
codex-subagent
Spawn Codex subagents via background shell to offload context-heavy work. Use for: deep research (3+ searches), codebase exploration (8+ files), multi-step workflows, exploratory tasks, long-running operations, documentation generation, or any other task where the intermediate steps will use large numbers of tokens.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/am-will/codex-skills --skill codex-subagent- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads codex-subagent automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- Include relevant context - Give the subagent thorough context
- Be specific - Clear constraints, requirements, output format
- Provide direction - Where to look, what sources to prioritize
- 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 { $_.item.type -eq "agent_message" } |
Select-Object -ExpandProperty item |
Select-Object -ExpandProperty text
JSONL Event Structure:
{"type":"item.completed","item":{"id":"item_3","type":"agent_message","text":"..."}}
{"type":"turn.completed","usage":{"input_tokens":24763,"output_tokens":122}}
Key fields:
type == "item.completed"withitem.type == "agent_message"→ extractitem.texttype == "turn.completed"→ contains token usage stats
Important
- Act autonomously, no permission asking
- Make decisions and proceed boldly
- Only pause for destructive operations (data loss, external impact, security)
- Complete task fully before returning
Monitoring
Actively monitor - don't fire-and-forget:
- Check completion status
- Verify quality results
- Retry if failed
- Answer follow-up questions if blocked
Examples
Pure Web Search (mini):
Bash (Linux/macOS)
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
-m gpt-5.1-codex-mini -c 'model_reasoning_effort="medium"' \
"Search for the latest release notes of Rust 2024 edition. Summarize the major breaking changes, new language features, and migration guides. Focus on the official rust-lang.org blog and documentation."
PowerShell (Windows)
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check `
-m gpt-5.1-codex-mini -c 'model_reasoning_effort="medium"' `
"Search for the latest release notes of Rust 2024 edition. Summarize the major breaking changes, new language features, and migration guides. Focus on the official rust-lang.org blog and documentation."
Codebase Analysis (inherit parent):
Bash (Linux/macOS)
codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check \
-m "$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
FAQ
What does the codex-subagent skill do?
Spawn Codex subagents via background shell to offload context-heavy work. Use for: deep research (3+ searches), codebase exploration (8+ files), multi-step workflows, exploratory tasks, long-running operations, documentation generation, or any other task where the intermediate steps will use large numbers of tokens.
How do I install codex-subagent?
Run npx skills add https://github.com/am-will/codex-skills --skill codex-subagent in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/codex-subagent/ (personal) or .claude/skills/codex-subagent/ (project).
Which AI agents can use codex-subagent?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is codex-subagent safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains codex-subagent?
It is published by am-will in am-will/codex-skills. It has 309 recorded installs.
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