claude-skillkit

name

claude-skillkit

description

Professional skill creation with research-driven workflow and automated validation. USE WHEN: Creating new skills, validating existing skills, deciding between Skills vs Subagents, migrating documents to skills, or running individual validation tools. PRIMARY TRIGGERS: "create skill" = Full creation (12 steps with research + execution planning) "validate skill" = Validation workflow (steps 3-8) "Skills vs Subagents" = Decision workflow (step 0) "convert doc to skill" = Migration workflow "estimate tokens" = Token optimization "security scan" = Security audit WORKFLOW COMPLIANCE: Structured workflows with validation checkpoints. Research phase (Step 1c-1d) ensures skills based on proven approaches. DIFFERENTIATOR: Research-driven creation. Web search (3-5 queries) before building. Multi-proposal generation. 9 automation scripts. Quality 9.0+/10. REUSED: Anthropic's init_skill.py and package_skill.py (production-tested).

Section 1: Intent Detection & Routing

Detect user intent, route to appropriate workflow.

Intent	Keywords	Route To
Full creation	"create", "build", "new skill"	Section 2
Validation	"validate", "check quality"	Section 3
Decision	"Skills vs Subagents", "decide"	Section 4
Migration	"convert", "migrate doc"	Section 5
Single tool	"validate only", "estimate tokens", "scan"	Section 6

PROCEED to corresponding section after intent detection.

Workflow Value: Research-driven approach validates design before building. Sequential steps with checkpoints produce 9.0/10+ quality vs ad-hoc creation.

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Section 2: Full Creation Workflow (Overview)

Prerequisites: Skill description provided, workspace available Quality Target: >=7.5/10 (Good), >=8.0/10 (Excellent) - See v1.2.1 quality improvements Time: <10 min with automation

12-Step Process with Validation Gates:

STEP 0: Decide Approach

• Tool: decision_helper.py
• Decides: Skills vs Subagents
• Gate: Proceed only if "Skills" recommended

STEP 1: Understand & Research

• 1a. Gather requirements
• 1b. Identify knowledge gaps
• 1c. Research domain (Verbalized Sampling: 3-4 web searches with diverse angles)
• 1d. Generate proposals (3-5 options evaluated with multi-criteria scoring)
• 1e. User validates and approves approach
• 1f. Execution planning: P0/P1/P2 prioritization with token budgets assigned
  • See: references/section-2-full-creation-workflow.md (Step 1f details)

STEP 2: Initialize & Create Content

• Tool: python scripts/init_skill.py skill-name --path /path (Anthropic)
• Alternative: migration_helper.py (if converting from document)
• 2.5 Checkpoint: Sequential creation (P0→P1→P2), token budget monitoring
• 2.8 Verification: P0/P1/P2 completion validation before proceeding
  • See: references/section-2-full-creation-workflow.md (Steps 2.5 & 2.8 details)

STEP 3: Validate Structure

• Tool: validate_skill.py
• Gate: Fix critical issues before proceeding

STEP 4: Security Audit

• Tool: security_scanner.py
• Gate: Fix critical vulnerabilities immediately

STEP 5: Token Optimization

• Tool: token_estimator.py
• Gate: Optimize if >5000 tokens

STEP 6: Progressive Disclosure

• Tool: split_skill.py
• Gate: Split if SKILL.md >350 lines

STEP 7: Generate Tests

• Tool: test_generator.py
• Creates: Automated validation tests

STEP 8: Quality Assessment (v1.2.1 Enhanced)

• Tool: quality_scorer.py
• Gate: Must achieve >=7.5/10 before packaging
• v1.2.1 Improvements:
  • Imperative detection: 11x more accurate (3.33% → 37.50%)
  • Better YAML frontmatter handling
  • Improved markdown formatting detection

Note: Quality scorer now more accurately detects imperative voice in descriptions. Target 70-79% (Grade C) is acceptable, 80-89% (Grade B) is good, 90%+ (Grade A) is excellent.

