, offer to revise or keep original
• Token count increases: Report reason (added examples, motivations, or structure), verify completeness score improved to justify expansion
• Completeness score decreases: Signal regression, recommend keeping original or revising optimization strategy