Claude-skill-registry cursor-explorer-mcp

Use for token-expensive operations requiring multi-file analysis - codebase exploration, broad searches, architecture understanding, tracing flows, finding implementations across files. Uses MCP cursor-agent server (company pays) with clean async interface. Do NOT use for single-file analysis, explaining code already in immediate context, or pure reasoning tasks.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/cursor-explorer-mcp" ~/.claude/skills/majiayu000-claude-skill-registry-cursor-explorer-mcp && rm -rf "$T"

manifest: skills/data/cursor-explorer-mcp/SKILL.md

Cursor Explorer (MCP)

Trigger immediately when you see:

"Find where X is..." → cursor-agent
"How does X work?" (multi-file) → cursor-agent
"Trace the flow of..." → cursor-agent
Manual approach needs 3+ file reads → cursor-agent

Skip for: single file, pure reasoning, code in context, 1-2 line answers

Workflow

# 1. Start query (batch multiple questions)
start = mcp__cursor_agent__cursor_agent_start({
  "query": "Find where X is. Give file:line, code snippets, purpose."
})
query_id = json.loads(start)["query_id"]

# 2. Get result (blocks until done)
result = mcp__cursor_agent__cursor_agent_result({
  "query_id": query_id,
  "wait": True  # Blocks automatically, no manual monitoring needed
})
output = json.loads(result)

# 3. If completed, present findings. If failed, fall back to Read/Grep.

Never retry on failure - just fall back to manual tools.

Query Tips

Request file:line refs
Ask for code snippets
Batch related questions
Be specific about format needed