能力标签
OpenLore
🔌
MCP工具

OpenLore

基于 TypeScript · 让 AI 助手直接操作你的系统与工具
⭐ 153 Stars 🍴 20 Forks 💻 TypeScript 📄 MIT 🏷 AI 8.0分
8.0AI 综合评分
mcpai-agentsai-codingtypescript
✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐:OpenLore 是一款优质的MCP工具。AI 综合评分 8.0 分,在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案,这是一个值得深入了解的选择。

📚 深度解析

OpenLore 是一款基于 MCP(Model Context Protocol)标准协议的 AI 工具扩展。MCP 协议由 Anthropic 开发并开源,旨在建立 AI 模型与外部工具之间的标准化通信接口,目前已被 Claude Desktop、Claude Code、Cursor 等主流 AI 工具采纳。

通过安装 OpenLore,你的 AI 助手将获得额外的工具调用能力,可以用自然语言直接操控该工具的功能,无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置,永久增强"——配置完成后,每次与 AI 对话时都可以无缝调用这些工具。

在技术实现上,MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信,工具的功能以"工具列表"的形式暴露给 AI 模型,AI 可以按需调用。OpenLore 提供了结构化的工具调用接口,使 AI 模型能够精确地理解和使用每个功能点,显著降低 AI 在工具使用上的错误率。

与传统的 API 集成相比,MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON,即可让 AI 获得全新能力。AI Skill Hub 将 OpenLore 评为 AI 评分 8.0 分,属于同类工具中的优质选择。

📋 工具概览

OpenLore 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

GitHub Stars
⭐ 153
开发语言
TypeScript
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
8.0 分
工具类型
MCP工具
Forks
20

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

OpenLore 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

📌 核心特色
  • 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
  • 提供结构化工具调用接口,显著降低 AI 集成复杂度
  • 支持 Claude Desktop 和 Claude Code 无缝接入,开箱即用
  • 可与其他 MCP 工具组合叠加,构建完整 AI 工作站
  • 轻量无侵入设计,不影响现有系统架构
🎯 主要使用场景
  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/clay-good/OpenLore

# 方式二:手动配置 claude_desktop_config.json
{
  "mcpServers": {
    "openlore": {
      "command": "npx",
      "args": ["-y", "openlore"]
    }
  }
}

# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
📋 安装步骤说明
  1. 确认已安装 Node.js(v18 或以上版本)
  2. 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
  3. 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
  4. 保存配置文件并重启 Claude 客户端
  5. 重启后,在对话中即可使用本工具
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 安装后在 Claude 对话中直接使用
# 示例:
用户: 请帮我用 OpenLore 执行以下任务...
Claude: [自动调用 OpenLore MCP 工具处理请求]

# 查看可用工具列表
# 在 Claude 中输入:"列出所有可用的 MCP 工具"
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
// claude_desktop_config.json 配置示例
{
  "mcpServers": {
    "openlore": {
      "command": "npx",
      "args": ["-y", "openlore"],
      "env": {
        // "API_KEY": "your-api-key-here"
      }
    }
  }
}

// 保存后重启 Claude Desktop 生效
📑 README 深度解析 真实文档 完整度 55/100 含工作流图 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

OpenLore

<p align="center"> <strong>Deterministic, local-first memory and guardrails for AI coding agents — with no LLM in the hot path.</strong><br> One call tells your agent the code a task touches; one gate tells it what's unsafe to change.<br> Grounded in static analysis. No API key. Same answer every time. </p>

