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#mcp
codebase-analysis
Codebase analysis via the Codex MCP server with a read-only sandbox. Trigger when user needs architecture overview ("analyze this codebase with Codex", "have Codex map dependencies"), onboarding to unfamiliar code, understanding legacy systems, or identifying technical debt.
DeepseekModel
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质量 良好 · 64
v1.0.0
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name codebase-analysis description Codebase analysis via the Codex MCP server with a read-only sandbox. Trigger when user needs architecture overview ("analyze this codebase with Codex", "have Codex map dependencies"), onboarding to unfamiliar code, understanding legacy systems, or identifying technical debt. Codebase Analysis via Codex Use Codex to get a second-opinion architectural read of the current project, with the sandbox locked to read-only . Codex consults; Claude writes. Transport Always use the MCP tool. The plugin runs codex mcp-server on stdio via .mcp.json . Tool name: mcp__plugin_codex_cli__codex . If the example below errors with an unknown-tool error, run /mcp and substitute the actual prefix (e.g., mcp__codex_cli__codex ). Shell fallback is a last resort (see ../references/commands.md ). Model Pin model: "gpt-5.6-sol" with config: { "model_reasoning_effort": "medium" } on the opening call. Codebase analysis benefits from the flagship's reasoning across many files — don't downgrade to gpt-5.6-luna here. Set both on the first codex call only; codex-reply inherits them. Honor an explicit user-named model if given. See ../references/patterns.md → Models and Reasoning effort. Basic call mcp__plugin_codex_cli__codex({ "prompt": "Analyze this project's architecture: entry points, major modules, component relationships, and notable dependencies.", "sandbox": "read-only", "model": "gpt-5.6-sol", "config": { "model_reasoning_effort": "medium" } }) The response includes a threadId . Use mcp__plugin_codex_cli__codex-reply with that id to drill in without re-establishing context. When to Use Onboarding to an unfamiliar codebase Understanding legacy systems Mapping component relationships Finding hidden dependencies Architecture documentation Technical debt assessment Examples Full project analysis: mcp__plugin_codex_cli__codex({ "prompt": "Analyze this project. Report on:\n- Overall architecture\n- Key dependencies\n- Component relationships\n- Potential issues", "sandbox": "read-only", "model": "gpt-5.6-sol", "config": { "model_reasoning_effort": "medium" } }) Flow mapping: mcp__plugin_codex_cli__codex({ "prompt": "Map the authentication flow. Identify every component involved from request to session creation.", "sandbox": "read-only", "model": "gpt-5.6-sol", "config": { "model_reasoning_effort": "medium" } }) Dependency analysis: mcp__plugin_codex_cli__codex({ "prompt": "Analyze dependencies: direct vs transitive, outdated packages, circular dependencies, bundle-size impact.", "sandbox": "read-only", "model": "gpt-5.6-sol", "config": { "model_reasoning_effort": "medium" } }) Iterative workflow (prefer codex-reply ) When you're still working on the same area of the codebase, continue the existing thread rather than starting a new codex call. Codex retains context between rounds; fresh calls force it to re-read files and drift from its prior reasoning. Typical loop: Initial consult → save the threadId from the response. Claude reads related files / runs a query / makes a change. codex-reply with new findings or a follow-up question. Repeat — but cap at 3–4 rounds total. If the thread isn't converging, stop and bring the current state back to the user. threadId is an MCP argument — pass it as the threadId field of codex-reply , not in the prompt text. See ../references/mcp-schema.md for wrong-vs-right examples. Example — three rounds on the same architecture thread: # Round 1 — initial map (opening call pins model + effort) mcp__plugin_codex_cli__codex({ "prompt": "Map the auth flow end-to-end.", "sandbox": "read-only", "model": "gpt-5.6-sol", "config": { "model_reasoning_effort": "medium" } }) # → threadId: "019da14b-..." / flags: uncertainty about session rotation # Round 2 — Claude reads src/session/ and reports back mcp__plugin_codex_cli__codex-reply({ "threadId": "019da14b-...", "prompt": "src/session/rotate.ts shows a 15m rotation window, not the 1h you assumed. Does that change anything in your flow map?" }) # Round 3 — drill into a specific layer mcp__plugin_codex_cli__codex-reply({ "threadId": "019da14b-...", "prompt": "Focus on the data layer. What invariants does this flow depend on and where are they enforced?" }) Start a fresh thread when: the user switches topic, the threadId is no longer in context, or Claude has made substantial code changes that would be cleaner to re-prime than to patch incrementally. See ../references/patterns.md . After the analysis Codex's read is a second opinion, not authoritative — it can misread structure or miss context it never saw. Relay the findings to the user and attribute them to Codex, rather than presenting them as verified fact. Spot-check claims against the actual code before acting on them (see ../references/patterns.md → Validation) — especially dependency, impact, and "nothing else uses this" claims. Surface uncertainty or disagreement to the user instead of smoothing it over into a confident-sounding summary. Safety Always sandbox: "read-only" . Codex must not modify files. Never use workspace-write or danger-full-access . Never use --dangerously-bypass-approvals-and-sandbox . Fallback (rare) If the MCP server is unavailable (plugin disabled, server crashed), see ../references/commands.md for the Bash equivalent. Requires dangerouslyDisableSandbox: true because Codex writes its own session state.
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下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |