agentic/code/addons/skill-factory/skills/skill-enhancer/SKILL.md
AI-powered enhancement of skill SKILL.md files. Use to transform basic templates into comprehensive, high-quality skill documentation.
npx skillsauth add jmagly/aiwg skill-enhancerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Single responsibility: Enhance basic SKILL.md files using AI to extract better examples, create comprehensive guides, and improve overall skill quality. (BP-4)
Before executing, VERIFY:
DO NOT enhance without backing up original content.
ASK USER instead of guessing when:
NEVER hallucinate content not present in references.
| Context Type | Included | Excluded | |--------------|----------|----------| | RELEVANT | SKILL.md, references/, skill purpose | Other skills | | PERIPHERAL | Similar high-quality skills as examples | Unrelated documentation | | DISTRACTOR | Source scraping details | Enhancement history |
| Mode | Method | Cost | Quality | |------|--------|------|---------| | Local | Claude Code Max plan | Free | 9/10 | | API | Anthropic API | ~$0.05-0.20 | 9/10 | | Manual | Guided template | Free | Variable |
# Create backup
cp output/<skill-name>/SKILL.md output/<skill-name>/SKILL.md.backup
# Record backup timestamp
echo "Backup created: $(date)" > output/<skill-name>/.enhancement_backup
# Check current SKILL.md quality
wc -l output/<skill-name>/SKILL.md
grep -c '```' output/<skill-name>/SKILL.md # Code examples
grep -c '^## ' output/<skill-name>/SKILL.md # Sections
# List reference files
ls -la output/<skill-name>/references/
# Sample reference content
head -50 output/<skill-name>/references/*.md
Option A: Local Enhancement (Recommended)
Using Claude Code Max (no API costs):
# With skill-seekers
skill-seekers enhance output/<skill-name>/ --local
# This opens Claude Code in new terminal
# AI analyzes references and enhances SKILL.md
# Takes 30-60 seconds
Option B: API Enhancement
# Set API key
export ANTHROPIC_API_KEY=sk-ant-...
# Run API enhancement
skill-seekers enhance output/<skill-name>/ --api
Option C: Manual Enhancement Template
Use this prompt with any AI:
Analyze the reference documentation in the following files:
[list reference files]
Based on this documentation, enhance the SKILL.md to include:
1. 5-10 practical code examples (extracted from references)
2. Comprehensive quick reference section
3. Key concepts explanation
4. Common patterns and best practices
5. FAQ based on common questions in docs
Keep all content grounded in the actual documentation.
Do not invent features not present in references.
# Compare before/after
wc -l output/<skill-name>/SKILL.md.backup output/<skill-name>/SKILL.md
# Check for code examples
grep -c '```' output/<skill-name>/SKILL.md
# Verify no hallucinations (spot check)
# Examples in SKILL.md should reference content in references/
# Show diff
diff output/<skill-name>/SKILL.md.backup output/<skill-name>/SKILL.md | head -100
# Or visual diff if available
code --diff output/<skill-name>/SKILL.md.backup output/<skill-name>/SKILL.md
On error:
API error → Switch to local modeLow quality output → Restore backup, try different approachTimeout → Retry with smaller reference setHallucination detected → Restore backup, manual enhancementRollback command:
mv output/<skill-name>/SKILL.md.backup output/<skill-name>/SKILL.md
State saved to: .aiwg/working/checkpoints/skill-enhancer/
checkpoints/skill-enhancer/
├── original_skill_md.md # Backup of original
├── reference_analysis.json # What was analyzed
├── enhancement_config.json # Settings used
└── enhancement_log.txt # Process log
| Metric | Before | Target After | |--------|--------|--------------| | SKILL.md lines | ~75 | ~300-500 | | Code examples | 0-2 | 5-10 | | Sections | 3-5 | 8-12 | | Quick reference | None | Comprehensive | | FAQ | None | 5-10 questions |
After enhancement, verify:
# Spot-check example against references
example=$(grep -A 5 '```' output/<skill-name>/SKILL.md | head -6)
grep -r "$example" output/<skill-name>/references/
# Should find matching content
{
"skill_dir": "output/myskill/",
"mode": "local",
"options": {
"max_examples": 10,
"include_faq": true,
"include_quick_ref": true,
"preserve_sections": ["Description", "Key Features"],
"enhance_sections": ["Code Examples", "Quick Reference", "FAQ"]
},
"quality": {
"min_examples": 5,
"min_lines": 200,
"require_navigation": true
}
}
Find code blocks in references, select most practical ones.
Identify question-like content, common patterns, edge cases.
Extract key APIs, common parameters, return types.
Identify recommendations, warnings, tips in documentation.
| Issue | Diagnosis | Solution | |-------|-----------|----------| | No improvement | References too sparse | Add more reference content first | | Hallucinations | AI invented features | Restore backup, use stricter prompt | | Timeout | References too large | Enhance in sections | | Quality low | Poor reference quality | Clean references first |
data-ai
Report which research-corpus radar sidecars are overdue for refresh. Computes staleness (days since last refresh vs the cadence window) for every radar, sorted most-overdue-first. Runs via `aiwg corpus radar-status`.
data-ai
Aggregate research-corpus radar sidecars into a corpus or per-cluster freshness report — totals, overdue count, per-cluster / per-GRADE / per-trajectory breakdowns, an overdue table, and per-radar rationale snippets. Runs via `aiwg corpus radar-report`.
testing
Scaffold radar/freshness sidecars for research-corpus REFs. Pulls title/authors from the citation sidecar and GRADE from the analysis doc, defaults the refresh cadence from GRADE and the cluster from a corpus-local map, and stamps documentation/radar/REF-XXX-radar.md. Runs via `aiwg corpus radar-init`.
data-ai
Compute an entity's publication trajectory — per-year paper counts, topic drift, hot-streak detection (≥3 consecutive A-grade years), and career phase. Runs via `aiwg corpus profile-temporal`.