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search-mesh

SIMD-accelerated, single-pass codebase intelligence fabric for AI agents. Offload multi-keyword scans and AST context-pruning to native silicon.

Install / Use

claude mcp add jamestkelly -- npx -y github:jamestkelly/search-mesh

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

78/100

Supported Platforms

Claude Code
Claude Desktop

Our assessment of search-mesh

search-mesh scores 78/100 on our quality scale, 2266th of 3,478 Development & Engineering skills we index.

Its MCP Server is 5.7 KB long, well organised into 14 sections with 8 code examples: a solid amount of guidance for an agent.

It has 3 GitHub stars, so there is little community track record yet; judge it on its content.

Substance
26/30
Structure
20/20
Description
15/15
Adoption
3/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated about 3 months ago, so search-mesh is actively maintained.
  • Our last check on 2026-09-20 found the source still online.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 92/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

search-mesh compared with similar skills

All 4 of these similar skills score higher than search-mesh; compare them before choosing.

SkillScoreStarsUpdatedFormat
search-mesh (this skill)by jamestkelly7833mo agoMCP Server
Agent-Reachby Panniantong10086.1k13d agoCLAUDE.md
headroomby headroomlabs-ai10074.1ktodayCLAUDE.md
rufloby ruvnet10073.5ktodayCLAUDE.md
CowAgentby zhayujie10047.2ktodayCLAUDE.md

Frequently asked questions

How do I install search-mesh?
Run claude mcp add jamestkelly -- npx -y github:jamestkelly/search-mesh. The install tabs above show the steps for each supported agent.
Which AI agents does search-mesh work with?
It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
Is search-mesh safe to use?
It is MIT-licensed and scores 92/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is search-mesh still maintained?
The repository was last updated about 3 months ago, so search-mesh is actively maintained.

Search-Mesh

Search-Mesh is a Rust-native codebase intelligence service for autonomous coding agents.

The goal is to reduce agent latency and token waste by moving repository search, syntax-aware filtering, context extraction, and precise patching into a local MCP-compatible process.

Status

This repository is in early setup. The current focus is a small, correct MVP:

  • A Rust workspace with separate core and MCP crates.
  • A JSON-RPC over stdio server implementing the MCP initialize lifecycle handshake, suitable for Claude Code, OpenCode, and other MCP-compatible agents.
  • A first search tool, scan, backed by multi-keyword scanning.
  • A syntax probe tool, ast_probe, backed by tree-sitter for Rust, Python, JavaScript, and TypeScript.
  • A context extraction tool, squeeze, that returns AST-bounded source blocks.
  • A patch tool, patch, that applies precise line/column edits and reports syntax validity.

SIMD acceleration, tree-sitter verification, semantic squeezing, and atomic patching are planned phases, not current guarantees.

Intended Tools

  • scan: scan target directories for multiple keywords in one pass.
  • ast_probe: validate raw hits against syntax tree node types.
  • squeeze: return the smallest useful AST-bounded code block around a hit.
  • patch: apply byte-offset edits and verify syntax after mutation.

See docs/usage.md for local examples and docs/mcp-protocol.md for the draft protocol.

Install

Claude Code Plugin (Recommended)

This repository is a self-hosted Claude Code plugin marketplace. It bundles the search-mesh MCP server registration and the agent skill together, so /plugin install wires up both in one step:

/plugin marketplace add jamestkelly/search-mesh
/plugin install search-mesh@search-mesh

On macOS and Linux, the plugin downloads and verifies the correct prebuilt search-mesh-mcp binary automatically the first time it's used — no separate install step. Windows isn't supported by the automatic installer yet; use cargo install search-mesh-mcp or a manual binary install there.

Manual Install

For OpenCode, or Claude Code configured manually rather than through the plugin, search-mesh-mcp needs to be on your PATH first.

search-mesh-mcp is published to crates.io and as prebuilt GitHub Release binaries.

Via cargo:

cargo install search-mesh-mcp

Via prebuilt binary: download search-mesh-mcp-<target>.tar.gz for your platform (macOS arm64, macOS x64, or Linux x64) from the Releases page, extract it, and put search-mesh-mcp on your PATH.

Configure Claude Code Manually

Add to .mcp.json at your project root:

{
  "mcpServers": {
    "search-mesh": {
      "command": "search-mesh-mcp",
      "args": []
    }
  }
}

Configure OpenCode

Add to opencode.jsonc:

{
  "mcp": {
    "search-mesh": {
      "type": "local",
      "command": ["search-mesh-mcp"],
      "enabled": true
    }
  }
}

Agent Skill

The Claude Code plugin install above already includes this skill. To install it manually for other agents/setups, skills/search-mesh/SKILL.md teaches an agent when to prefer scan, ast_probe, squeeze, and patch over shell tools like grep, cat, and sed. Copy it into your own skills directory:

mkdir -p ~/.claude/skills/search-mesh
cp skills/search-mesh/SKILL.md ~/.claude/skills/search-mesh/SKILL.md

Repository Layout

.claude-plugin/
  plugin.json       Claude Code plugin manifest.
  marketplace.json  Self-hosted marketplace catalog listing this plugin.
.mcp.json           Bundled MCP server registration (used by the plugin).
crates/
  search-mesh-core/   Core search, parsing, squeezing, and patching logic.
  search-mesh-mcp/    JSON-RPC/MCP stdio server.
docs/
  architecture.md     System shape and phased design.
  mcp-protocol.md     Tool schemas and response shapes.
  roadmap.md          Near-term implementation plan.
  usage.md            Local MCP usage examples.
examples/
  scan-request.jsonl  Example newline-delimited JSON-RPC requests.
  initialize-request.jsonl
  ast-probe-request.jsonl
  squeeze-request.jsonl
  patch-request.jsonl
  patch-target.txt
scripts/
  search-mesh-mcp-launcher.sh  Installs search-mesh-mcp on first use (macOS/Linux), then execs it.
skills/
  search-mesh/SKILL.md  Agent skill teaching when to use these tools.

Development

Install Rust with rustup, then run:

just check

Equivalent commands:

cargo fmt --all -- --check
cargo clippy --workspace --all-targets -- -D warnings
cargo test --workspace

CI And Releases

Pull requests and pushes to main run the Review workflow:

  • cargo fmt --all -- --check
  • cargo clippy --workspace --all-targets -- -D warnings
  • cargo test --workspace
  • MCP JSONL example smoke tests

Releases are prepared with release-plz from Conventional Commits:

  • release-plz opens a release PR that bumps Cargo.toml/Cargo.lock versions and updates the changelog.
  • Merging that PR to main creates a git tag and GitHub Release per changed package, and publishes both crates to crates.io.
  • When a search-mesh-mcp-v* release is published, the Release Binaries workflow builds and attaches search-mesh-mcp binaries (plus a .sha256 checksum file for each) for macOS (arm64, x64) and Linux (x64) as release assets.

Design Principles

  • Prefer the smallest correct implementation before optimization.
  • Keep protocol handling separate from core repository intelligence.
  • Measure performance before adding specialized acceleration paths.
  • Avoid .unwrap() and .expect() outside tests.
  • Return agent-friendly payloads instead of whole files when possible.

License

MIT. See LICENSE.

Related Skills

View on GitHub
GitHub Stars3
CategoryDevelopment
Updated2mo ago
Forks2

Languages

Rust

Trust signals

92/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 low