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Planner

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jdrhyne
planner

Create structured plans for multi-task projects that can be used by the task-orchestrator skill. Use when breaking down complex work into parallel and sequential tasks with dependencies.

Overview

Publisherjdrhyne
Repositoryagent-skills
Skill nameplanner
Stars
240
Forks
30
Bundled files
Instructions only
LicenseMIT
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 jdrhyne on GitHub. Read the source before you install it.

Installation

Install the 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/jdrhyne/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/planner .claude/skills/planner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Planner

Create structured, orchestrator-ready plans for multi-task projects.

Source: Adapted from am-will's codex-skills workflow patterns Pairs with: task-orchestrator skill for follow-on implementation


Quick Start

Follow this process:

  1. Phase 0: Clarify requirements (ask up to 5 targeted questions)
  2. Phase 1: Research & understand the codebase
  3. Phase 2: Create detailed plan with sprints, tasks, acceptance criteria
  4. Phase 3: Subagent review of the plan
  5. Phase 4: Return the plan in markdown and save a file only if the user asked for a persisted artifact

Key Principles

Task Atomicity

Each task must be:

  • Atomic and committable — small, independent pieces of work
  • Specific and actionable — not vague
  • Testable — include tests or validation method
  • Located — include file paths and code locations

Bad vs Good Task Breakdown

❌ Bad: "Implement third-party sign-in"

✓ Good:

  • "Add sign-in config to environment variables"
  • "Install and configure the required authentication package"
  • "Create sign-in callback route handler in src/routes/auth.ts"
  • "Add the sign-in button to the login UI"

Sprint Structure

Each sprint must:

  • Result in a demoable, runnable, testable increment
  • Build on prior sprint work
  • Include clear demo/verification checklist

Plan Template

markdown
# Plan: [Task Name]

**Generated**: [Date]
**Estimated Complexity**: [Low/Medium/High]

## Overview
[Brief summary of what needs to be done and the general approach]

## Prerequisites
- [Dependencies or requirements that must be met first]
- [Tools, libraries, or access needed]
- [Tooling limitations, e.g., browser relay/CDP restrictions]

## Sprint 1: [Sprint Name]
**Goal**: [What this sprint accomplishes]
**Demo/Validation**:
- [How to run/demo this sprint's output]
- [What to verify]

### Task 1.1: [Task Name]
- **Location**: [File paths or components involved]
- **Description**: [What needs to be done]
- **Perceived Complexity**: [1-10]
- **Dependencies**: [Any previous tasks this depends on]
- **Acceptance Criteria**:
  - [Specific, testable criteria]
- **Validation**:
  - [Test(s) or alternate validation steps]

### Task 1.2: [Task Name]
[...]

## Sprint 2: [Sprint Name]
[...]

## Testing Strategy
- [How to test the implementation]
- [What to verify at each sprint]

## Potential Risks
- [Things that could go wrong]
- [Mitigation strategies]

## Rollback Plan
- [How to undo changes if needed]

Hand-off

Once plan is ready, hand off to the parallel-task worker:

Please run parallel-task.md against my-plan.md

Or invoke directly:

"Run all unblocked tasks in plan.md using parallel subagents. Keep looping until all tasks are complete."

Safety Boundaries

  • Do not save a plan file unless the user asked for one or the surrounding workflow explicitly needs a persisted artifact.
  • Do not assign overlapping write scopes to parallel tasks without calling out the conflict.
  • Do not invent dependencies, validation steps, or completion status that the repo context does not support.
  • Do not turn a planning request into implementation work unless the user explicitly asks to move from planning to implementation.

Related

  • parallel-task — Parallel task worker that executes the plan this skill produces

Adapted from am-will's codex-skills planner/parallel-task prompts.

Frequently asked questions

What does the Planner AI skill do?

Create structured plans for multi-task projects that can be used by the task-orchestrator skill. Use when breaking down complex work into parallel and sequential tasks with dependencies.

Why use Planner on TypingMind?

Because you install it once and use it with any model. 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 Planner in TypingMind?

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

Which AI models can use 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 Planner?

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

Is the Planner AI skill free?

Yes. It is published on GitHub by jdrhyne under the MIT license. 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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