smithery.ai

cc-explorer

Comprehensive Claude Code documentation researcher for brainstorming and project planning.

First seen Apr 9, 2026

Installation

$ npx skills add https://smithery.ai

Summary

  • Comprehensive Claude Code documentation researcher for brainstorming and project planning.
  • Use when the user asks about Claude Code capabilities, how to set up a project, what features are available, or needs to explore what's possible with Claude Code.
  • Triggers on phrases like "what can Claude Code do", "how should I set up", "what are my options", "brainstorm", "explore possibilities", "what features", "best way to structure".

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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.

Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,455 B
  • docs SUMMARY.md 451 B

History

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

SKILL.md

Claude Code Explorer Skill

This skill triggers automatic research when Claude Code questions are detected.

When This Skill Activates

This skill should activate when the user asks exploratory questions about Claude Code, such as:

  • "What can Claude Code do for..."
  • "How should I set up..."
  • "What are my options for..."
  • "Best way to structure..."
  • "What features are available..."

Action

When this skill is triggered, use the Task tool to spawn the cc-explorer agent:

Task(
  subagent_type="cc-explorer",
  prompt="Research Claude Code documentation for: [user's question]",
  description="Research Claude Code docs"
)

The cc-explorer agent will:

  1. Fetch the complete documentation index from code.claude.com/docs/llms.txt
  2. Read the changelog for latest features
  3. Systematically fetch all relevant documentation pages
  4. Synthesize comprehensive, well-researched advice

Output

Return the agent's comprehensive research results to the user.