Mmcp.market

leann-search skill

by parcadei·parcadei/Continuous-Claude-v3·3.9k stars·MIT

Semantic search across codebase using LEANN vector index

A100/100content scan

Is the leann-search skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.

No findings.

Install the leann-search skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git /tmp/Continuous-Claude-v3
mkdir -p ~/.claude/skills
cp -r /tmp/Continuous-Claude-v3/.claude/skills/archive/leann-search ~/.claude/skills/leann-search
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

LEANN Semantic Search

Use LEANN for meaning-based code search instead of grep.

When to Use

  • Conceptual queries: "how does authentication work", "where are errors handled"
  • Understanding patterns: "streaming implementation", "provider architecture"
  • Finding related code: code that's semantically similar but uses different terms

When NOT to Use

  • Exact matches: Use Grep for class Foo, def bar, specific identifiers
  • Regex patterns: Use Grep for error.handling, import.from
  • File paths: Use Glob for .test.ts, src//*.py

Commands

# Search the current project's index
leann search <index-name> "<query>" --top-k 5

# List available indexes
leann list

# Example
leann search rigg "how do providers handle streaming" --top-k 5

MCP Tool (in Claude Code)

leann_search(index_name="rigg", query="your semantic query", top_k=5)

Rebuilding the Index

When codebase changes significantly:

cd /path/to/project
leann build <project-name> --docs src tests scripts \
  --file-types '.ts,.py,.md,.json' \
  --no-recompute --no-compact \
  --embedding-mode sentence-transformers \
  --embedding-model all-MiniLM-L6-v2

How It Works

  1. LEANN uses sentence embeddings to understand meaning
  2. Searches find conceptually similar code, not just text matches
  3. Results ranked by semantic similarity score (0-1)

Grep vs LEANN Decision

More skills from parcadei/Continuous-Claude-v3

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