<p align="center"> <a href="https://www.npmjs.com/package/openlore"><img src="https://img.shields.io/npm/v/openlore?color=2563eb&label=npm&logo=npm&logoColor=white" alt="npm version"></a> <a href="https://github.com/clay-good/OpenLore/actions/workflows/ci.yml"><img src="https://github.com/clay-good/OpenLore/actions/workflows/ci.yml/badge.svg" alt="CI status"></a> <a href="LICENSE"><img src="https://img.shields.io/npm/l/openlore?color=22c55e" alt="MIT License"></a> <img src="https://img.shields.io/node/v/openlore?color=339933&logo=node.js&logoColor=white" alt="Node >=22.19"> <br> <img src="https://img.shields.io/badge/MCP-ready-7c3aed?logo=anthropic&logoColor=white" alt="MCP ready"> <img src="https://img.shields.io/badge/languages-21%20%2B%2012%20IaC-f97316" alt="21 languages + 12 IaC ecosystems"> <img src="https://img.shields.io/badge/tests-5500%2B-success" alt="5500+ tests"> <img src="https://img.shields.io/badge/API_key-not_required-0ea5e9" alt="No API key required"> <a href="https://github.com/clay-good/OpenLore/stargazers"><img src="https://img.shields.io/github/stars/clay-good/OpenLore?style=social" alt="GitHub stars"></a> </p>

<p align="center"> <img src="docs/openlore-demo.gif" alt="An unedited terminal recording of the published openlore v2.1.6 on a fresh clone of ripgrep: openlore install wires the agent and indexes 235 files live in 14 seconds with no API key, openlore orient returns the functions a task touches, openlore review reports a changed signature that left 39 callers stale, and openlore prove --estimate projects the payoff on that repo" width="100%"> </p>

<p align="center"><em>A real, unedited recording — the published <code>openlore</code> on a fresh clone of <a href="https://github.com/BurntSushi/ripgrep">ripgrep</a>. <strong>install</strong> wires your agent and indexes the repo live — 235 files, 2,978 functions, 4,329 call edges in <strong>14 seconds</strong>, no API key → <strong>orient</strong> returns the code a task touches → <strong>review</strong> catches a signature change that left <strong>39 callers</strong> stale → <strong>prove</strong> projects the payoff. Re-record it yourself: <a href="docs/openlore-demo.tape"><code>docs/openlore-demo.tape</code></a>.</em></p>

<p align="center"> <strong><a href="#install-in-one-command">Install</a> · <a href="#what-you-get">What you get</a> · <a href="#value-scorecard--does-it-pay-for-itself">Benchmarks</a> · <a href="#governance">Governance</a> · <a href="#how-it-works">How it works</a> · <a href="#openlore-vs-alternatives">vs. Alternatives</a> · <a href="#documentation">Docs</a></strong> </p>

---

AI coding agents are powerful but amnesiac and ungoverned: every task restarts by re-reading the same files, long sessions drift onto stale assumptions, and nothing warns the agent when a change is about to break a contract or cross a boundary.

OpenLore fixes both halves. It runs a one-time static analysis of your repo and keeps a live knowledge graph — call structure, types, tests, decisions, IaC, spec drift. Your agent queries it to start every task already oriented and to certify a change before it lands. It's deterministic and local-first — no LLM in the hot path — so the same question always returns the same grounded answer, and the agent is told when a fact goes stale instead of served a confident guess.

Core features

Everything is deterministic and local; only the two entries marked "API key" ever talk to a model.

Analyze (no key) — Full call graph in SQLite, community detection, McCabe complexity, extracted DB schemas / HTTP routes / UI components / middleware / env vars. Outputs a ~600-token CODEBASE.md digest. The file watcher updates the graph incrementally on every save and converges to what analyze --force would produce; when a change exceeds the per-save budget, the un-recomputed files are marked explicitly stale, never silently divergent.

Drift (no key) — Compares git changes against spec mappings in milliseconds (Gap / Uncovered / Stale / ADR-gap). Installs as a pre-commit hook. → docs/drift-detection.md

Test-impact selection (no key)select_tests walks the call graph backward from a change to every test that reaches it, with paths. An honest over-approximate prioritizer, not a replacement for the full suite. → docs/test-impact-selection.md

<details> <summary><strong>All the other tools</strong> — certificates, dead-code, invariants, coverage gaps, clones, error & env impact, coupling…</summary>

