接入你的智能体
發布者自己的指令。不是 PlugMCP 端點。
用我們的金鑰與日誌託管一份副本。
npx -y [email protected]Claude Code 沒有本機指令的安裝連結。複製下方指令並在終端機中執行。
claude mcp add agentsmesh -- npx -y agentsmesh@0.43.1在終端機中執行。
{ "mcpServers": { "agentsmesh": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}claude_desktop_config.json
遠端 URL 還是本機指令？ 兩種安裝方式的差異
來自發布者
AI coding config sync CLI. Keep rules, commands, agents, skills, MCP servers, hooks and permissions in one .agentsmesh source and generate native config for Claude Code, Cursor, GitHub Copilot, Gemini CLI, Codex CLI and every other tool, plus a shared les
Every AI coding assistant reads its own config: CLAUDE.md, AGENTS.md, .cursor/rules/*.mdc, .github/copilot-instructions.md, .gemini/settings.json, and more. Keeping rules, prompts, MCP servers, hooks, and permissions the same across Claude Code, Cursor, GitHub Copilot, Gemini CLI, Codex CLI, and the rest by hand does not scale, and the copies drift.
AgentsMesh makes .agentsmesh/ the one source. Write rules, commands, agents, skills, MCP servers, hooks, ignore patterns, and permissions once. agentsmesh generate writes every tool's native files, agentsmesh import pulls existing configs back in without loss, and agentsmesh check fails CI when anything drifts.
Your agents learn from your repo. Lessons give every agent, in every tool, one git-tracked memory: capture a rule after a failure, recall it before the next edit. Same mistake, never twice. No other config-sync tool has this.
[!NOTE] Full documentation, guides, and the per-tool reference: samplexbro.github.io/agentsmesh
Every method ships the same CLI (agentsmesh, alias amsh). The npm package also exposes the TypeScript API.
# Homebrew (macOS / Linux), no Node.js required
brew install samplexbro/agentsmesh/agentsmesh
# Standalone binary (Linux / macOS), no Node.js required
# Windows: download agentsmesh-windows-x64.exe from GitHub Releases
curl -fsSL https://github.com/sampleXbro/agentsmesh/releases/latest/download/install.sh | sh
# npm / pnpm / yarn, requires Node.js 20+
npm install -g agentsmesh # or: pnpm add -g agentsmesh / yarn global add agentsmesh
npm install -D agentsmesh # pin per repo as a dev dependency, then run with npx
Binaries with SHA-256 checksums are on GitHub Releases.
agentsmesh init # detect the tools you use, import their configs, or scaffold fresh
agentsmesh generate # write each tool's native config from the one source
agentsmesh check # CI drift gate against .agentsmesh/.lock
On a terminal, init runs a short wizard: pick targets, import detected configs (.cursor/, .claude/, .github/copilot-instructions.md, and more), enable lessons, and optionally generate right away. Nothing is written until you finish. Use --yes for the non-interactive default (in a script, init without --yes stops when it finds existing tool config, so generate never replaces config you have not imported), --global for your user-level config under ~/.agentsmesh/, and npx agentsmesh if you installed it as a dev dependency.
init enables the tools you actually use, not all of them: whatever this project already has config for, else whatever is installed on your machine, else a minimal set of Claude Code, Cursor and Copilot. It prints what it picked. Pass --targets a,b to choose, or --all-targets for everything.
[!TIP] Commit both
.agentsmesh/and the generated tool files, like a lockfile: the tools read the generated files directly, andagentsmesh checkkeeps the two from drifting.
# Before: one copy of the same intent per tool
CLAUDE.md
AGENTS.md
.cursor/rules/*.mdc
.github/copilot-instructions.md
.gemini/settings.json
.windsurf/rules/*.md
.codex/config.toml
.kiro/steering/*.md
# After: one canonical source, generated everywhere
.agentsmesh/
rules/_root.md # the root rule every tool projects
commands/ # reusable slash-style prompts
agents/ # agent definitions
skills/ # composable skills with supporting files
mcp.json # MCP server definitions
hooks.yaml # pre/post tool hooks
permissions.yaml # allow/deny rules
ignore # paths the assistant must not touch
lessons/ # optional shared agent memory
agentsmesh.yaml selects targets and features, agentsmesh.local.yaml holds per-developer overrides (gitignored), and .agentsmesh/.lock records the checksums that check enforces. Cross-file links are rewritten to each tool's own paths on the way out.
Lessons are a memory of past mistakes that every agent reads before it acts and writes after something goes wrong, so a lesson one agent learns today helps every teammate's agent tomorrow, in any tool.
