Prompt file imported from drmoisan/TaskMaster (.github/prompts/orchestrate-python-work.prompt.md). Copyright stays with the author.

Python Orchestration Prompt

Name: orchestrate-python-work
Author: drmoisan

Use this prompt to run an end-to-end Python workflow through python-orchestrator.

Objective

Coordinate the user request from intake to completion using the correct path based on rough change budget.

Inputs

Request summary (required): clear objective and expected outcome
Likely affected files (optional): any known production/test files
Initial classification hint (optional): feature or bug
Constraints (optional): APIs/paths/behavior that must remain unchanged

Required orchestration behavior

Estimate rough change budget first (production Python files and test Python files).
If budget is 1–3 production Python files (+ corresponding tests):
- Delegate directly to python-typed-engineer for plan + implementation + QA closure.
If budget is >3 production Python files or >3 test Python files:
- Execute full large-path lifecycle:
  1. Scope and create potential entry (feature vs bug, short-name creation)
  2. Promote to GitHub issue and capture issue metadata
  3. Create branch and active feature folder
  4. Delegate research + spec/story completion
1. Delegate plan creation to python-atomic-planning (which delegates to atomic_planner, then validates preflight via atomic_executor until PREFLIGHT: ALL CLEAR)
2. After all-clear, delegate implementation + QA directly to python-atomic-executor
3. Delegate post-implementation feature review

Persistence and resume

Maintain orchestration state in artifacts/orchestration/python-orchestrator-state.json.
Resume from the next incomplete step if interrupted.
Do not end early while required downstream steps remain.

Output expectations

On completion, report:

Selected route (small or large)
Branch name (if created)
Key captured variables (promotion-type, short-name, issue-num, feature-folder when applicable)
Created/updated artifact paths
Final readiness summary and any remaining action items

# Python Orchestration Prompt Use this prompt to run an end-to-end Python workflow through `python-orchestrator`. ## Objective Coordinate the user request from intake to completion using the correct path based on rough change budget. ## Inputs - **Request summary (required):** clear objective and expected outcome - **Likely affected files (optional):** any known production/test files - **Initial classification hint (optional):** `feature` or `bug` - **Constraints (optional):** APIs/paths/behavior that must remain unchanged ## Required orchestration behavior 1. Estimate rough change budget first (production Python files and test Python files). 2. If budget is **1–3 production Python files** (+ corresponding tests): - Delegate directly to `python-typed-engineer` for plan + implementation + QA closure. 3. If budget is **>3 production Python files** or **>3 test Python files**: - Execute full large-path lifecycle: 1) Scope and create potential entry (`feature` vs `bug`, short-name creation) 2) Promote to GitHub issue and capture issue metadata 3) Create branch and active feature folder 4) Delegate research + spec/story completion 5) Delegate plan creation to `python-atomic-planning` (which delegates to `atomic_planner`, then validates preflight via `atomic_executor` until `PREFLIGHT: ALL CLEAR`) 6) After all-clear, delegate implementation + QA directly to `python-atomic-executor` 7) Delegate post-implementation feature review ## Persistence and resume - Maintain orchestration state in `artifacts/orchestration/python-orchestrator-state.json`. - Resume from the next incomplete step if interrupted. - Do not end early while required downstream steps remain. ## Output expectations On completion, report: - Selected route (`small` or `large`) - Branch name (if created) - Key captured variables (`promotion-type`, `short-name`, `issue-num`, `feature-folder` when applicable) - Created/updated artifact paths - Final readiness summary and any remaining action items

{ "prompt": "# Python Orchestration Prompt\n\nUse this prompt to run an end-to-end Python workflow through `python-orchestrator`.\n\n## Objective\n\nCoordinate the user request from intake to completion using the correct path based on rough change budget.\n\n## Inputs\n\n- **Request summary (required):** clear objective and expected outcome\n- **Likely affected files (optional):** any known production/test files\n- **Initial classification hint (optional):** `feature` or `bug`\n- **Constraints (optional):** APIs/paths/behavior that must remain unchanged\n\n## Required orchestration behavior\n\n1. Estimate rough change budget first (production Python files and test Python files).\n2. If budget is **1–3 production Python files** (+ corresponding tests):\n - Delegate directly to `python-typed-engineer` for plan + implementation + QA closure.\n3. If budget is **>3 production Python files** or **>3 test Python files**:\n - Execute full large-path lifecycle:\n 1) Scope and create potential entry (`feature` vs `bug`, short-name creation)\n 2) Promote to GitHub issue and capture issue metadata\n 3) Create branch and active feature folder\n 4) Delegate research + spec/story completion\n 5) Delegate plan creation to `python-atomic-planning` (which delegates to `atomic_planner`, then validates preflight via `atomic_executor` until `PREFLIGHT: ALL CLEAR`)\n 6) After all-clear, delegate implementation + QA directly to `python-atomic-executor`\n 7) Delegate post-implementation feature review\n\n## Persistence and resume\n\n- Maintain orchestration state in `artifacts/orchestration/python-orchestrator-state.json`.\n- Resume from the next incomplete step if interrupted.\n- Do not end early while required downstream steps remain.\n\n## Output expectations\n\nOn completion, report:\n\n- Selected route (`small` or `large`)\n- Branch name (if created)\n- Key captured variables (`promotion-type`, `short-name`, `issue-num`, `feature-folder` when applicable)\n- Created/updated artifact paths\n- Final readiness summary and any remaining action items", "variables": [], "notes": "Route a Python request through budget-based orchestration: direct small-scope delivery or full feature/bug lifecycle with promotion, research, plan/execution, and review.", "metadata": { "source": { "provider": "github-prompts", "repository": "https://github.com/drmoisan/TaskMaster", "path": ".github/prompts/orchestrate-python-work.prompt.md", "ref": "b97ea1a96fe0368aadf2ab166f78405eb0906c6f", "url": "https://github.com/drmoisan/TaskMaster/blob/b97ea1a96fe0368aadf2ab166f78405eb0906c6f/.github/prompts/orchestrate-python-work.prompt.md", "key": "drmoisan/TaskMaster/.github/prompts/orchestrate-python-work.prompt.md" } } }

orchestrate-python-work

About

Python Orchestration Prompt

Objective

Inputs

Required orchestration behavior

Persistence and resume

Output expectations

Use it

Manifest

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