Concise Planning

---
name: concise-planning-2ca1d4e31947
description: "Use when a user asks for a plan for a coding task, to generate a clear, actionable, and atomic checklist."
risk: unknown
source: community
date_added: "2026-02-27"
---

# 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

```markdown
# 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-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/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.

```bash
# 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:

```bash
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.

```bash
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.

<!-- AGI-INTEGRATION-END -->