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Agent Skill · 研究检索

cc-codex-spec-bootstrap(CC Codex spec bootstrap 搜索)

cc-codex-spec-bootstrap 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

cc-codex-spec-bootstrap 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 33,144。

总安装 2,399周安装 102GitHub Stars 1下载 840核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:cc-codex-spec-bootstrap(CC Codex spec bootstrap 搜索)
来源仓库:https://github.com/mindfold-ai/marketplace
仓库路径:skills/cc-codex-spec-bootstrap
安装命令:
npx skills add https://github.com/mindfold-ai/marketplace --skill cc-codex-spec-bootstrap
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/mindfold-ai/marketplace --skill cc-codex-spec-bootstrap

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

cc-codex-spec-bootstrap 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。通过多智能体流水线实现 Claude Code 与 Codex 的协同工作,提升代码生成质量。

安装前建议确认权限范围和维护状态,避免触发未授权的联网或文件操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/cc-codex-spec-bootstrap

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/mindfold-ai/marketplace --skill cc-codex-spec-…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

cc-codex-spec-bootstrap 怎么安装?

在终端执行 npx skills add https://github.com/mindfold-ai/marketplace --skill cc-codex-spec-bootstrap。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

cc-codex-spec-bootstrap 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

cc-codex-spec-bootstrap 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/mindfold-ai/marketplace --skill cc-codex-spec-bootstrap 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

cc-codex-spec-bootstrap 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

cc-codex-spec-bootstrap 用的人多吗?

公开数据是累计安装 2,399 次,近一周安装 102 次,下载 840 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

cc-codex-spec-bootstrap 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
cc-codex-spec-bootstrap

CC Codex spec bootstrap 搜索 · 本页

2,3991执行命令未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

CC + Codex Spec Bootstrap Pipeline

A multi-agent pipeline where Claude Code (CC) orchestrates and Codex executes in parallel. CC analyzes the repo with GitNexus + ABCoder, creates Trellis task PRDs, then Codex agents fill the coding specs — each with access to the same code intelligence MCP tools.

Why This Exists

AI coding agents produce better code when they have project-specific coding guidelines (not generic templates). But filling those guidelines manually is tedious. This skill automates the bootstrap:

  1. You (Claude Code) analyze the repo architecture using GitNexus + ABCoder
  2. You create Trellis tasks with rich PRDs containing architectural context + MCP tool instructions
  3. Codex agents run those tasks in parallel, each filling one spec directory using the same MCP tools

The result: every spec file contains real code examples, actual patterns, and project-specific anti-patterns — not placeholder text.


Prerequisites

Before running this skill, ensure these tools are set up. See references/mcp-setup.md for detailed installation instructions.

ToolPurposeRequired
TrellisWorkflow framework with .trellis/spec/ structureYes
GitNexusCode → knowledge graph (Tree-sitter + KuzuDB)Yes
ABCoderCode → UniAST (ts-morph for TS, tree-sitter for others)Yes
Codex CLIParallel task execution agentYes

Quick check:

# Verify all tools
npx gitnexus status          # GitNexus indexed?
abcoder list-repos            # ABCoder has ASTs?
codex mcp list                # Codex has MCP servers?
python3 .trellis/scripts/get_context.py  # Trellis initialized?

Phase 1: Analyze the Repository

Step 1: Index with GitNexus

npx gitnexus analyze

This builds a knowledge graph: nodes (symbols), edges (dependencies), clusters (module groups), and execution flows. Takes ~5s for a typical monorepo.

After indexing, use GitNexus MCP tools to understand the architecture:

gitnexus_query({query: "plugin system"})        # Find execution flows
gitnexus_context({name: "SomeClass"})            # 360-degree symbol view
gitnexus_cypher({query: "MATCH (n:Class) RETURN n.name, n.file LIMIT 30"})  # Graph queries

Step 2: Parse with ABCoder

ABCoder provides precise AST analysis — function signatures, type definitions, cross-file dependency chains.

# Parse each package
abcoder parse /path/to/package --lang typescript --name package-name --output ~/abcoder-asts

Then use ABCoder MCP tools:

get_repo_structure({repo_name: "package-name"})
get_file_structure({repo_name: "package-name", file_path: "src/core/types.ts"})
get_ast_node({repo_name: "package-name", node_ids: [{mod_path: "...", pkg_path: "...", name: "ClassName"}]})

Step 3: Map the Architecture

Combine insights from both tools to understand:

  • Package boundaries — which packages exist, what each one does
  • Module clusters — GitNexus clusters resource shows functional groupings
  • Key patterns — Fetcher/Provider/Plugin/Adapter/Router patterns
  • Cross-package data flows — how data moves between packages
  • Error handling patterns — how errors propagate
  • State management — what's stateless vs stateful

Write down your findings — they go into the PRDs.