STEP 9: Package for Deployment (v1.2.1 Enhanced)

• Tool: python scripts/package_skill.py skill-name/
• Options: --strict flag for production deployments
• v1.2.1 Fixes:
  • Fixed output directory handling
  • Fixed archive structure organization
  • Enhanced pre-packaging validation
• Creates: .skill file ready to deploy

For detailed implementation: See references/section-2-full-creation-workflow.md

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Section 3: Validation Workflow (Overview)

Use when: Validating existing skill

Steps: Execute validation subset (Steps 3-8)

1. Structure validation (validate_skill.py)
2. Security audit (security_scanner.py)
3. Token analysis (token_estimator.py)
4. Progressive disclosure check
5. Test generation (optional)
6. Quality assessment (quality_scorer.py)

For detailed workflow: See references/section-3-validation-workflow-existing-skill.md

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Section 4: Decision Workflow (Overview)

Use when: Uncertain if Skills is right approach

Process:

1. Run decision_helper.py
2. Answer interactive questions
3. Receive recommendation with confidence score
4. Proceed if Skills recommended (confidence >=75%)

For detailed workflow: See references/section-4-decision-workflow-skills-vs-subagents.md

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Section 5: Migration Workflow (Overview)

Use when: Converting document to skill

Process:

1. Decision check (Step 0)
2. Migration analysis (migration_helper.py)
3. Structure creation
4. Execute validation steps (3-8)
5. Package (Step 9)

For detailed workflow: See references/section-5-migration-workflow-doc-to-skill.md

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Section 6: Individual Tool Usage

Use when: User needs single tool, not full workflow

Entry Point: User asks for specific tool like "estimate tokens" or "security scan"

Available Tools

Validation Tool:

python scripts/validate_skill.py skill-name/ --format json

Guide: knowledge/tools/14-validation-tools-guide.md

Token Estimator:

python scripts/token_estimator.py skill-name/ --format json

Guide: knowledge/tools/15-cost-tools-guide.md

Security Scanner:

python scripts/security_scanner.py skill-name/ --format json

Guide: knowledge/tools/16-security-tools-guide.md

Pattern Detector:

# Analysis mode with JSON output
python scripts/pattern_detector.py "convert PDF to Word" --format json

# List all patterns
python scripts/pattern_detector.py --list --format json

# Interactive mode (text only)
python scripts/pattern_detector.py --interactive

Guide: knowledge/tools/17-pattern-tools-guide.md

Decision Helper:

# Analyze use case (JSON output - agent-layer default)
python scripts/decision_helper.py --analyze "code review with validation"

# Show decision criteria (JSON output)
python scripts/decision_helper.py --show-criteria --format json

# Text mode for human reading (debugging)
python scripts/decision_helper.py --analyze "description" --format text

v1.2.1 Bug Fix:

• Fixed confidence calculation bug for Subagent recommendations
• Before: Score -3 showed 82% confidence (should be 75%)
• Before: Score -5 showed 75% confidence (should be 85%)
• Fixed: Confidence now correctly increases with stronger scores
• Formula changed: (abs(score) - 3) and (abs(score) - 6) for proper scaling

Guide: knowledge/tools/18-decision-helper-guide.md

Test Generator (v1.2: Parameter update):

python scripts/test_generator.py skill-name/ --test-format pytest --format json

• --test-format: Test framework (pytest/unittest/plain, default: pytest)
• --format: Output style (text/json, default: text)
• Backward compatible: Old --output parameter still works (deprecated)

Guide: knowledge/tools/19-test-generator-guide.md

Split Skill:

python scripts/split_skill.py skill-name/ --format json

Guide: knowledge/tools/20-split-skill-guide.md

Quality Scorer (v1.2.1 Enhanced):

python scripts/quality_scorer.py skill-name/ --format json

v1.2.1 Improvements:

• Imperative voice detection improved 11x (3.33% → 37.50%)
• Fixed: YAML frontmatter now stripped before analysis
• Fixed: Markdown formatting (bold, italic, code, links) properly removed
• Improved: First 3 words checked instead of only first word
• Threshold lowered: 70% → 50% for full points (30% for partial)

