# Blueprint

> Turn a one-line objective into a step-by-step construction plan any coding agent can execute cold. Each step has a self-contained context brief — a fresh agent in a new session can pick up any step without reading prior steps.

- Skill: `techwavedev/blueprint` (Agent Skill)
- Install (CLI): `npx skillmds@latest add techwavedev/blueprint`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/blueprint/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/blueprint

---


# Blueprint — Construction Plan Generator

Turn a one-line objective into a step-by-step plan any coding agent can execute cold.

## Overview

Blueprint is for multi-session, multi-agent engineering projects where each step must be independently executable by a fresh agent that has never seen the conversation history. Install it once, invoke it with `/blueprint <project> <objective>`.

## When to Use This Skill

- Use when the task requires multiple PRs or sessions
- Use when multiple agents or team members need to share execution
- Use when you want adversarial review of the plan before execution
- Use when parallel step detection and dependency graphs matter

## How It Works

1. **Research** — Scans the codebase, reads project memory, runs pre-flight checks
2. **Design** — Breaks the objective into one-PR-sized steps, identifies parallelism, assigns model tiers
3. **Draft** — Generates the plan from a structured template with branch workflow rules, CI policy, and rollback strategies inline
4. **Review** — Delegates adversarial review to a strongest-model sub-agent (falls back to default model if unavailable)
5. **Register** — Saves the plan and updates project memory

## Examples

### Example 1: Database migration
```
/blueprint myapp "migrate database to PostgreSQL"
```

### Example 2: Plugin extraction
```
/blueprint antbot "extract providers into plugins"
```

## Best Practices

- ✅ Use for tasks requiring 3+ PRs or multiple sessions
- ✅ Let Blueprint auto-detect git/gh availability — it degrades gracefully
- ❌ Don't invoke for tasks completable in a single PR
- ❌ Don't invoke when the user says "just do it"

## Key Differentiators

- **Cold-start execution**: Every step has a self-contained context brief
- **Adversarial review gate**: Strongest-model review before execution
- **Zero runtime risk**: Pure markdown — no hooks, no scripts, no executable code
- **Plan mutation protocol**: Steps can be split, inserted, skipped with audit trail

## Installation

```bash
mkdir -p ~/.claude/skills
git clone https://github.com/antbotlab/blueprint.git ~/.claude/skills/blueprint
```

## Additional Resources

- [GitHub Repository](https://github.com/antbotlab/blueprint)
- [Examples: small plan](https://github.com/antbotlab/blueprint/blob/main/examples/small-plan.md)
- [Examples: large plan](https://github.com/antbotlab/blueprint/blob/main/examples/large-plan.md)

---

<!-- 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 agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

```bash
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Blueprint"
```

### Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags blueprint ai-agents
```

### Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>
```

### Control Tower Integration

Register agents and tasks with the Control Tower (`execution/control_tower.py`) for centralized orchestration across machines and LLM providers.

### Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.

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