Invoking Codex CLI

Claude-only orchestration skill. This skill exists to let Claude delegate out to Codex CLI. It is excluded from any future shared multi-harness skill corpus.

Delegate tasks to Codex CLI using markdown files for instruction input and deliverable output. Write instructions to TASK.md, invoke Codex, then read OUTPUT.md for results.

Pattern: Markdown File Handoff

Claude Code                         Codex CLI
    |                                   |
    +-- Write TASK.md ------------------+
    |   (instructions + context)        |
    |                                   |
    +-- Execute: codex e "Read TASK.md, |
    |   follow instructions, write      |
    |   deliverables to OUTPUT.md"      |
    |                                   |
    |                                   +-- Reads TASK.md
    |                                   +-- Executes task
    |                                   +-- Writes OUTPUT.md
    |                                   |
    +-- Read OUTPUT.md <----------------+
    |   (deliverables + results)        |
    v                                   v

Benefits:

• Eliminates shell escaping issues (quotes, special characters, code blocks)
• Keeps context structured in reviewable files
• Enforces explicit deliverable format
• Supports session resume via thread ID

When to Use

Use this skill for:

• Complex implementation requiring multi-file changes
• Refactoring with dependency tracking
• Code generation with detailed specifications
• Tasks requiring extended thinking (20-30 minutes)

Model Selection

Model: Always use gpt-5.2-codex (latest model).

Reasoning Effort: Adjust based on task complexity.

Recommendation: Use default xhigh reasoning for most tasks. Lower reasoning effort for simpler tasks instead of switching to older models.

Invocation

Standard Pattern (Recommended)

codex e "Read TASK.md in the current directory. Follow the instructions exactly. Write all deliverables to OUTPUT.md." \
  -C "/path/to/project" \
  --full-auto \
  --skip-git-repo-check

With Lower Reasoning Effort

# For simpler tasks, reduce reasoning effort (not model)
codex e "Read TASK.md, follow instructions, write results to OUTPUT.md" \
  -C "/path/to/project" \
  -c model_reasoning_effort=medium \
  --full-auto \
  --skip-git-repo-check

Resume Session

codex e resume <thread_id> "Read TASK.md for updated instructions, append results to OUTPUT.md"

Core Flags Reference

Model: Always gpt-5.2-codex (latest). Adjust reasoning effort instead of changing models.

Task File Template (TASK.md)

# Task: [Brief Title]

## Objective
[Clear statement of what needs to be accomplished]

## Context
[Relevant background, constraints, requirements]

## Input Files
- `src/api/users.ts` - User service to modify
- `src/types/user.ts` - Type definitions

## Instructions
1. [First step]
2. [Second step]
3. [Third step]

## Deliverables
Write to OUTPUT.md:
- Summary of changes made
- List of files modified
- Any issues encountered
- Recommendations for follow-up
- Thread ID for session resume

## Constraints
- Do not modify files outside src/api/
- Maintain backward compatibility
- Follow existing code style

Output File Template (OUTPUT.md)

Instruct Codex to produce:

# Deliverables: [Task Title]

## Summary
[Brief description of what was done]

## Changes Made

### Files Modified
| File | Change |
|------|--------|
| `src/api/users.ts` | Added validation logic |

### Code Changes
[Key code snippets if relevant]

## Issues Encountered
- [Any problems and how resolved]

## Recommendations
- [Suggested follow-up actions]

## Session
Thread ID: `<thread_id>` (for resume)

Workflow Example (ras-commander)

1. Write TASK.md

# Task: Add Validation to Precipitation API

## Objective
Add depth conservation validation to precipitation methods.

## Context
The precipitation methods in ras_commander/precip/ need validation
to ensure depth conservation at 10^-6 precision.

## Input Files
- `ras_commander/precip/Atlas14Storm.py`
- `ras_commander/precip/StormGenerator.py`

## Instructions
1. Add depth conservation check after hyetograph generation
2. Raise ValidationError if conservation fails
3. Log successful validation with actual precision achieved

## Deliverables
Write to OUTPUT.md:
- Summary of validation logic added
- Files modified with line references
- Test cases to validate

## Constraints
- Use existing ValidationSeverity pattern
- Maintain backward compatibility

2. Execute Codex

codex e "Read TASK.md, follow the instructions, write deliverables to OUTPUT.md" \
  -C "C:/GH/ras-commander" \
  --full-auto \
  --skip-git-repo-check

3. Read OUTPUT.md

Parse the results, verify changes, and continue the workflow.

Environment Variables

CODEX_API_KEY=sk-xxx      # Required (or use codex login)
OPENAI_API_KEY=sk-xxx     # Alternative

Session Management

• Thread ID appears in Codex console output at session start
• Request thread ID in OUTPUT.md deliverables section
• Resume with: codex e resume <thread_id> "follow-up instruction"

Tips

1. Be explicit in TASK.md - Include all context, don't assume
2. Specify output structure - Tell Codex exactly what OUTPUT.md should contain
3. List input files - Explicitly name files Codex should read
4. Define constraints - Prevent unwanted modifications
5. Request thread ID - Include in deliverables for session resume
6. Use default model - gpt-5.2-codex is best for complex reasoning tasks

When to Escalate

Use Codex for:

• Implementation requiring extended thinking
• Multi-file refactoring
• Complex code generation
• Architecture planning

Use specialized ras-commander agents for:

• HDF analysis -> hdf-analyst
• Geometry parsing -> geometry-parser
• USGS integration -> usgs-integrator

Cross-References

Agents (delegate when needed):

• code-oracle-codex -- Delegate for deep code analysis with Codex

Skills (related workflows):

• qareviewtriple-model -- Legacy provider-mixed workflow; explicit user request only