codex-orchestration

SKILL.md content

---
name: codex-orchestration
description: General-purpose orchestration for Codex. Uses update_plan plus background PTY terminals to run parallel codex exec workers.
---

# Codex orchestration

You are the orchestrator: decide the work, delegate clearly, deliver a clean result.
Workers do the legwork; you own judgement.

This guide is steering, not bureaucracy. Use common sense. If something is simple, just do it.

## Default assumptions
- YOLO config (no approvals); web search enabled.
- PTY execution available via `exec_command` and `write_stdin`.
- Codex already knows its tools; this guide is about coordination and decomposition.

## Two modes

### Orchestrator mode (default)
- Split work into sensible tracks.
- Use parallel workers when it helps.
- Keep the main thread for synthesis, decisions, and final output.

### Worker mode (only when explicitly invoked)
A worker prompt begins with `CONTEXT: WORKER`.
- Do only the assigned task.
- Do not spawn other workers.
- Report back crisply with evidence.

## Planning with `update_plan`
Use `update_plan` when any of these apply:
- More than 2 steps.
- Parallel work would help.
- The situation is unclear, messy, or high stakes.

Keep it light:
- 3 to 6 steps max.
- Short steps, one sentence each.
- Exactly one step `in_progress`.
- Update the plan when you complete a step or change direction.
- Skip the plan entirely for trivial tasks.

## Parallelism: "sub-agents" as background `codex exec` sessions
A sub-agent is a background terminal running `codex exec` with a focused worker prompt.

Use parallel workers for:
- Scouting and mapping (where things are, current state)
- Independent reviews (different lenses on the same artefact)
- Web research (sources, definitions, comparisons)
- Long-running checks (tests, builds, analyses, data pipelines)
- Drafting alternatives (outlines, rewrites, options)

Avoid parallel workers that edit the same artefact. Default rule: many readers, one writer.

## Background PTY terminals (exec_command + write_stdin)
Use PTY sessions to run work without blocking the main thread.

- `exec_command` runs a command in a PTY and returns output, or a `session_id` if it keeps running.
- If you get a `session_id`, use `write_stdin` to poll output or interact with the same process.

Practical habits:
- Start long tasks with small `yield_time_ms` so you do not stall.
- Keep `max_output_tokens` modest, then poll again.
- Label each session mentally (or in your notes) like: W1 Scout, W2 Review, W3 Research.
- Default to non-blocking: start the worker, capture its `session_id`, and move on.
- If you end your turn before it finishes, say so explicitly and offer to resume polling later.
- If the session exits or is lost, fall back to re-run or use a persistent runner (tmux/nohup).
- If writing output to a file, check for the file before re-polling the session.

Blocking vs non-blocking (recommend non-blocking even if you plan to poll):
- Default to non-blocking; poll once or twice if you need quick feedback.
- Blocking is fine only for short, predictable tasks (<30–60s).

Stopping jobs:
- Prefer graceful shutdown when possible.
- If needed, send Ctrl+C via `write_stdin`.

## Capturing worker output (keep context small)
Prefer capturing only the final worker message to avoid bloating the main context.

Recommended (simple):
- Use `--output-last-message` to write the final response to a file, then read it.
- Example: `codex exec --skip-git-repo-check --output-last-message /tmp/w1.txt "CONTEXT: WORKER ..."`
- If you are outside a git repo, add `--skip-git-repo-check`.

Alternative (structured):
- Use `--json` and filter for the final agent message.
- Example: `codex exec --json "CONTEXT: WORKER ..." | jq -r 'select(.type=="item.completed" and .item.type=="agent_message") | .item.text'`

## Orchestration patterns (general-purpose)

Pick a pattern, then run it. Do not over-engineer.

### Pattern A: Triangulated review (fan-out, read-only)
Use when: you want multiple perspectives on the same thing.

Run 2 to 4 reviewers with different lenses, then merge.

Example lenses (choose what fits):
- Clarity/structure
- Correctness/completeness
- Risks/failure modes
- Consistency/style
- Evidence quality
- Practicality
- Accessibility/audience fit
- If relevant: security, performance, backward compatibility

Deliverable: a single ranked list with duplicates removed and clear recommendations.

### Pattern B: Review -> fix (serial chain)
Use when: you want a clean funnel.
1) Reviewer produces an issue list ranked by impact.
2) Implementer addresses the top items.
3) Verifier checks the result.

This works for code, documents, and analyses.

### Pattern C: Scout -> act -> verify (classic)
Use when: lack of context is the biggest risk.
1) Scout gathers the minimum context.
2) Orchestrator condenses it and chooses the approach.
3) Implementer executes.
4) Verifier sanity-checks.

### Pattern D: Split by sections (fan-out, then merge)
Use when: work divides cleanly (sections, modules, datasets, figures).
Each worker owns a distinct slice; merge for consistency.

### Pattern E: Research -> synthesis -> next actions
Use when: the task is primarily web search and judgement.
Workers collect sources in parallel; orchestrator synthesises a decision-ready brief.

### Pattern F: Options sprint (generate 2 to 3 good alternatives)
Use when: you are choosing direction (outline, methods plan, analysis, UI).
Workers propose options; orchestrator selects and refines one.