Example Impact:

• Before: readme-expert.skill = 78/100 (Grade C)
• After: readme-expert.skill = 81/100 (Grade B)

Guide: knowledge/tools/21-quality-scorer-guide.md

Migration Helper:

python scripts/migration_helper.py doc.md --format json

Guide: knowledge/tools/22-migration-helper-guide.md

Tool Output Standardization (v1.0.1+)

All 9 tools now support --format json parameter:

• ✅ Consistent JSON schema across all automation tools
• ✅ Parseable with python -m json.tool for validation
• ✅ Backward compatible - text mode still available as default (or via --format text)
• ✅ Agent-layer tools (decision_helper) default to JSON for automation

JSON Output Structure (Standardized):

{
  "status": "success" | "error",
  "tool": "tool_name",
  "timestamp": "ISO-8601",
  "data": { /* tool-specific results */ }
}

Quality Assurance Enhancements (v1.2+)

File & Reference Validation:

• validate_skill.py now comprehensively checks file references (markdown links, code refs, path patterns)
• package_skill.py validates references before packaging, detects orphaned files
• Prevents broken references and incomplete files in deployed skills

Content Budget Enforcement (v1.2+):

• Hard limits on file size: P0 ≤150 lines, P1 ≤100 lines, P2 ≤60 lines
• Real-time token counting with progress indicators
• Prevents file bloat that previously caused 4-9x target overruns

Execution Planning (v1.2+):

• P0/P1/P2 prioritization prevents over-scoping
• Token budget allocated per file to maintain efficiency
• Research phase respects Verbalized Sampling probability thresholds (p>0.10)

Quality Scorer Context (v1.2.1 Updated):

• Scoring Calibration: General skill quality heuristics
  • 70-79% (Grade C): Acceptable quality
  • 80-89% (Grade B): Good quality
  • 90-100% (Grade A): Excellent quality
• v1.2.1 Improvements:
  • Imperative detection 11x more accurate
  • Better handling of YAML frontmatter and markdown formatting
  • Realistic thresholds: 50% for full points (down from 70%)
• Usage Note: Style scoring may not fit all skill types (educational vs technical)
• Recommendation: Use as guidance, supplement with manual review for edge cases

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Section 7: Knowledge Reference Map (Overview)

Strategic context loaded on-demand.

Foundation Concepts (Files 01-08):

• Why Skills exist vs alternatives
• Skills vs Subagents decision framework
• Token economics and efficiency
• Platform constraints and security
• When NOT to use Skills

Application Knowledge (Files 09-13):

• Real-world case studies (Rakuten, Box, Notion)
• Technical architecture patterns
• Adoption and testing strategies
• Competitive landscape analysis

Tool Guides (Files 14-22):

• One guide per automation script
• Usage patterns and parameters
• JSON output formats
• Integration examples

For complete reference map: See references/section-7-knowledge-reference-map.md

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Workflow Compliance Reinforcement

This skill works best when workflows are followed sequentially.

Why compliance matters:

1. Research validation reduces iteration (validate before build)
2. Security checks prevent vulnerabilities (catch issues early)
3. Token optimization ensures efficiency (avoid bloat)
4. Quality gates maintain standards (9.0/10+ target)

Mechanisms encouraging compliance:

• Frontmatter priming: "WORKFLOW COMPLIANCE" statement
• Section routing: Explicit "PROCEED to Section X"
• Validation gates: IF/THEN with checkpoints
• Quality target: ">=9.0/10 requires following workflow"

Flexible when needed:

• Single tool usage (Section 6) skips full workflow
• Validation-only (Section 3) runs subset of steps
• User can request deviations with justification

Goal: Strong encouragement through design, not strict enforcement.

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Additional Resources

Detailed implementations available in references/ directory:

All section overviews above link to detailed reference files for deep-dive information. Load references on-demand when detailed implementation guidance needed.