techwavedev Concise Planning
Goal
Turn a user request into a single, actionable plan with atomic steps.
Workflow
1. Scan Context
- Read
README.md, docs, and relevant code files. - Identify constraints (language, frameworks, tests).
2. Minimal Interaction
- Ask at most 1–2 questions and only if truly blocking.
- Make reasonable assumptions for non-blocking unknowns.
3. Generate Plan
Use the following structure:
- Approach: 1-3 sentences on what and why.
- Scope: Bullet points for "In" and "Out".
- Action Items: A list of 6-10 atomic, ordered tasks (Verb-first).
- Validation: At least one item for testing.
Plan Template
# Plan
<High-level approach>
## Scope
- In:
- Out:
## Action Items
[ ] <Step 1: Discovery>
[ ] <Step 2: Implementation>
[ ] <Step 3: Implementation>
[ ] <Step 4: Validation/Testing>
[ ] <Step 5: Rollout/Commit>
## Open Questions
- <Question 1 (max 3)>
Checklist Guidelines
- Atomic: Each step should be a single logical unit of work.
- Verb-first: "Add...", "Refactor...", "Verify...".
- Concrete: Name specific files or modules when possible.
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
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 Concise Planning"
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 concise-planning 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.