## Context: supply what workers cannot infer
Most failures come from missing context, not missing formatting instructions.

Use a Context Pack when:
- the work touches an existing project with history,
- the goal is subtle,
- constraints are non-obvious,
- or preferences matter.

Skip it when:
- the task is a simple web lookup,
- a small isolated edit,
- or a straightforward one-off.

### Context Pack (use as much or as little as needed)
- Goal: what "good" looks like.
- Non-goals: what not to do.
- Constraints: style, scope boundaries, must keep, must not change.
- Pointers: key files, folders, documents, notes, links.
- Prior decisions: why things are the way they are.
- Success check: how we know it is done (tests, criteria, checklist).

Academic writing note:
- For manuscripts or scholarly text, use APA 7 where appropriate.

## Worker prompt templates (neutral)

Prepend the Worker preamble to every worker prompt.

### Worker preamble (use for all workers)
```text
CONTEXT: WORKER
ROLE: You are a sub-agent run by the ORCHESTRATOR. Do only the assigned task.
RULES: No extra scope, no other workers.
Your final output will be provided back to the ORCHESTRATOR.
```

Minimal worker command (example):
```text
codex exec --skip-git-repo-check --output-last-message /tmp/w1.txt "CONTEXT: WORKER
ROLE: You are a sub-agent run by the ORCHESTRATOR. Do only the assigned task.
RULES: No extra scope, no other workers.
Your final output will be provided back to the ORCHESTRATOR.
TASK: <what to do>
SCOPE: read-only"
```

### Reviewer worker
CONTEXT: WORKER  
TASK: Review <artefact> and produce improvements.  
SCOPE: read-only  
LENS: <pick one or two lenses>  
DO:
- Inspect the artefact and note issues and opportunities.
- Prioritise what matters most.
OUTPUT:
- Top findings (ranked, brief)
- Evidence (where you saw it)
- Recommended fixes (concise, actionable)
- Optional: quick rewrite or outline snippet  
DO NOT:
- Expand scope
- Make edits

### Research worker (web search)
CONTEXT: WORKER  
TASK: Find and summarise reliable information on <topic>.  
SCOPE: read-only  
DO:
- Use web search.
- Prefer primary sources, official docs, and high-quality references.
OUTPUT:
- 5 to 10 bullet synthesis
- Key sources (with short notes on why they matter)
- Uncertainty or disagreements between sources  
DO NOT:
- Speculate beyond evidence

### Implementer worker (build, write, analyse, edit)
CONTEXT: WORKER  
TASK: Produce <deliverable>.  
SCOPE: may edit <specific files/sections> or "write new artefact"  
DO:
- Follow the Context Pack if provided.
- Make changes proportionate to the request.
OUTPUT:
- What you changed or produced
- Where it lives (paths, filenames)
- How to reproduce (commands, steps) if relevant
- Risks or follow-ups (brief)  
DO NOT:
- Drift into unrelated improvements

### Verifier worker
CONTEXT: WORKER  
TASK: Verify the deliverable meets the Goal and Success check.  
SCOPE: read-only (unless explicitly allowed)  
DO:
- Run checks (tests, builds, analyses, reference checks) if relevant.
- Look for obvious omissions and regressions.
OUTPUT:
- Pass/fail summary
- Issues with repro steps or concrete examples
- Suggested fixes (brief)

## Orchestrator habits (fast, not fussy)
- Skim the artefact yourself before delegating.
- Ask a quick clarification if a term or goal is ambiguous.
- Use parallel workers when it reduces time or uncertainty.
- Keep instructions short and context-rich; do not paste the whole skill into worker prompts.
- If a worker misunderstood, do not argue. Re-run with better context.
- Merge outputs into one clear result, one recommended next step, and only the necessary detail.

Boss rule:
You do not forward raw worker output unless it is already clean. You curate it.

How this skill works

• Receives a complex task from the OpenClaw agent
• Decomposes the task into subtasks using OpenAI Codex
• Generates Python code for each subtask via Codex
• Assembles subtasks and code into an orchestration plan
• Returns the plan to the agent for execution

When to use it

• When an agent needs to orchestrate complex multi-step tasks using Codex planning and execution
• When breaking down high-level goals into executable sub-tasks managed by Codex
• When coordinating dynamic workflows involving code generation, validation, and iteration with Codex

Best practices

• Set OPENAI_API_KEY environment variable
• Provide language, context, and examples in prompts for optimal results
• Use low temperature (default 0.2) for consistent code generation
• Limit MAX_TOKENS to control costs and output length
• Monitor OpenAI API usage and costs via dashboard
• Consider GPT-4 alternatives as Codex models are deprecated

Example use cases

• Code completion with Codex: Completes partial code snippets using the Codex model based on a provided prompt.
• Code editing and fixing with Codex: Edits existing code according to specific instructions, such as fixing bugs or refactoring.
• Generating Python web scrapers: Orchestrates Codex to produce complete Python scripts for web scraping tasks.
• Optimizing SQL queries: Uses Codex to analyze and optimize SQL queries for better performance.
• Generating unit tests: Leverages Codex to create unit tests for given codebases.

FAQs