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codex-subagentCodex subagent 搜索

Agent Skill

codex-subagent 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。

总安装

7,490

周安装

309

GitHub Stars

902

下载量

2,447
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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:codex-subagent(Codex subagent 搜索)
来源仓库:https://github.com/am-will/codex-skills
仓库路径:skills/codex-subagent
安装命令:
npx skills add https://github.com/am-will/codex-skills --skill codex-subagent
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/am-will/codex-skills --skill codex-subagent

简介

生成自治子代理以卸载上下文繁重的工作并保留父代币预算。

  • 子代理燃烧自己的代币并仅返回最终结果,非常适合深度研究(3 个以上搜索)、代码库探索(8 个以上文件)、多步骤工作流程和长时间运行的操作
  • 选择用于纯搜索任务的迷你模型 (gpt-5.1-codex-mini) 或继承父模型以进行多步骤分析、重构和生成工作
  • 通过使用 -o 进行基于文件的输出捕获,通过后台 shell 执行支持最多 5 个并行子代理
  • 参数或 JSONL 事件流解析
  • 需要遵循结构化模板的详细、上下文丰富的提示:任务上下文、具体目标、约束、输出格式和成功标准

SKILL.md

Codex Subagent Skill

Spawn autonomous subagents to offload context-heavy work. Subagents burn their own tokens, return only final results.

Golden Rule: If task + intermediate work would add 3,000+ tokens to parent context → use subagent.

Intelligent Prompting

Critical: Parent agent must provide subagent with essential context for success.

Good Prompting Principles

  1. Include relevant context - Give the subagent thorough context
  2. Be specific - Clear constraints, requirements, output format
  3. Provide direction - Where to look, what sources to prioritize
  4. Define success - What constitutes a complete answer

Examples

Bad: "Research authentication"

Good: "Research authentication in this Next.js codebase. Focus on: 1) Session management strategy (JWT vs session cookies), 2) Auth provider integration (NextAuth, Clerk, etc), 3) Protected route patterns. Check /app, /lib/auth, and middleware files. Return architecture summary with code examples."

Bad: "Search for Codex SDK"

Good: "Find the most recent Codex SDK documentation and summarize key updates. Focus on: 1) Installation/quickstart, 2) Core API methods and parameters, 3) Breaking changes or deprecations. Prioritize official OpenAI docs and release notes. Return a concise summary with citations."

Bad: "Find API endpoints"

Good: "Find all REST API endpoints in this Express.js app. Look in /routes, /api, and /controllers directories. For each endpoint document: method (GET/POST/etc), path, auth requirements, request/response schemas. Return as markdown table."

Prompting Template

[TASK CONTEXT]
You are researching/analyzing [SPECIFIC TOPIC] in [LOCATION/CODEBASE/DOMAIN].

[OBJECTIVES]
Your goals:
1. [1st objective with specifics]
2. [2nd objective]
3. [3rd objective if needed]

[CONSTRAINTS]
- Focus on: [specific areas/files/sources]
- Prioritize: [what matters most]
- Ignore: [what to skip]

[OUTPUT FORMAT]
Return: [exactly what format parent needs]

[SUCCESS CRITERIA]
Complete when: [specific conditions met]

Model Selection

Use Mini Model (gpt-5.1-codex-mini + medium)

Pure search only - no additional work after gathering info.

Bash (Linux/macOS)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m gpt-5.1-codex-mini -c 'model_reasoning_effort="medium"' \
  "Search web for [TOPIC] and summarize findings"

PowerShell (Windows)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m gpt-5.1-codex-mini -c 'model_reasoning_effort="medium"' `
  "Search web for [TOPIC] and summarize findings"

Inherit Parent Model + Reasoning

Multi-step workflows - search + analyze/refactor/generate:

Bash (Linux/macOS)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m "$MODEL" -c "model_reasoning_effort=\"$REASONING\"" \
  "Find auth files THEN analyze security patterns and propose improvements"

PowerShell (Windows)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m $MODEL -c "model_reasoning_effort=`"$REASONING`"" `
  "Find auth files THEN analyze security patterns and propose improvements"

Decision Logic

Is task PURELY search/gather?
├─ YES: Any work after gathering?
│  ├─ NO → mini model
│  └─ YES → inherit parent
└─ NO → inherit parent

Basic Usage

Bash (Linux/macOS)

# Get parent session settings (respects active profile; falls back to top-level)
# NOTE: codex-parent-settings.sh prints two lines; use mapfile to avoid empty REASONING.
mapfile -t _settings < <(scripts/codex-parent-settings.sh)
MODEL="${_settings[0]}"
REASONING="${_settings[1]}"

# Spawn subagent (inherit parent)
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m "$MODEL" -c "model_reasoning_effort=\"$REASONING\"" \
  "DETAILED_PROMPT_WITH_CONTEXT"

# Safer prompt construction (no backticks / command substitution)
PROMPT=$(cat <<'EOF'
[TASK CONTEXT]
You are analyzing /path/to/repo.

