Codex Issue Coordinator logo

Codex Issue Coordinator

Community
owainlewis
codex-issue-coordinator

Coordinates a large batch of GitHub issues through separate Codex worker threads, tested pull requests, review loops, and gated merges. Use when the user asks one Codex thread to manage several coding sessions or complete a parent issue, milestone, or issue batch.

Overview

Publisherowainlewis
Repositoryblueprint
Skill namecodex-issue-coordinator
Stars
419
Forks
73
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by owainlewis on GitHub. Read the source before you install it.

Installation

Install the Codex Issue Coordinator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/owainlewis/blueprint/tree/main/skills/codex-issue-coordinator
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/owainlewis/blueprint.git /tmp/blueprint
mkdir -p .claude/skills
cp -r /tmp/blueprint/skills/codex-issue-coordinator .claude/skills/codex-issue-coordinator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codex Issue Coordinator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Codex Issue Coordinator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Codex Issue Coordinator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Codex issue coordinator

Use one Codex thread to coordinate a GitHub issue batch. Give each active issue its own worker thread, worktree, branch, and pull request. Workers hold implementation context. GitHub holds dependency state.

Explicitly naming /codex-issue-coordinator, or explicitly telling the coordinator that agents may merge, authorizes in-scope workers to merge their own pull requests after every merge gate below passes. An implicit skill match does not grant merge authority: run in no-merge mode unless the user's words grant it. Neither mode authorizes deployment, release publication, destructive operations, branch-protection bypasses, or changes outside the supplied batch.

Read required skills directly from their SKILL.md files as instructions for this workflow. Do not invoke them through a skill command that the client blocks.

Preconditions

  1. Read the repository instructions, parent issue, native sub-issues, declared dependencies, project board, issue state, and linked open or merged pull requests. A merged pull request is only evidence of completion when it closes or explicitly references the issue and its final change and proof satisfy the issue scope. Reconcile that issue and project state instead of dispatching it again. Do not infer completion from a link alone.
  2. Use Codex thread tools. If visible Codex threads and Codex-managed worktrees are unavailable, stop. Do not replace workers with hidden subagents.
  3. Make every issue a complete task before dispatch. If a missing product or technical decision could change behavior, data, security, compatibility, operations, cost, or proof, mark that issue as needing human input.
  4. Build a dependency graph. Treat GitHub as the durable source of issue, pull request, review, and merge state. Stop for human correction if the graph contains a dependency cycle. Do not create a second local tracker.
  5. Default to two active workers. Use another limit only when the user asks or the repository gives a stricter limit.

Coordinator workflow

  1. Keep the calling thread as coordinator. Name it #<parent> Coordinator when the batch has a parent issue. Use set_thread_pinned to pin it at the start of every new or resumed coordinator run, before inspecting or dispatching workers.
  2. Reuse an existing active worker for the same issue. Inspect an unclear or interrupted worker before creating another. If the issue already has an open pull request, create or resume its worker on that pull request's exact head branch and include the branch and pull request URL in its prompt. Otherwise create a Codex project thread in a managed worktree from the latest remote default branch and include the required new branch name.
  3. Name every worker #<issue-number> <short title>.
  4. Start only issues whose prerequisites are merged. Independent issues may run together up to the worker limit. Do not stack dependent branches in this workflow.
  5. Give the worker the full issue URL, dependency state, repository location, resolved branch and pull request state, and the worker contract below. Tell it to use /task-to-pr and pass the coordinator's explicit merge or no-merge mode. Merge mode grants authority only for that worker's issue.
  6. Monitor workers with compact wait_threads snapshots. Use read_thread only when a worker needs help. Send decisions or corrected scope back with send_message_to_thread; do not edit the worker's files from the coordinator.
  7. Continue independent work when one issue is blocked. After three failed attempts at the same check or review finding, pause that issue, record the evidence on GitHub, and request human input.
  8. In merge mode, when a worker reports completion, verify GitHub says the pull request is merged, the issue contains final proof, and the issue is closed. Close it explicitly if the merged pull request did not close it. Then mark the issue Done when the project supports it and archive the worker thread. In no-merge mode, stop that worker after its pull request passes all agent-completable gates, leave the issue in Review, record any pending human approval or merge as its blocker, and leave the thread available.
  9. Refresh the dependency graph after every merge and dispatch newly ready issues from the updated remote default branch. In no-merge mode, when a dependent issue is waiting only for a passing prerequisite pull request to be merged, record that human-merge dependency as its blocker. Do not wait indefinitely or dispatch the dependent from an unmerged branch.
  10. In merge mode, finish only when every in-scope issue is merged, closed, and Done when the project supports that state, or every unfinished issue has a recorded blocker that needs human action. In no-merge mode, finish when every in-scope issue has a ready pull request with all agent-completable gates passing or a recorded human blocker.

