techwavedev 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: This should be run in a dedicated worktree (created by brainstorming skill).
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: Use superpowers:subagent-driven-development (if subagents available) or superpowers:executing-plans to implement this plan. 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"
```
Remember
- Exact file paths always
- Complete code in plan (not "add validation")
- Exact commands with expected output
- Reference relevant skills with @ syntax
- DRY, YAGNI, TDD, frequent commits
Plan Review Loop
After completing each chunk of the plan:
- Dispatch plan-document-reviewer subagent (see plan-document-reviewer-prompt.md) for the current chunk
- Provide: chunk content, path to spec document
- If ❌ Issues Found:
- Fix the issues in the chunk
- Re-dispatch reviewer for that chunk
- Repeat until ✅ Approved
- If ✅ Approved: proceed to next chunk (or execution handoff if last chunk)
Chunk boundaries: Use ## Chunk N: <name> headings to delimit chunks. Each chunk should be ≤1000 lines and logically self-contained.
Review loop guidance:
- Same agent that wrote the plan fixes it (preserves context)
- If loop exceeds 5 iterations, surface to human for guidance
- Reviewers are advisory - explain disagreements if you believe feedback is incorrect
Execution Handoff
After saving the plan:
"Plan complete and saved to docs/superpowers/plans/<filename>.md. Ready to execute?"
Execution path depends on harness capabilities:
If harness has subagents (Claude Code, etc.):
- REQUIRED: Use superpowers:subagent-driven-development
- Do NOT offer a choice - subagent-driven is the standard approach
- Fresh subagent per task + two-stage review
If harness does NOT have subagents:
- Execute plan in current session using superpowers:executing-plans
- Batch execution with checkpoints for review
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior Architecture Decision Records (ADRs), trade-off analyses, and system design rationale. Critical for maintaining consistency across long-running projects.
# Check for prior architecture/design context before starting
python3 execution/memory_manager.py auto --query "architecture decisions and trade-off analysis for Writing Plans"
Storing Results
After completing work, store architecture/design decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Architecture: event-driven microservices with CQRS, Pulsar for messaging, Qdrant for semantic search" \
--type decision --project <project> \
--tags writing-plans architecture
Multi-Agent Collaboration
Broadcast architecture decisions to ALL agents so implementation stays aligned with the chosen patterns.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Completed architecture review — ADR documented, trade-offs analyzed, team aligned" \
--project <project>
Control Tower Coordination
Register architecture tasks in the Control Tower so all agents across machines know the current system design and constraints.