make-plan

Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.

46,120 stars

Best use case

make-plan is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.

Teams using make-plan should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/make-plan/SKILL.md --create-dirs "https://raw.githubusercontent.com/thedotmack/claude-mem/main/openclaw/skills/make-plan/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/make-plan/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How make-plan Compares

Feature / Agentmake-planStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Make Plan

You are an ORCHESTRATOR. Create an LLM-friendly plan in phases that can be executed consecutively in new chat contexts.

## Delegation Model

Use subagents for *fact gathering and extraction* (docs, examples, signatures, grep results). Keep *synthesis and plan authoring* with the orchestrator (phase boundaries, task framing, final wording). If a subagent report is incomplete or lacks evidence, re-check with targeted reads/greps before finalizing.

### Subagent Reporting Contract (MANDATORY)

Each subagent response must include:
1. Sources consulted (files/URLs) and what was read
2. Concrete findings (exact API names/signatures; exact file paths/locations)
3. Copy-ready snippet locations (example files/sections to copy)
4. "Confidence" note + known gaps (what might still be missing)

Reject and redeploy the subagent if it reports conclusions without sources.

## Plan Structure

### Phase 0: Documentation Discovery (ALWAYS FIRST)

Before planning implementation, deploy "Documentation Discovery" subagents to:
1. Search for and read relevant documentation, examples, and existing patterns
2. Identify the actual APIs, methods, and signatures available (not assumed)
3. Create a brief "Allowed APIs" list citing specific documentation sources
4. Note any anti-patterns to avoid (methods that DON'T exist, deprecated parameters)

The orchestrator consolidates findings into a single Phase 0 output.

### Each Implementation Phase Must Include

1. **What to implement** — Frame tasks to COPY from docs, not transform existing code
   - Good: "Copy the V2 session pattern from docs/examples.ts:45-60"
   - Bad: "Migrate the existing code to V2"
2. **Documentation references** — Cite specific files/lines for patterns to follow
3. **Verification checklist** — How to prove this phase worked (tests, grep checks)
4. **Anti-pattern guards** — What NOT to do (invented APIs, undocumented params)

### Final Phase: Verification

1. Verify all implementations match documentation
2. Check for anti-patterns (grep for known bad patterns)
3. Run tests to confirm functionality

## Key Principles

- Documentation Availability ≠ Usage: Explicitly require reading docs
- Task Framing Matters: Direct agents to docs, not just outcomes
- Verify > Assume: Require proof, not assumptions about APIs
- Session Boundaries: Each phase should be self-contained with its own doc references

## Anti-Patterns to Prevent

- Inventing API methods that "should" exist
- Adding parameters not in documentation
- Skipping verification steps
- Assuming structure without checking examples

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