Phase 2: Create Trellis Tasks

Task Decomposition Strategy

Create one task per (package, layer) combination. Each task is independently executable by a Codex agent.

Typical decomposition for a monorepo:

package-a/backend    → Task 1
package-a/frontend   → Task 2
package-b/backend    → Task 3
package-b/frontend   → Task 4
cross-layer-guide    → Task 5

Skip layers that don't apply (e.g., no frontend task for a pure CLI library).

Create Task Directories

python3 .trellis/scripts/task.py create "Fill <package> <layer> spec" --slug <package>-<layer>-spec

Write PRDs

Each PRD must contain these sections. This is the critical part — the PRD is the entire context a Codex agent receives.

# Fill <package> <layer> spec

## Goal
One sentence: what to analyze, what files to fill.

## Context
Project-specific architectural knowledge you gathered in Phase 1.
Key concepts, patterns, abstractions — everything the agent needs
to understand the codebase without reading every file.

## Tools Available
[Use the MCP Tools Template below]

## Files to Fill
List each spec file with bullet points on what to document.
Include hints about which source files to analyze.

## Important Rules

### Spec files are NOT fixed — adapt to reality
- Delete template files that don't apply
- Create new files for patterns templates don't cover
- Rename files if template names don't fit
- Update index.md to reflect the final set

### Parallel agents — stay in your lane
- ONLY modify files under your assigned spec directory
- DO NOT modify source code, other spec directories, or task files
- DO NOT run git commands
- You may read any file for analysis

## Acceptance Criteria
- [ ] Real code examples from the actual codebase (with file paths)
- [ ] Anti-patterns documented
- [ ] No placeholder text remaining
- [ ] index.md reflects actual file set

## Technical Notes
Package path, language, framework, build tools, key deps.

MCP Tools Template for PRDs

Include this in every PRD so Codex knows how to call the tools:

## Tools Available

You have two MCP servers configured — use both for accurate specs:

### GitNexus MCP (architecture-level: clusters, execution flows, impact)
| Tool | Purpose | Example |
|------|---------|---------|
| `gitnexus_query` | Find execution flows by concept | `gitnexus_query({query: "..."})` |
| `gitnexus_context` | 360-degree symbol view | `gitnexus_context({name: "ClassName"})` |
| `gitnexus_impact` | Blast radius analysis | `gitnexus_impact({target: "X", direction: "upstream"})` |
| `gitnexus_cypher` | Direct graph queries | `gitnexus_cypher({query: "MATCH ..."})` |

### ABCoder MCP (symbol-level: AST nodes, signatures, cross-file deps)
| Tool | Purpose | Example |
|------|---------|---------|
| `get_repo_structure` | Full file listing | `get_repo_structure({repo_name: "pkg"})` |
| `get_file_structure` | All nodes in a file | `get_file_structure({repo_name: "pkg", file_path: "src/..."})` |
| `get_ast_node` | Code + deps + refs | `get_ast_node({repo_name: "pkg", node_ids: [...]})` |

### Recommended Workflow
1. GitNexus first — find relevant execution flows and clusters
2. ABCoder second — get exact code patterns and signatures
3. Read source files — for full context where needed
4. Write specs — with real code examples from steps 2-3

Phase 3: Launch Codex Agents

Run in Parallel

Each task is independent — launch all agents simultaneously:

# One terminal per task
codex -q "Read .trellis/tasks/<task-slug>/prd.md and execute the task. Use GitNexus and ABCoder MCP tools to analyze the codebase, then fill all spec files listed in the PRD."

Monitor Progress

Check which spec files have been filled:

# Line counts — 0 or ~50 means still template
find .trellis/spec -name "*.md" -exec sh -c 'echo "$(wc -l < "$1") $1"' _ {} \; | sort -rn

# Check for remaining placeholders
grep -rl "To be filled" .trellis/spec/

# Newly created or modified files
find .trellis/spec -name "*.md" -newer .trellis/tasks/ -exec ls -la {} \;

Review Results

After all agents complete:

  1. Check line counts — substantive files should be 80+ lines
  2. Grep for leftover placeholders
  3. Spot-check a few files for real code examples vs generic advice
  4. Verify index.md in each directory reflects actual files

Checklist

  • GitNexus analyzed (npx gitnexus analyze)
  • ABCoder parsed all packages
  • GitNexus + ABCoder MCP configured for both Claude Code and Codex
  • Architecture mapped (packages, patterns, boundaries)
  • One task per (package, layer) created with task.py create
  • Each PRD has: Context, MCP Tools, Files to Fill, Rules, Acceptance Criteria
  • Codex agents launched in parallel
  • Results reviewed — no placeholders, real code examples present

平台分布

Codex

35.36%
按下载量换算297

Claude

30.56%
按下载量换算257

Cursor

20.97%
按下载量换算176

Gemini CLI

11.04%
按下载量换算93

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/mindfold-ai/marketplace --skill cc-codex-spec-bootstrap 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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