  • find_dead_code (no key) — cross-language mark-and-sweep, "what dies if I delete X?" Confidence-tagged candidates, never deletion authority. → docs/reachability-dead-code.md
  • change_impact_certificate (no key) — certifies whether a diff newly opens a path into a declared surface (differential reachability), plus blast radius and tests. CLI: openlore impact-certificate.
  • certify_public_surface (no key, opt-in) — breaking-change verdict per export, consumers named. Renamed exports detected via symbol-identity continuity. CLI: openlore certify-public-surface.
  • check_architecture (no key) — enforces author-declared layer, boundary, dependency, cycle, reachability, orphan, and instability rules. → docs/architecture-invariants.md
  • verify_claim (no key, opt-in)confirmed / refuted / unverifiable with a citation receipt, never an LLM guess.
  • openlore enforce (no key, advisory) — the unified gate over all governance findings; one enforcement.policy maps each finding → blocking / frozen / advisory / off. → docs/configuration.md
  • Decisions on the graph (API key for consolidation)record_decision before writing code; a pre-commit hook gates until reviewed. Decisions become decision:: nodes joined to the files they govern, so analyze_impact returns them as neighbors.
  • Epistemic Lease (no key) — models drift as a navigation phenomenon; every MCP response carries a brief, factual freshness note once context ages. orient() resets it.
  • structural_diff (no key) — the structural complement to git diff: functions/edges added/removed, signature changes, and callers now stale. → docs/structural-diff.md
  • get_change_coupling (no key) — co-change coupling and churn from git. Advisory, correlation not causation. → docs/change-coupling.md
  • report_coverage_gaps (no key, opt-in) — which load-bearing code has no test reaching it, ranked by significance. Never claims a symbol is "tested." → docs/coverage-gaps.md
  • get_style_fingerprint — a descriptive idiom profile so an agent matches the house style; a counter below the evidence floor reports null, never a guess.
  • find_clones — the edit-time "does a near-duplicate already exist?" query (a symbol or raw snippet), ranked exact > structural > near.
  • analyze_error_propagation — exceptions that escape vs. those caught (TS/JS/Python/Java/C#), or returned errors and panic/recover flow in Go; a sound lower bound.
  • analyze_env_impact — "what breaks if I remove this env var?": read sites, upstream callers, tests, per-site required.
  • briefing_since — the catch-up lens: changed symbols since a base ref, ranked into a fixed tier order.
  • plan_parallel_work / map_in_flight_conflicts — a hazard-typed conflict graph over a task list, or over every in-flight branch/PR/agent-task (opt-in coordination preset).
  • Share the index — the graph is a function of committed source, so a team analyzes once: openlore export bundle → a portable .olbundle, openlore import bootstraps in seconds (validate-or-rebuild). → docs/shareable-bundle.md
  • Preflight — a CI staleness gate; any PR editing indexed files fails until the graph is refreshed. → docs/preflight.md

</details>

---

Requirements

- Node.js 22.19+ (node:sqlite is available without runtime flags and all runtime dependencies support this floor). - No API key for analyze, drift, mcp, init, and every governance/navigation tool. - Provider access only for standalone generate, verify, and drift --use-llm. Agent-hosted Generate/Repair uses the connected host model and needs no additional OpenLore key:

  export ANTHROPIC_API_KEY=sk-ant-...    # default provider
  export OPENAI_API_KEY=sk-...           # OpenAI
  export GEMINI_API_KEY=...              # Google Gemini
  
…or use a CLI-based provider (codex-cli, claude-code, gemini-cli, antigravity-cli, mistral-vibe, cursor-agent) — no key, just the CLI on your PATH.

---

Install in one command

npm install -g openlore && openlore install

That one command auto-detects your agent (Claude Code, Cursor, Cline, Continue, Pi, AGENTS.md), wires it to call orient() automatically, registers the MCP server, and builds the index — no API key, no config, no questions. Then ask your agent:

orient("add a payment method")

…and it begins already knowing the relevant functions, their callers, matching specs, tests, and the risk of changing each — in a single call.

Zero config, everything discoverable. Core value needs no keys. Run openlore features to see every opt-in capability (embeddings, the commit gate, the spec store…), whether it's active, and the one command to turn it on.

---

Quickstart & what it costs

npm install -g openlore
cd /path/to/your-project
openlore install     # wire your agent — here and for every future repo — AND build the index

That single command auto-detects your agent surfaces and wires each to call orient(), registers the MCP server so it starts with your agent, builds the local BM25 index (no network), wires a non-blocking decision trail, and — for Claude Code — injects a bounded, ignorable orientation block before each new prompt so the common task begins already oriented. It also wires the user scope for agents that have one, so every git repository you open afterwards reaches OpenLore and builds its index in the background on first touch (git work trees only, disclosed once per repository, --repo-only to opt out). Nothing prompts you; nothing runs on npm install.

openlore install --no-analyze   # wire surfaces only; build the index later
openlore install --dry-run      # preview every change without writing
openlore doctor                 # verify config, index, MCP wiring, embeddings
openlore update                 # upgrade (detects npm / Homebrew / npx)

The MCP server keeps the index fresh as you edit (file watcher on by default; node_modules/, dist/, target/ pruned automatically). See docs/install.md.

Platform support: Linux and Windows are exercised in CI; macOS is supported. Agent launch configurations are generated for the host that runs openlore install; regenerate them after moving a configured workspace to another machine or changing its Node installation.

What it asks for — measured on a fresh clone of ripgrep with the published openlore@2.1.6 (npx openlore init && time npx openlore analyze && du -sh .openlore):

What it costsOn ripgrep (232 files indexed)
**One-time index build****13.6 s**, entirely local — no API key, no network
**Disk****27 MB** under .openlore/ (gitignorable; always rebuildable from source)
**Per-query latency****~430 µs p50** in-process via the MCP server (a cold one-shot CLI call is ~2 s, mostly Node startup)
**Your source code**never leaves the machine — no account, no telemetry (opt-in only), no hosted index
**Lock-in**none — delete .openlore/ and nothing about your repo has changed

Optional telemetry is enabled only with OPENLORE_TELEMETRY=1. It records tool calls, agent identity, latency, error messages, decision titles, and lease events. It stays in the repository's gitignored .openlore/telemetry/ directory, rotates locally, is never transmitted, and records filesystem locations in error/module fields as project-relative paths (or ~-relative paths). Optional LLM diagnostics are enabled only with OPENLORE_LLM_LOGS=1; they store prompts and responses after secret redaction in the local, gitignored .openlore/logs/ directory with owner-only log-file permissions on POSIX systems. Newly opted-in logging retains at most six files or 300 MB; logs left by older releases are pruned on the next opted-in save, or may be removed by deleting .openlore/logs/. Neither telemetry nor LLM logs are uploaded. Token-bearing daemon descriptors are written with owner-only 0o600 permissions on POSIX systems. Windows does not expose equivalent POSIX mode enforcement through Node; keep the workspace under a user-only ACL when using a daemon token there. Network-visible daemon binds require a token and a wildcard host (0.0.0.0 or ::); discovery and lifecycle probes remain loopback-only.

Large monorepos take minutes rather than seconds — stated plainly in Known Limitations.

<details> <summary>Optional pipeline, install from source, and Nix</summary>

openlore generate   # standalone provider-backed generation (API key or supported local/host CLI)
openlore drift      # detect spec/code drift (no API key)
openlore decisions  # manage architectural decisions

Install from source:

git clone https://github.com/clay-good/openlore
cd openlore && npm install && npm run build && npm link

Nix / NixOS:

nix run github:clay-good/openlore -- analyze
nix shell github:clay-good/openlore

</details>

Migrating from spec-gen? The package is now openlore — see docs/RENAME-TO-OPENLORE.md.

---

OpenLore vs. alternatives

Everyone in this category answers the same first question: how does the agent see the codebase without reading it file by file? LSP toolkits answer with symbols, graph MCP servers with a parsed graph, search platforms with an index. All real answers, several of them good.

