techwavedev SkillCheck
Overview
Validate SKILL.md files against the agentskills specification and Anthropic best practices. Catches structural errors, semantic contradictions, naming anti-patterns, and quality gaps in a single read-only pass.
When to Use This Skill
- Use when user says "check skill", "skillcheck", or "validate SKILL.md"
- Use when reviewing a skill before publishing to a marketplace
- Use when debugging why a skill doesn't trigger correctly
- Use when onboarding a team to skill authoring standards
- Do NOT use for anti-slop detection, security scanning, or token analysis; use SkillCheck Pro for those
How It Works
Step 1: Parse
Read the target SKILL.md file and extract YAML frontmatter.
Step 2: Validate
Apply all Free tier checks in order:
| Category | Checks | What it catches |
|---|---|---|
| Structure (1.x) | Name format, description WHAT+WHEN, allowed-tools, categories, XML injection | Malformed frontmatter, missing fields |
| Body (2.x) | Line count, hardcoded paths, stale dates, empty sections, deprecated syntax, MCP tool qualification | Content quality issues |
| Naming (3.x) | Vague terms, single-word names, gerund suggestions | Poor discoverability |
| Semantic (4.x) | Contradictions, ambiguous terms, missing output format, wisdom/platitudes, misplaced triggers | Logical inconsistencies |
| Quality (8.x) | Examples, error handling, triggers, output format, prerequisites, negative triggers | Strengths (positive patterns) |
Step 3: Score
Calculate overall score (0-100). Penalties: critical = -20, warning = -5, suggestion = -1.
Step 4: Report
Return structured results: score, grade (Excellent/Good/Needs Work/Poor), issue list with check IDs, line numbers, messages, and fix suggestions.
Examples
Example 1: Validating a skill
User: check my skill at ~/.claude/skills/weekly-report/SKILL.md
SkillCheck output:
## weekly-report Check Results [FREE]
Score: 85/100 (Good)
### Warnings (2)
- 1.2-desc-when (line 3): Description missing WHEN clause
- 4.5-desc-no-triggers (line 3): Description lacks triggering conditions
### Suggestions (1)
- 3.4-gerund-naming (line 2): Skill name could use gerund form
### Passed Checks: 28
Example 2: Clean skill passes all checks
User: skillcheck ~/.claude/skills/processing-pdfs/SKILL.md
Score: 100/100 (Excellent)
All 31 checks passed. No issues found.
Limitations
- Read-only: does not modify any files
- Free tier covers structural, semantic, and naming checks only
- Anti-slop, security, WCAG, token, enterprise, and workflow checks require SkillCheck Pro
- Semantic checks (contradiction detection, wisdom/platitude) are heuristic with ~5% false positive rate
- Does not validate referenced files or scripts; only checks SKILL.md content
- Single-file validation; does not cross-check against other skills in the same directory
Best Practices
- Run SkillCheck before submitting skills to any marketplace
- Fix all critical and warning issues; suggestions are optional
- Use the check ID (e.g.,
1.2-desc-when) to find the exact rule in the skill body - Re-run after fixes to confirm the score improved
Common Pitfalls
Problem: Score seems low due to many suggestions Solution: Suggestions cap at -15 points total. Focus on warnings and criticals first.
Problem: False positive on ambiguous terms inside code blocks Solution: SkillCheck skips code blocks and inline code. If you still see false positives, wrap the term in backticks.
Problem: Wisdom/platitude check flags legitimate instructions Solution: Rephrase generic advice ("Remember that testing is important") as concrete directives ("Run tests before committing").
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Skill Check"
Storing Results
After completing work, store AI agent orchestration decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
--type decision --project <project> \
--tags skill-check ai-agents
Multi-Agent Collaboration
This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
--project <project>
Control Tower Integration
Register agents and tasks with the Control Tower (execution/control_tower.py) for centralized orchestration across machines and LLM providers.
Blockchain Identity
Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.