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Swarm Planner

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am-will
swarm-planner

[EXPLICIT INVOCATION ONLY] Creates dependency-aware implementation plans optimized for parallel multi-agent execution.

Overview

Publisheram-will
Repositorycodex-skills
Skill nameswarm-planner
Stars
1K
Forks
60
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by am-will on GitHub. Read the source before you install it.

Installation

Install the Swarm Planner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/am-will/codex-skills.git /tmp/codex-skills
mkdir -p .claude/skills
cp -r /tmp/codex-skills/skills/swarm-planner .claude/skills/swarm-planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Swarm Planner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Swarm Planner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Swarm Planner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Swarm-Ready Planner

Create implementation plans with explicit task dependencies optimized for parallel agent execution. This skill can be ran inside or outside of Plan Mode.

Core Principles

  1. Explore Codebase: Investigate architecture, patterns, existing implementations, dependencies, and frameworks in use.
  2. Fresh Documentation First: Use Context7 for ANY external library, framework, or API before planning tasks
  3. Ask Questions: Clarify ambiguities and seek clarification on scope, constraints, or priorities throughout the planning process. At any time.
  4. Explicit Dependencies: Every task declares what it depends on, enabling maximum parallelization
  5. Atomic Tasks: Each task is independently executable by a single agent
  6. Review Before Yield: A subagent reviews the plan for gaps before finalizing

Process

1. Research

Codebase investigation:

  • Architecture, patterns, existing implementations
  • Dependencies and frameworks in use

1a. Optional: Stop to Clarification Questions

  • If the architecture is unclear or missing STOP AND YIELD to the user, and request user input (AskUserQuestions) before moving on. Always offer recommendations for clarification questions.
  • If architecture is present, skip 1a and move onto next step.

2. Documentation

Documentation retrieval (REQUIRED for external dependencies):

Use Context7 skill or MCP to fetch current docs for any libraries/frameworks or APIs that are or will be used in project. If Context7 is not available, use web search.

This ensures version-accurate APIs, correct parameters, and current best practices.

3. STOP and Request User Input

When anything is unclear or could reasonably be done multiple ways:

  • Stop and ask clarifying questions immediately
  • Do not make assumptions about scope, constraints, or priorities
  • Questions should reduce risk and eliminate ambiguity
  • Always offer recommendations for clarification questions.
  • Use request_user_input or AskUserQuestion tool if available.

4. Create Dependency-Aware Plan

Structure the plan with explicit task dependencies using this format:

Task Dependency Format

Each task MUST include:

  • id: Unique identifier (e.g., T1, T2.1)
  • depends_on: Array of task IDs that must complete first (empty [] for root tasks)
  • description: What the task accomplishes
  • location: File paths involved
  • validation: acceptance criteria

Example:

T1: [depends_on: []] Create database schema migration
T2: [depends_on: []] Install required packages
T3: [depends_on: [T1]] Create repository layer
T4: [depends_on: [T1]] Create service interfaces
T5: [depends_on: [T3, T4]] Implement business logic
T6: [depends_on: [T2, T5]] Add API endpoints
T7: [depends_on: [T6]] Write integration tests

Tasks with empty/satisfied dependencies can run in parallel (T1, T2 above).

4. Save Plan

Save to <topic>-plan.md in the CWD.

5. Subagent Review

After saving, spawn a subagent to review the plan:

Review this implementation plan for:
1. Missing dependencies between tasks
2. Ordering issues that would cause failures
3. Missing error handling or edge cases
4. Gaps, holes, gotchas.

Provide specific, actionable feedback. Do not ask questions.

Plan location: [file path]
Context: [brief context about the task]

If the subagent provides actionable feedback, revise the plan before yielding.

Plan Template

markdown
# Plan: [Task Name]

**Generated**: [Date]

## Overview
[Summary of task and approach]

## Prerequisites
- [Tools, libraries, access needed]

## Dependency Graph

[Visual representation of task dependencies] T1 ──┬── T3 ──┐ │ ├── T5 ── T6 ── T7 T2 ──┴── T4 ──┘


## Tasks

### T1: [Name]
- **depends_on**: []
- **location**: [file paths]
- **description**: [what to do]
- **validation**: [how to verify]
- **status**: Not Completed
- **log**: [leave empty, to be filled out later]
- **files edited/created**: [leave empty, to be filled out later]

### T2: [Name]
- **depends_on**: []
- **location**: [file paths]
- **description**: [what to do]
- **validation**: [how to verify]
- **status**: Not Completed
- **log**: [leave empty, to be filled out later]
- **files edited/created**: [leave empty, to be filled out later]

### T3: [Name]
- **depends_on**: [T1]
- **location**: [file paths]
- **description**: [what to do]
- **validation**: [how to verify]
- **status**: Not Completed
- **log**: [leave empty, to be filled out later]
- **files edited/created**: [leave empty, to be filled out later]

[... continue for all tasks ...]

## Parallel Execution Groups

| Wave | Tasks | Can Start When |
|------|-------|----------------|
| 1 | T1, T2 | Immediately |
| 2 | T3, T4 | Wave 1 complete |
| 3 | T5 | T3, T4 complete |
| ... | ... | ... |

## Testing Strategy
- [How to test]
- [What to verify]

## Risks & Mitigations
- [What could go wrong + how to handle]

Important

  • Every task must have explicit depends_on field
  • Root tasks (no dependencies) can be executed in parallel immediately
  • Do NOT implement - only create the plan
  • Always use Context7 for external dependencies before finalizing tasks
  • Always ask questions where ambiguity exists

Frequently asked questions

What does the Swarm Planner AI skill do?

[EXPLICIT INVOCATION ONLY] Creates dependency-aware implementation plans optimized for parallel multi-agent execution.

Why use Swarm Planner on TypingMind?

Because you install it once and use it with any model. Swarm Planner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Swarm Planner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/am-will/codex-skills/tree/main/skills/swarm-planner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Swarm Planner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Swarm Planner?

As many as you like. As long as a model supports skills, you can use Swarm Planner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Swarm Planner AI skill free?

It is published on GitHub by am-will. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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