Skip to main content
ClaudeWave
ES, Spanish
Skill32.8k estrellas del repoactualizado 6d ago

planning

The planning skill creates and manages persistent markdown files organized across three documents (task_plan.md, findings.md, progress.md) to maintain focus and context during complex multi-step tasks. Use it when tackling research projects, features requiring more than five tool calls, or any structured work where progress tracking prevents goal drift and lost context across multiple phases.

Instalar en Claude Code
Copiar
git clone --depth 1 https://github.com/davila7/claude-code-templates /tmp/planning && cp -r /tmp/planning/cli-tool/components/skills/ai-maestro/planning ~/.claude/skills/planning
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# AI Maestro Planning

Solve the execution problem -- staying focused during complex, multi-step tasks. Uses persistent markdown files to track goals, findings, and progress so you never lose context. Part of the [AI Maestro](https://github.com/23blocks-OS/ai-maestro) suite.

## When to Use

- Multi-step tasks (3+ steps)
- Research projects
- Building features requiring >5 tool calls
- Any task where you might lose track of the goal

## The 3-File Pattern

Create in `docs_dev/` (or `$AIMAESTRO_PLANNING_DIR`):

| File | Purpose | Update When |
|------|---------|-------------|
| `task_plan.md` | Goals, phases, decisions, errors | After each phase |
| `findings.md` | Research, discoveries, resources | During research |
| `progress.md` | Session log, test results | Throughout session |

## Quick Start

```bash
PLAN_DIR="${AIMAESTRO_PLANNING_DIR:-docs_dev}"
mkdir -p "$PLAN_DIR"
```

Then create `task_plan.md` with:
```markdown
# Task: [Goal]

## Phases
- [ ] Phase 1: Research
- [ ] Phase 2: Design
- [ ] Phase 3: Implement
- [ ] Phase 4: Test

## Decisions
| Decision | Rationale | Date |
|----------|-----------|------|

## Errors Encountered
| Error | Attempt | Resolution |
|-------|---------|------------|
```

## The 6 Rules

1. **Create plan first** -- Never start complex work without `task_plan.md`
2. **Read before decide** -- Re-read the plan before any major decision
3. **Update after act** -- Mark phases complete, log what changed
4. **2-action rule** -- After every 2 search/browse operations, save findings to `findings.md`
5. **Log all errors** -- Every error goes in the plan with attempt number and resolution
6. **Never repeat failures** -- If an action failed, change your approach

## The 3-Strike Protocol

| Strike | Action |
|--------|--------|
| 1 | Diagnose root cause, apply targeted fix |
| 2 | Try a different approach entirely |
| 3 | Question assumptions, search for similar issues |
| After 3 | Escalate to user with all attempts documented |

## The 5-Question Reboot

Lost? Answer these from your planning files:

1. Where am I? (current phase in `task_plan.md`)
2. Where am I going? (remaining phases)
3. What's the goal? (goal section)
4. What have I learned? (`findings.md`)
5. What have I done? (`progress.md`)

## Full AI Maestro Experience

This skill works standalone with no dependencies. For the complete experience including **memory search**, **docs search**, **graph query**, **agent messaging**, and **agent management**, install the full [AI Maestro](https://github.com/23blocks-OS/ai-maestro) platform.
agent-evaluationSkill

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

agent-expertSubagent

Use this agent when creating specialized Claude Code agents for the claude-code-templates components system. Specializes in agent design, prompt engineering, domain expertise modeling, and agent best practices. Examples: <example>Context: User wants to create a new specialized agent. user: 'I need to create an agent that specializes in React performance optimization' assistant: 'I'll use the agent-expert agent to create a comprehensive React performance agent with proper domain expertise and practical examples' <commentary>Since the user needs to create a specialized agent, use the agent-expert agent for proper agent structure and implementation.</commentary></example> <example>Context: User needs help with agent prompt design. user: 'How do I create an agent that can handle both frontend and backend security?' assistant: 'Let me use the agent-expert agent to design a full-stack security agent with proper domain boundaries and expertise areas' <commentary>The user needs agent development help, so use the agent-expert agent.</commentary></example>

agent-managementSkill

Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.

agent-manager-skillSkill

Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.

agent-memory-mcpSkill

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

agent-memory-systemsSkill

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

agent-messagingSkill

Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an agent", "check agent inbox", "message another agent", "reply to a message", "notify an agent", or any inter-agent communication task.

agent-tool-builderSkill

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa