Structured AI development workflows that replace ad-hoc prompting with plan → execute → validate loops.
Based on MAP cognitive architecture (Nature Communications, 2025) — 74% improvement in planning tasks.
- Structured workflows — 11 specialized agents instead of single-prompt chaos
- Quality gates — automatic validation catches errors before they compound
- 40-60% cost savings — prevents circular reasoning and scope creep
- Learning system — captures patterns for reuse across projects
1. Install
pip install mapify-cli2. Initialize (in your project)
cd your-project
mapify init3. Start Claude Code and run your first workflow
claude/map-efficient implement user authentication with JWT tokens
You'll know it's working when: Claude spawns specialized agents (TaskDecomposer → Actor → Monitor) with structured output instead of freeform responses.
| Command | Use For |
|---|---|
/map-efficient |
Production features (recommended) |
/map-debug |
Bug fixes and debugging |
/map-review |
Pre-commit code review |
/map-fast |
Throwaway prototypes only |
MAP orchestrates specialized agents through slash commands:
TaskDecomposer → breaks goal into subtasks
↓
Actor → generates code
↓
Monitor → validates quality (loop if needed)
↓
Predictor → analyzes impact (for risky changes)
The orchestration lives in .claude/commands/map-*.md prompts created by mapify init.
| Guide | Description |
|---|---|
| Installation | All install methods, PATH setup, troubleshooting |
| Usage Guide | Workflows, examples, cost optimization, playbook |
| Architecture | Agents, MCP integration, customization |
- Command not found → Run
mapify initin your project first - Agent errors → Check
.claude/agents/has all 11.mdfiles - More help →
Improvements welcome: prompts for specific languages, new agents, CI/CD integrations.
MIT
MAP brings structure to AI-assisted development. Start with /map-efficient and see the difference.