Make Plan logo

Make Plan

OrganizationPopular
agentscope-ai
make_plan

For external plan request scenarios, guides the Agent to request a clear, actionable, step-by-step plan from a stronger Agent via list_agents and chat_with_agent, emphasizing that the plan is executed by the requester, not by the consulted Agent.

Overview

Publisheragentscope-ai
RepositoryQwenPaw
Skill namemake_plan
Stars
35.6K
Forks
3.2K
Bundled files
Instructions only
LicenseApache-2.0
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 agentscope-ai on GitHub. Read the source before you install it.

Installation

Install the Make Plan 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.
    https://github.com/agentscope-ai/QwenPaw/tree/main/src/qwenpaw/agents/skills/make_plan-en
  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/agentscope-ai/QwenPaw.git /tmp/QwenPaw
mkdir -p .claude/skills
cp -r /tmp/QwenPaw/src/qwenpaw/agents/skills/make_plan-en .claude/skills/make_plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Make Plan 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 Make Plan 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 Make Plan 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.

Make Plan

Use this Skill when you need to make an external plan request to a stronger Agent.

The goal of this Skill is not to outsource a task, but rather to:

  • Request a plan from a stronger Agent
  • The plan must consist of clear, actionable steps
  • You execute the plan yourself
  • Do not ask the consulted Agent to execute the task directly

How to invoke:

  • Use list_agents() to check available Agents
  • Use chat_with_agent(...) to request the stronger Agent to "make a plan"
  • In text, explicitly include the prompt: only provide a step-by-step executable plan, do not execute
  • To supplement or refine the original plan in a follow-up, pass session_id

Recommended invocation skeleton:

text
list_agents()

chat_with_agent(
  to_agent="<stronger_agent>",
  text="[Agent <auto> requesting] Please help me create an execution plan for the following task. You do not need to execute the task -- just output clear, actionable steps in order.",
)

Core Rules

This Skill handles one thing only:

  1. Find a stronger Agent
  2. Request that Agent to output an execution plan
  3. Require the plan to be step-by-step, actionable, and in sequential order
  4. You execute the plan yourself rather than asking the other Agent to do it for you

If what you truly need is a "plan", use this Skill. If what you need is a final answer, architectural judgment, review conclusion, or direct execution, do not misuse this Skill.

Applicable Scenarios

The following scenarios are suitable for making an external plan request:

  • The task requires multi-step decomposition
  • Steps have dependencies between them
  • A clear sequence, checkpoints, or verification points are needed
  • Multiple modules, files, systems, or roles are involved
  • The user explicitly requests a plan before execution
  • You want to obtain a more complete and reliable execution path first

Do Not Use This Way

Do not use this Skill in the following situations:

  • You actually want the other Agent to do the task for you
  • What you really lack is a small piece of factual information, not a plan
  • What you truly need is an architectural judgment or solution comparison
  • You have not yet clearly understood the task objective

What to Ask the Stronger Agent For

When calling chat_with_agent(...), clearly state:

  • You need a plan, not execution
  • Steps must be concrete, not just abstract advice
  • Preferably include verification methods and completion criteria

Tool Invocation Rules

Execute in this order by default:

  1. Call list_agents() to confirm available stronger Agents
  2. Select the most suitable target Agent for creating the plan
  3. Call chat_with_agent(...) to request the generation of an execution plan
  4. After receiving the plan, execute it yourself -- do not continue outsourcing the task to the other Agent

When requesting the plan, explicitly state:

  • Only the plan is needed, not execution
  • Steps must be specific, not general advice
  • Clearly request sequence, dependencies, checkpoints, and verification methods when needed

Common parameters for chat_with_agent:

  • to_agent: Target Agent ID
  • text: Request content; must explicitly state "only output the plan, do not execute the task"
  • session_id: Optional; pass this to continue an existing conversation

Notes:

  • base_url generally does not need to be provided; the tool will automatically resolve the current API address
  • Not passing session_id will automatically create a new session

Request Template

Please help me create an execution plan for the following task. You do not need to execute the task itself -- just output the plan.

Task: [What needs to be done]

Goal: [What the final outcome should be]

Constraints:

  • [...]
  • [...]

Plan requirements:

  1. Break it down into clear, executable steps
  2. Indicate the recommended order
  3. Point out dependencies, checkpoints, or verification methods where necessary
  4. If there are obvious risks, include key points to watch out for

Output format: [e.g., Output 5-8 numbered steps, 1-3 sentences each]

Example:

text
chat_with_agent(
  to_agent="strong_reasoner",
  text="""
Please help me create an execution plan for the following task.
You do not need to execute the task itself -- just output the plan.

Task:
Modify a multi-module feature.

Goal:
Complete the change with low risk and avoid missing integration points.

Constraints:
- Minimize rework
- Include verifiable intermediate checkpoints

Plan requirements:
1. Break it down into actionable steps
2. Indicate the recommended order
3. Mark key dependencies and checkpoints
4. Include verification methods where possible

Output format:
Please output 3-5 numbered steps, each as specific as possible.
""",
)

For follow-up conversations:

text
chat_with_agent(
  to_agent="strong_reasoner",
  text="Based on the previous plan, please further refine the checkpoints for steps 3 and 4. Still only provide the plan -- do not execute the task.",
  session_id="<previous_session_id>",
)

What to Do After Receiving the Plan

Treat the stronger Agent's reply as "execution plan input", not as "the task is already done".

What you should do:

  1. Distill the truly executable steps
  2. Determine whether adjustments are needed for your environment
  3. Execute the steps yourself
  4. If new uncertainties arise, continue refining the plan

Do not deliver the plan as-is to the user as a final result, unless the user specifically asked for the plan itself.

Plan Quality Standards

A qualified plan should at least meet the following criteria:

  • Has clear steps, not vague advice
  • Step order is clear
  • Each step is an executable action
  • