pengzhangzhi Writing Plans
Overview
Write comprehensive implementation plans assuming the engineer has zero context for our codebase and questionable taste. Document everything they need to know: which files to touch for each task, code, testing, docs they might need to check, how to test it. Give them the whole plan as bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.
Assume they are a skilled developer, but know almost nothing about our toolset or problem domain. Assume they don't know good test design very well.
Announce at start: "I'm using the writing-plans skill to create the implementation plan."
Context: If working in an isolated worktree, it should have been created via the superpowers-ml:using-git-worktrees skill at execution time.
Save plans to: docs/superpowers/plans/YYYY-MM-DD-<feature-name>.md
- (User preferences for plan location override this default)
Scope Check
If the spec covers multiple independent subsystems, it should have been broken into sub-project specs during brainstorming. If it wasn't, suggest breaking this into separate plans — one per subsystem. Each plan should produce working, testable software on its own.
File Structure
Before defining tasks, map out which files will be created or modified and what each one is responsible for. This is where decomposition decisions get locked in.
- Design units with clear boundaries and well-defined interfaces. Each file should have one clear responsibility.
- You reason best about code you can hold in context at once, and your edits are more reliable when files are focused. Prefer smaller, focused files over large ones that do too much.
- Files that change together should live together. Split by responsibility, not by technical layer.
- In existing codebases, follow established patterns. If the codebase uses large files, don't unilaterally restructure - but if a file you're modifying has grown unwieldy, including a split in the plan is reasonable.
This structure informs the task decomposition. Each task should produce self-contained changes that make sense independently.
Bite-Sized Task Granularity
Each step is one action (2-5 minutes):
- "Write the failing test" - step
- "Run it to make sure it fails" - step
- "Implement the minimal code to make the test pass" - step
- "Run the tests and make sure they pass" - step
- "Commit" - step
Plan Document Header
Every plan MUST start with this header:
# [Feature Name] Implementation Plan
> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers-ml:subagent-driven-development (recommended) or superpowers-ml:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** [One sentence describing what this builds]
**Architecture:** [2-3 sentences about approach]
**Tech Stack:** [Key technologies/libraries]
---
Task Structure
### Task N: [Component Name]
**Files:**
- Create: `exact/path/to/file.py`
- Modify: `exact/path/to/existing.py:123-145`
- Test: `tests/exact/path/to/test.py`
- [ ] **Step 1: Write the failing test**
```python
def test_specific_behavior():
result = function(input)
assert result == expected
```
- [ ] **Step 2: Run test to verify it fails**
Run: `pytest tests/path/test.py::test_name -v`
Expected: FAIL with "function not defined"
- [ ] **Step 3: Write minimal implementation**
```python
def function(input):
return expected
```
- [ ] **Step 4: Run test to verify it passes**
Run: `pytest tests/path/test.py::test_name -v`
Expected: PASS
- [ ] **Step 5: Commit**
```bash
git add tests/path/test.py src/path/file.py
git commit -m "feat: add specific feature"
```
No Placeholders
Every step must contain the actual content an engineer needs. These are plan failures — never write them:
- "TBD", "TODO", "implement later", "fill in details"
- "Add appropriate error handling" / "add validation" / "handle edge cases"
- "Write tests for the above" (without actual test code)
- "Similar to Task N" (repeat the code — the engineer may be reading tasks out of order)
- Steps that describe what to do without showing how (code blocks required for code steps)
- References to types, functions, or methods not defined in any task
Remember
- Exact file paths always
- Complete code in every step — if a step changes code, show the code
- Exact commands with expected output
- DRY, YAGNI, TDD, frequent commits
Self-Review
After writing the complete plan, look at the spec with fresh eyes and check the plan against it. This is a checklist you run yourself — not a subagent dispatch.
1. Spec coverage: Skim each section/requirement in the spec. Can you point to a task that implements it? List any gaps.
2. Placeholder scan: Search your plan for red flags — any of the patterns from the "No Placeholders" section above. Fix them.
3. Type consistency: Do the types, method signatures, and property names you used in later tasks match what you defined in earlier tasks? A function called clearLayers() in Task 3 but clearFullLayers() in Task 7 is a bug.
If you find issues, fix them inline. No need to re-review — just fix and move on. If you find a spec requirement with no task, add the task.
Execution Handoff
After saving the plan, offer execution choice:
"Plan complete and saved to docs/superpowers/plans/<filename>.md. Two execution options:
1. Subagent-Driven (recommended) - I dispatch a fresh subagent per task, review between tasks, fast iteration
2. Inline Execution - Execute tasks in this session using executing-plans, batch execution with checkpoints
Which approach?"
If Subagent-Driven chosen:
- REQUIRED SUB-SKILL: Use superpowers-ml:subagent-driven-development
- Fresh subagent per task + two-stage review
If Inline Execution chosen:
- REQUIRED SUB-SKILL: Use superpowers-ml:executing-plans
- Batch execution with checkpoints for review
ML research addendum
An ML plan that ends at "tests pass" is incomplete. A passing test shows the code PATH might run; it does not show the method works. Build a staged verification ladder into the plan as bite-sized tasks, and gate expensive cluster/GPU work behind the cheap rungs.
Keep four things separate — a plan task must say which it establishes, and never conflate them:
- Code correctness — the path runs (R1 static/import/config; R2 one-batch shape/dtype/device + forward/backward).
- Trainability — the model can learn (R3 tiny overfit).
- Cheap empirical signal — early read on the real setup (R4 launcher smoke run; R5 short pilot).
- Full empirical evidence — the locked primary metric on the real study (R6 full run).
Include only the rungs that fit the work. For every verification step, name the concrete ARTIFACT that proves the rung passed — a logged loss curve, a saved overfit checkpoint, a smoke-run log — not "verify it works". State the artifact in the step the way the base skill states expected command output.
Design the ladder with superpowers-ml:ml-feedback-ladder; pin the experiment card and locked metric with superpowers-ml:ml-experiment-design before defining tasks.