Our review
This skill analyzes and improves existing Claude skills by researching current best practices and applying targeted enhancements.
Strengths
- Automates skill updates with current practices
- Uses web search and sub-agents for thorough analysis
- Produces a structured report with clear priorities
- Preserves original intent while optimizing clarity and efficiency
Limitations
- Requires web search access for up-to-date recommendations
- May not cover all specialized domains without external sources
- Depends on the quality of the skill's triggers and description
When you want to modernize or optimize an existing Claude skill based on latest best practices.
For a brand new skill or if you don't need external comparative analysis.
Security analysis
SafeThe skill uses standard, non-destructive tools (WebSearch, Read, Edit, Write, Glob, Task) to research and improve other skills. No commands that delete, exfiltrate, or disable safety are present.
No concerns found
Examples
Improve my Claude skill for web scraping by researching current best practices and updating the skill file accordingly.Audit my skill 'data-analyzer' for token efficiency and suggest improvements based on best practices.name: skill-improver description: Research and improve Claude skills with current best practices. Triggers on requests to improve skills, update skills, research best practices for skills, enhance skill quality, or modernize existing skills. allowed-tools: WebSearch, Read, Edit, Write, Glob, Task
Skill Improver
Research current best practices and improve existing Claude skills.
Process Overview
- Identify target skill and its domain
- Research current best practices (web search)
- Analyze existing skill against best practices
- Generate improvement recommendations
- Apply improvements (if requested)
Step 1: Identify Target
Locate the skill to improve:
# Find skill location
ls -la .claude/skills/<skill-name>/
Read SKILL.md and all references to understand current implementation.
Step 2: Research Best Practices
Use subagents (Task tool) to parallelize research across multiple topics:
Spawn parallel research agents for:
1. "<domain> best practices 2026"
2. "<domain> common mistakes to avoid"
3. "Claude AI <domain> techniques" (if applicable)
Each subagent should return:
- Key findings with sources
- Actionable recommendations
Focus areas:
- Industry standards and conventions
- Common pitfalls and how to avoid them
- Performance optimizations
- Security considerations (if relevant)
- Token efficiency for LLM skills
Step 3: Analyze Skill
Compare existing skill against:
| Aspect | Check | |--------|-------| | Clarity | Instructions unambiguous? | | Completeness | All use cases covered? | | Efficiency | Minimal tokens for max utility? | | Accuracy | Reflects current best practices? | | Triggers | Description covers all valid triggers? | | Structure | Follows skill-creator guidelines? |
Step 4: Generate Recommendations
Create improvement report:
## Skill Improvement Report: <skill-name>
### Summary
- Current state assessment
- Key findings from research
### Recommendations
#### High Priority
1. [Issue]: [Recommended fix]
#### Medium Priority
1. [Issue]: [Recommended fix]
#### Low Priority / Nice-to-Have
1. [Suggestion]
### Best Practices Found
- [Practice 1]: [Source]
- [Practice 2]: [Source]
Step 5: Apply Improvements
If user approves changes:
- Edit SKILL.md with improvements
- Update/add references if needed
- Update scripts if applicable
- Validate with
scripts/validate_skill.py
Guidelines
- Preserve intent - Improvements should enhance, not change skill purpose
- Cite sources - Link to best practice sources when recommending changes
- Prioritize impact - Focus on changes that meaningfully improve skill quality
- Maintain conciseness - Don't bloat skills with unnecessary content
- Test triggers - Ensure description still triggers appropriately after changes
See references/research-strategies.md for search query templates and source evaluation guidelines.
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