Worker contract

Each worker owns one issue and follows this order:

  1. Read the issue, repository instructions, relevant spec, code, tests, and dependency pull requests. Move the issue to In Progress when possible.
  2. Use the Codex-managed worktree. If the issue has an open pull request, reuse its exact head branch and pull request. Otherwise create the repository's required issue-linked branch from the updated default branch.
  3. Implement only the issue. Add or update tests for every acceptance criterion, affected failure path, regression risk, and named edge case.
  4. Use /test. Browser-facing work requires a real browser check of the affected success and failure flows, responsive widths, keyboard behavior, console errors, and failed requests when relevant.
  5. Commit and push the tested change. Open a draft pull request if one is not already open. Include a short summary and current proof.
  6. Mark the pull request ready for review and move the issue to Review. Do this before requesting independent review or waiting for GitHub review.
  7. Use /review with a fresh subagent that did not implement the change. Wait for required CI and automated review. In merge mode, also wait for every repository-required approval. In no-merge mode, do not wait indefinitely for human feedback; record a pending required approval as a human blocker.
  8. Address every valid in-scope finding. Reply to review threads with the fix or evidence for making no change. Resolve a thread only when it is fully addressed.
  9. After any code change, repeat affected /test proof and fresh /review, commit and push, update the pull request evidence, and wait for CI and automated review again. Iterate until the mode's gates pass; do not manufacture extra rounds or wait indefinitely for optional human feedback.
  10. In merge mode, merge the pull request using the repository's preferred method after every merge gate passes. Never use an admin bypass. Wait until GitHub reports the pull request as merged, record final proof, and verify the issue is closed. Close it explicitly if the pull request did not. In no-merge mode, leave the passing pull request open for a human to merge. Report the final state to the coordinator.

Merge gates

A worker may merge only when all of these are true:

  • The pull request is ready for review, not draft.
  • The complete issue scope and acceptance criteria have recorded proof.
  • Focused and required wider tests pass on the final commit.
  • A fresh /review verdict is Approve on the final code.
  • Every required GitHub check passes.
  • Every actionable automated or human review thread is resolved.
  • Every repository-required approval is present.
  • The pull request is mergeable and current with its required base.
  • No security, data-loss, compatibility, operational, or product decision is unresolved.
  • The pull request changes only the worker's issue.

If a gate cannot pass, do not merge. Record the blocker and continue other independent work.

Worker prompt

Use this shape and replace every placeholder:

text
Use /task-to-pr to complete <issue URL> in this Codex-managed worktree.

Your thread owns only this issue, branch, worktree, and pull request.
Branch state: <reuse PR head branch and PR URL, or create new branch name>.
Delivery mode: <merge after all gates pass, or leave the passing PR open>.
Read the issue and repository instructions as the source of truth.
Implement the smallest complete change and prove acceptance criteria, edge
cases, failure paths, and regressions.

After local proof passes, commit, push, open or update the pull request, and
mark it ready for review. Only then run fresh independent /review and wait for
CI and GitHub review. Address valid findings and repeat test and review after
every code change.

<If merge mode: The user explicitly authorized this /codex-issue-coordinator run to
merge this issue's pull request after every merge gate passes. Wait until
GitHub reports the merge, update and close the issue, and return the result.>
<If no-merge mode: Leave the passing pull request open for human merge and
return its URL and proof.>
Do not deploy, publish a release, bypass repository rules, or change unrelated
work.

Return

For each issue, report its worker, pull request, gate state, and blocker. State which issues are complete and which need human action. Do not repeat worker logs that do not change the result.

Boundaries

  • Use visible Codex threads for workers. Fresh subagents are only for the independent /review inside a worker.
  • Never dispatch two workers for one issue or let two workers share a worktree.
  • Never replace or duplicate an existing pull request for the issue. Resume it.
  • Reconcile already-merged pull requests and closed issues before dispatch, but only after their change and proof satisfy the issue scope.
  • Never start dependent work from an unmerged pull request in this workflow.
  • Never merge an unrelated pull request merely because it blocks the batch.
  • Never infer deployment or release authority from merge authority.
  • Do not archive a worker until its merge and final issue state are verified.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Codex Issue Coordinator AI skill do?

Coordinates a large batch of GitHub issues through separate Codex worker threads, tested pull requests, review loops, and gated merges. Use when the user asks one Codex thread to manage several coding sessions or complete a parent issue, milestone, or issue batch.

Why use Codex Issue Coordinator on TypingMind?

Because you install it once and use it with any model. Codex Issue Coordinator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Codex Issue Coordinator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/owainlewis/blueprint/tree/main/skills/codex-issue-coordinator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Codex Issue Coordinator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Codex Issue Coordinator?

As many as you like. As long as a model supports skills, you can use Codex Issue Coordinator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Codex Issue Coordinator AI skill free?

Yes. It is published on GitHub by owainlewis under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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