task-orchestration
Execute repo work one task at a time using a strict plan → execute → iterate loop tracked in .copilot-todo.yaml.
Best use case
task-orchestration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Execute repo work one task at a time using a strict plan → execute → iterate loop tracked in .copilot-todo.yaml.
Teams using task-orchestration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/task-orchestration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How task-orchestration Compares
| Feature / Agent | task-orchestration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Execute repo work one task at a time using a strict plan → execute → iterate loop tracked in .copilot-todo.yaml.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Task Orchestration Use this skill to drive work through a **single authoritative YAML todo file**: `.copilot-todo.yaml`. ## Source of truth - `.copilot-todo.yaml` — **only** status tracker. - `.copilot-todo.md` — legacy/migration only (do not update by hand). ## Getting Started with Templates ### Creating a new plan from template ```bash # Copy the template to your repo root or session folder cp .github/skills/task-orchestration/template/plan.md.template plan.md # Edit plan.md with your tasks (use Markdown checkboxes: - [ ] Task name) # Generate .copilot-todo.yaml from your plan python3 .github/skills/task-orchestration/scripts/task_orchestrator.py \ --plan-md plan.md \ --plan-mode regen \ init ``` ### Creating a .copilot-todo.yaml from scratch ```bash # Copy the template cp .github/skills/task-orchestration/template/copilot-todo.yaml.template .copilot-todo.yaml # Edit the tasks section, then initialize python3 .github/skills/task-orchestration/scripts/task_orchestrator.py init ``` **Template locations:** - `template/plan.md.template` — Markdown plan with task checkboxes - `template/copilot-todo.yaml.template` — YAML structure with example task ## Loop (plan → execute → iterate) Repeat until no runnable tasks remain: 1) Sync / migrate state - Reuse existing `.copilot-todo.yaml` (default): ```bash python3 .github/skills/task-orchestration/scripts/task_orchestrator.py init ``` - (Optional) Seed/regenerate tasks from `plan.md` using **Markdown task list items** (`- [ ] ...` / `- [x] ...`). - If an item starts with an explicit ID prefix like `A1. ...` or `T-001: ...`, that ID is used. - Otherwise a stable-ish `P-XXXXXXXX` ID is generated from the text. ```bash python3 .github/skills/task-orchestration/scripts/task_orchestrator.py \ --plan-md plan.md \ --plan-mode regen \ init ``` 2) Start next task ```bash python3 .github/skills/task-orchestration/scripts/task_orchestrator.py next ``` 3) Plan (short bullets, only for the chosen task) 4) Execute (minimal diff; run smallest relevant existing build/test) 5) Update status ```bash python3 .github/skills/task-orchestration/scripts/task_orchestrator.py update <ID> completed --note "..." # or python3 .github/skills/task-orchestration/scripts/task_orchestrator.py update <ID> blocked --note "..." ``` 5b) Commit (required when using `ralph_loop.py --require-commit`) ```bash git add -A git commit -m "<ID>: <short summary>" ``` 6) Iterate / expand If you discover missing work, **add follow-up tasks** (don’t silently expand scope): ```bash python3 .github/skills/task-orchestration/scripts/task_orchestrator.py add "Title" --deps <ID1,ID2> --priority Medium --goal "..." ``` ## Ralph-style multi-session loop (Copilot CLI) To run **one fresh Copilot run per task** (uses `copilot -p`, consuming Copilot requests accordingly): ```bash python3 .github/skills/task-orchestration/scripts/ralph_loop.py \ --non-interactive \ --max-steps 10 # or: derive tasks from plan.md python3 .github/skills/task-orchestration/scripts/ralph_loop.py \ --non-interactive \ --plan-md plan.md \ --plan-mode regen \ --max-steps 10 ``` Notes: - This does **not** bypass quotas; it just splits work into multiple sessions. - By default `ralph_loop.py` enforces **one commit per finished task** (use `--no-require-commit` to disable). - Keep `--max-steps` small to avoid burning requests if a task gets stuck.
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