The memory is one git-tracked file, .agentsmesh/lessons/lessons.json, driven by two commands:
# After a failure, the agent writes the rule down with a trigger:
agentsmesh lessons add "Normalize CLI display paths to forward slashes" \
--topic windows-paths --trigger-file "src/cli/**/*.ts"
# Before the next edit, any agent in any tool recalls it:
agentsmesh lessons query --file src/cli/output.ts --session auto
# -> "Normalize CLI display paths to forward slashes"
agentsmesh init --lessons wires the loop once: an always-on rule in _root.md, a lessons skill where supported, recall and capture hooks on hook-capable tools, and matching MCP tools (lessons_query, lessons_add) for agents without shell access. Rules can be scoped by file, command, or keyword, or always-on with --scope always. A git merge driver, set up by init --lessons and then by generate in every clone, combines lessons captured on two branches; agentsmesh lessons resolve repairs a conflict that slips through.
Full walkthrough: Teach your AI agents with lessons · agentsmesh lessons reference
Ruler and rulesync both cover the common ground well, and rulesync supports more tools than AgentsMesh does. What is left to differentiate is what survives translation: links rewritten per target, a round-trip that loses nothing, a lockfile-backed drift gate, new tools addable without a core release, and a memory that learns.
Verified against each project's source and docs on 2026-09-19. Tool counts are counted from source, not marketing claims, and differ by how variants are tallied. Two newer projects solve adjacent problems rather than this one: capa is a package manager and MCP gateway, and Sentry's dotagents manages skill and MCP dependencies without emitting instruction files. Detail and sources in the alternatives guide; corrections welcome.
Built to be depended on: 12,000+ tests on Linux, macOS, and Windows CI, JSON-Schema-validated configs, npm releases with provenance, and a per-file coverage floor in the test gate.
.agentsmesh/skills/api-gen/template.hbs becomes .claude/skills/... or .cursor/skills/... in each generated file. Generation pipelineimport, then diff, then generate, then check; nothing is overwritten blind. When several tools have a root rule, they are merged into one _root.md rather than the last one winning, their permissions, ignore patterns and MCP servers add up instead of replacing each other, and init --yes keeps both versions of a same-name rule, command or agent. Existing-project guidegenerate and convert reject output directories that resolve outside the project (or home directory with --global), checked before any write and under --dry-run too. Stale-file cleanup enforces the same boundary.convert --from <a> --to <b> rewrites one tool's config directly into another's. convert~/.agentsmesh/ syncs personal config to ~/.claude/, ~/.cursor/, ~/.codex/, and more; config commands accept --global. Global pathscheck gates drift, diff previews, merge rebuilds the lock after a Git conflict, and lint warns about content a tool would mishandle. check · lintextends. Install rules, skills, agents, and commands from any git repo; hooks, permissions, and MCP from remote sources stay off until you opt in. installgenerate, import, lint, diff, and check, --json envelopes on every command except watch, and an MCP server so agents can operate the config themselves. API · MCP serverRun agentsmesh <command> --help for flags, or see the CLI reference.
AgentsMesh generates native config for every major AI coding assistant, plus plugin targets. Native, embedded, and partial support per feature is tracked in the supported-tools matrix.
agentsmesh generate)agentsmesh generate --global)Contributions welcome: edit canonical .agentsmesh/ sources, never the generated files. See CONTRIBUTING.md. Released under the MIT license.
AI coding config sync CLI. Keep rules, commands, agents, skills, MCP servers, hooks and permissions in one .agentsmesh source and generate native config for Claude Code, Cursor, GitHub Copilot, Gemini
{ "mcpServers": { "agentsmesh": { "command": "npx", "args": [ "-y", "[email protected]" ] } }}總覽
AI coding config sync CLI. Keep rules, commands, agents, skills, MCP servers, hooks and permissions in one .agentsmesh source and generate native config for Claude Code, Cursor, GitHub Copilot, Gemini CLI, Codex CLI and every other tool, plus a shared les
工具
尚無工具清單。遠端伺服器會在首次健康檢查後取得工具清單。
常見問題
在本頁複製適用於 Cursor、Claude Desktop、Claude Code 或 VS Code 的 stdio 設定。它使用發布者的安裝方式，而不是 PlugMCP 代理伺服器。
這筆記錄未列出必要的環境變數。
Agentsmesh 透過 stdio 在本機執行。
上次檢查時間：2026-11-15。 AI coding config sync CLI. Keep rules, commands, agents, skills, MCP servers, hooks and permissions in one .agentsmesh source and generate native config for Claude Code, Cursor, GitHub Copilot, Gemini CLI, Codex CLI and every other tool, plus a shared les