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)

bash

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)

powershell

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)

bash

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)

powershell

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)

bash

# 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)

powershell

# 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)

bash

# 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:

powershell

# 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)

bash

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)

powershell

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)

bash

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

PowerShell (Windows)

powershell

$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:

jsonl

{"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:

Check completion status
Verify quality results
Retry if failed
Answer follow-up questions if blocked

Examples

Pure Web Search (mini):

Bash (Linux/macOS)

bash

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)

powershell

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)

bash

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)

powershell

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)

bash

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)

powershell

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

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

codex-subagent

NPX Install

Tags

SKILL.md Content

Codex Subagent Skill

Intelligent Prompting

Good Prompting Principles

Examples

Prompting Template

Model Selection

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

Bash (Linux/macOS)

PowerShell (Windows)

Inherit Parent Model + Reasoning

Bash (Linux/macOS)

PowerShell (Windows)

Decision Logic

Basic Usage

Bash (Linux/macOS)

PowerShell (Windows)

Parallel Subagents (Up to 5)

Bash (Linux/macOS)

PowerShell (Windows)

Output Handling

Method 1: -o Parameter (Recommended)

Bash (Linux/macOS)

PowerShell (Windows)

Method 2: JSONL Event Stream Parsing

Bash (Linux/macOS)

PowerShell (Windows)

Important

Monitoring

Examples

Bash (Linux/macOS)

PowerShell (Windows)

Bash (Linux/macOS)

PowerShell (Windows)

Bash (Linux/macOS)

PowerShell (Windows)

Config Reference