rhowardstone Writing Plans
Overview
Write comprehensive implementation plans assuming the executor has zero context for our project and questionable taste. Document everything they need to know: which files to touch for each task, code or protocols, testing or validation, docs they might need to check, how to verify it. Give them the whole plan as bite-sized tasks. DRY. YAGNI. Validation-first. Frequent checkpoints.
Assume they are skilled, but know almost nothing about our toolset or problem domain. Assume they don't know good test/validation design very well.
Announce at start: "I'm using the writing-plans skill to create the implementation plan."
Context: This should be run in a dedicated worktree (created by brainstorming skill).
Save plans to: docs/plans/YYYY-MM-DD-<feature-name>.md
Bite-Sized Task Granularity
Each step is one action (2-5 minutes):
For software:
- "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
For research/experiments:
- "Define the expected result and validation criteria" - step
- "Verify data/resources are available" - step
- "Execute the minimal procedure/analysis" - step
- "Check output against expected criteria" - step
- "Checkpoint (save state, commit notebook)" - step
Plan Document Header
Every plan MUST start with this header:
# [Feature/Experiment Name] Implementation Plan
> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
**Goal:** [One sentence describing what this builds/tests/answers]
**Architecture/Approach:** [2-3 sentences about approach]
**Tech Stack/Methods:** [Key technologies, libraries, methods, or protocols]
---
Task Structure
For Software Tasks:
### 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
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
git add tests/path/test.py src/path/file.py
git commit -m "feat: add specific feature"
**For Research/Experiment Tasks:**
```markdown
### Task N: [Analysis/Experiment Name]
**Resources:**
- Data: `exact/path/to/data.csv` or `data source description`
- Script: `analysis/exact/path/to/script.py`
- Output: `results/exact/path/to/output.csv`
- Notebook: `notebooks/exact/path/to/analysis.ipynb`
**Step 1: Define expected outcome**
Success criteria: [Specific measurable outcome]
- Expected range/value: [e.g., "p < 0.05", "AUC > 0.8", "correlation positive"]
- Failure looks like: [e.g., "no significant effect", "data quality issues"]
**Step 2: Verify prerequisites**
Check: Data exists and is valid
Run: `head -5 data/path/file.csv && wc -l data/path/file.csv`
Expected: [N rows, correct columns]
**Step 3: Execute analysis**
```python
# Minimal analysis code
df = pd.read_csv("data/path/file.csv")
result = statistical_test(df["column"])
print(f"Result: {result}")
Step 4: Validate result
Run analysis and check:
- Does result match expected criteria?
- Are there any warnings or anomalies?
- Is output in expected format?
Step 5: Checkpoint
git add analysis/script.py results/output.csv
git commit -m "analysis: complete [specific analysis]"
Or for notebooks: Save notebook, export key results
## Remember
- Exact file/data paths always
- Complete code/commands in plan (not "add validation")
- Exact commands with expected output
- Reference relevant skills with @ syntax
- DRY, YAGNI, validation-first, frequent checkpoints
- For experiments: include expected ranges and failure criteria
## Execution Handoff
After saving the plan, offer execution choice:
**"Plan complete and saved to `docs/plans/<filename>.md`. Two execution options:**
**1. Subagent-Driven (this session)** - I dispatch fresh subagent per task, review between tasks, fast iteration
**2. Parallel Session (separate)** - Open new session with executing-plans, batch execution with checkpoints
**Which approach?"**
**If Subagent-Driven chosen:**
- **REQUIRED SUB-SKILL:** Use superpowers:subagent-driven-development
- Stay in this session
- Fresh subagent per task + code review
**If Parallel Session chosen:**
- Guide them to open new session in worktree
- **REQUIRED SUB-SKILL:** New session uses superpowers:executing-plans