[OBJECTIVES]
1. Do X
2. Do Y

[OUTPUT FORMAT]
Return: path - purpose
EOF
)
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m "$MODEL" -c "model_reasoning_effort=\"$REASONING\"" \
  "$PROMPT"

# Pure search (use mini)
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m gpt-5.1-codex-mini -c 'model_reasoning_effort="medium"' \
  "SEARCH_ONLY_PROMPT"

# JSON output for parsing
codex exec --dangerously-bypass-approvals-and-sandbox --json "PROMPT" | jq -r 'select(.event=="turn.completed") | .content'

PowerShell (Windows)

# Get parent session settings (respects active profile; falls back to top-level)
$scriptPath = Join-Path $env:USERPROFILE ".codex\skills\codex-subagent\scripts\codex-parent-settings.ps1"
$settings = & $scriptPath
$MODEL = $settings[0]
$REASONING = $settings[1]

# Spawn subagent (inherit parent)
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m $MODEL -c "model_reasoning_effort=`"$REASONING`"" `
  "DETAILED_PROMPT_WITH_CONTEXT"

# Use here-string for multi-line prompts (avoids escaping issues)
$PROMPT = @'
[TASK CONTEXT]
You are analyzing /path/to/repo.

[OBJECTIVES]
1. Do X
2. Do Y

[OUTPUT FORMAT]
Return: path - purpose
'@

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m $MODEL -c "model_reasoning_effort=`"$REASONING`"" `
  $PROMPT

# Pure search (use mini)
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m gpt-5.1-codex-mini -c 'model_reasoning_effort="medium"' `
  "SEARCH_ONLY_PROMPT"

# Method 1 (Recommended): Use -o to output directly to file
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m $MODEL -c "model_reasoning_effort=`"$REASONING`"" `
  -o output.txt "PROMPT"
$content = Get-Content -Path output.txt -Raw

# Method 2: Parse JSONL event stream
$jsonl = codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --json "PROMPT"
$events = $jsonl -split "`n" | Where-Object { $_ } | ForEach-Object { $_ | ConvertFrom-Json }
$content = $events |
    Where-Object -Property type -EQ "item.completed" |
    Where-Object { $_.item.type -eq "agent_message" } |
    Select-Object -ExpandProperty item |
    Select-Object -ExpandProperty text

Parallel Subagents (Up to 5)

Spawn multiple subagents for independent tasks:

Bash (Linux/macOS)

# Research different topics simultaneously
codex exec --dangerously-bypass-approvals-and-sandbox -m "$MODEL" -c "model_reasoning_effort=\"$REASONING\"" "Research topic A..." &
codex exec --dangerously-bypass-approvals-and-sandbox -m "$MODEL" -c "model_reasoning_effort=\"$REASONING\"" "Research topic B..." &
wait

PowerShell (Windows)

Use PowerShell Jobs for parallel execution with -o to output to separate files:

# Parallel execution with file output
$job1 = Start-Job -ScriptBlock {
    param($m, $r, $out)
    codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
      -m $m -c "model_reasoning_effort=`"$r`"" -o $out "Research topic A..."
} -ArgumentList $MODEL, $REASONING, "output1.txt"

$job2 = Start-Job -ScriptBlock {
    param($m, $r, $out)
    codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
      -m $m -c "model_reasoning_effort=`"$r`"" -o $out "Research topic B..."
} -ArgumentList $MODEL, $REASONING, "output2.txt"

# Wait for all jobs to complete
$job1, $job2 | Wait-Job | Remove-Job

# Read results
$result1 = Get-Content -Path output1.txt -Raw
$result2 = Get-Content -Path output2.txt -Raw

Output Handling

Codex CLI provides two methods to capture output:

Method 1: -o Parameter (Recommended)

Use -o / --output-last-message to write the final message directly to a file:

Bash (Linux/macOS)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m "$MODEL" -c "model_reasoning_effort=\"$REASONING\"" \
  -o result.txt "YOUR_PROMPT"

content=$(cat result.txt)