OpenLore answers it too — then keeps going into the second question almost nobody is answering: what happens when the agent starts writing? A retrieval layer makes an agent informed; it doesn't make it safe. Nothing in a symbol index tells you this diff opened a path into your auth boundary, this signature change breaks four consumers by name, or the fact your agent has used for 40 tool calls went stale 12 commits ago. That half — governance, on the same graph, no LLM in the loop — is what OpenLore was built for.

Agent built-ins<br>*(Cursor, Claude Code)*LSP toolkits<br>*(e.g. Serena)*Graph MCP serversSearch platforms<br>*(e.g. Sourcegraph)***OpenLore**
Structural context instead of file reads❌ grep + reads✓ symbols✓ parsed graph✓ index✓ call graph + **IaC + decisions on one graph**
Local, no API key, deterministicPartial❌ hosted✓ no LLM in the hot path
Cross-session memory anchored to codePartial✓ notes✓ **carried across renames**, self-invalidating
Told when a cached fact goes **stale**✓ Epistemic Lease
Blast radius + which tests to runPartialPartial✓ backward reachability, with paths
Breaking-change **verdict** over a diffPartial✓ per export, **consumers named**
"Did this diff open a path into a sensitive boundary?"✓ differential, pre-commit
Spec/code drift + ADRs gated at commit✓ milliseconds, no API key
Cost/round-trip effect **published with the losses**✓ −26% round-trips on deep tasks

Where the others are the better pick — we'd rather you use the right tool than ours:

  • **Symbol-level edits (rename across files, move a symbol) — an LSP toolkit's** home turf. OpenLore is deliberately read-only; that protects its stores from mutation, while provenance labels and data framing disclose that served repository text is still untrusted. The two compose well.
  • Search across hundreds of repos, org-wide, with an audit trail — a code search platform. OpenLore is local-first and repo-scoped (federation is opt-in and read-only).
  • Just fast graph retrieval, nothing else — a graph MCP server is a smaller surface. OpenLore's extra weight is governance; skip it if you don't want a commit gate.
  • A small, familiar repo and shallow questions — your agent's built-in search is often cheaper. We measured it and published it.

Comparisons reflect each project's publicly documented capabilities as of July 2026 and describe categories, not verdicts on quality; a correction PR is always welcome. OpenLore exports SCIP, so it sits alongside these tools rather than against them.

---

🎯 aiskill88 AI 点评 A 级 2026-06-02

高质量的开源MCP工具,具有较强的实用价值

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
部署方案
  • Docker:OpenLore 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
OpenLore 中文教程OpenLore 安装报错怎么办OpenLore MCP 配置OpenLore Docker 部署OpenLore Agent 工作流OpenLore 与同类工具对比OpenLore 最佳实践OpenLore 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal

👥 适合人群

Claude Desktop / Claude Code 用户AI 工具开发者需要扩展 AI 能力的专业人士自动化工程师

🎯 使用场景

  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +标准化 MCP 协议,生态互联性强
  • +与 Claude 官方生态无缝对接
  • +即插即用,配置简单快捷
⚠️ 不足
  • 依赖 Claude 客户端,非 Claude 用户无法使用
  • MCP 协议仍在持续演进,接口可能变更
  • 需要一定的配置步骤
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

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❓ 常见问题 FAQ

OpenLore 是一款TypeScript开发的AI辅助工具。开源MCP工具:openlore provides persistent architectural memory for AI coding agents by turnin。⭐153 · TypeScript 主要应用场景包括:AI编码代理的持久内存。
💡 AI Skill Hub 点评

总体来看,OpenLore 是一款质量优秀的MCP工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ MIT 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 OpenLore
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 OpenLore
原始描述 开源MCP工具:openlore provides persistent architectural memory for AI coding agents by turnin。⭐153 · TypeScript
Topics mcpai-agentsai-codingtypescript
GitHub https://github.com/clay-good/OpenLore
License MIT
语言 TypeScript
🔗 原始来源
🐙 GitHub 仓库  https://github.com/clay-good/OpenLore 🌐 官方网站  https://www.npmjs.com/package/openlore

收录时间:2026-06-02 · 更新时间:2026-06-02 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。

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