smithery.ai

skill-improver

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.

First seen Apr 6, 2026

Installation

$ npx skills add https://smithery.ai

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Also in this package

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npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Allowed toolsWebSearch, Read, Edit, Write, Glob, Task
Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,956 B
  • docs SUMMARY.md 229 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 3 installs

SKILL.md

Skill Improver

Research current best practices and improve existing Claude skills.

Process Overview

  1. Identify target skill and its domain
  2. Research current best practices (web search)
  3. Analyze existing skill against best practices
  4. Generate improvement recommendations
  5. 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:

  1. Edit SKILL.md with improvements
  2. Update/add references if needed
  3. Update scripts if applicable
  4. 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.