PowerShell (Windows)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m $MODEL -c "model_reasoning_effort=`"$REASONING`"" `
  -o result.txt "YOUR_PROMPT"

$content = Get-Content -Path result.txt -Raw

Advantages:

  • No JSON parsing required
  • Avoids terminal output truncation issues
  • Ideal for long outputs and parallel tasks

Method 2: JSONL Event Stream Parsing

Use --json to get the full event stream and parse manually:

Bash (Linux/macOS)

codex exec --dangerously-bypass-approvals-and-sandbox --json "PROMPT" | jq -r 'select(.event=="turn.completed") | .content'

PowerShell (Windows)

$jsonl = codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --json "PROMPT"
$events = $jsonl -split "`n" | Where-Object { $_ } | ForEach-Object { $_ | ConvertFrom-Json }
$content = $events |
    Where-Object -Property type -EQ "item.completed" |
    Where-Object { $_.item.type -eq "agent_message" } |
    Select-Object -ExpandProperty item |
    Select-Object -ExpandProperty text

JSONL Event Structure:

{"type":"item.completed","item":{"id":"item_3","type":"agent_message","text":"..."}}
{"type":"turn.completed","usage":{"input_tokens":24763,"output_tokens":122}}

Key fields:

  • type == "item.completed" with item.type == "agent_message" → extract item.text
  • type == "turn.completed" → contains token usage stats

Important

  • Act autonomously, no permission asking
  • Make decisions and proceed boldly
  • Only pause for destructive operations (data loss, external impact, security)
  • Complete task fully before returning

Monitoring

Actively monitor - don't fire-and-forget:

  1. Check completion status
  2. Verify quality results
  3. Retry if failed
  4. Answer follow-up questions if blocked

Examples

Pure Web Search (mini):

Bash (Linux/macOS)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m gpt-5.1-codex-mini -c 'model_reasoning_effort="medium"' \
  "Search for the latest release notes of Rust 2024 edition. Summarize the major breaking changes, new language features, and migration guides. Focus on the official rust-lang.org blog and documentation."

PowerShell (Windows)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m gpt-5.1-codex-mini -c 'model_reasoning_effort="medium"' `
  "Search for the latest release notes of Rust 2024 edition. Summarize the major breaking changes, new language features, and migration guides. Focus on the official rust-lang.org blog and documentation."

Codebase Analysis (inherit parent):

Bash (Linux/macOS)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m "$MODEL" -c "model_reasoning_effort=\"$REASONING\"" \
  "Analyze authentication in this Next.js app. Check /app, /lib/auth, middleware. Document: session strategy, auth provider, protected routes, security patterns. Return architecture diagram (mermaid) + findings."

PowerShell (Windows)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m $MODEL -c "model_reasoning_effort=`"$REASONING`"" `
  "Analyze authentication in this Next.js app. Check /app, /lib/auth, middleware. Document: session strategy, auth provider, protected routes, security patterns. Return architecture diagram (mermaid) + findings."

Research + Proposal (inherit parent):

Bash (Linux/macOS)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
  -m "$MODEL" -c "model_reasoning_effort=\"$REASONING\"" \
  "Research WebGPU browser adoption (support tables, benchmarks, frameworks). THEN analyze feasibility for our React app. Consider: performance gains, browser compatibility, implementation effort. Return recommendation with pros/cons."

PowerShell (Windows)

codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
  -m $MODEL -c "model_reasoning_effort=`"$REASONING`"" `
  "Research WebGPU browser adoption (support tables, benchmarks, frameworks). THEN analyze feasibility for our React app. Consider: performance gains, browser compatibility, implementation effort. Return recommendation with pros/cons."

Config Reference

Parent settings: ~/.codex/config.toml

model = "gpt-5.2-codex"
model_reasoning_effort = "high"  # none | minimal | low | medium | high | xhigh
profile = "yolo"                 # optional; when set, profile values override top-level

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

31%
按下载量换算759

Claude Code

25.88%
按下载量换算633

OpenCode

17.99%
按下载量换算440

Gemini CLI

12.38%
按下载量换算303

Antigravity

7.22%
按下载量换算177

windsurf

3.71%
按下载量换算91

安全审计

Gen Agent Trust Hub

未通过

Socket

未通过

Snyk

未通过

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/am-will/codex-skills --skill codex-subagent;npx skills add am-will/codex-skills --skill "codex-subagent" 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 warning/failed 结果,不能写成本站确认安全。

来源信息

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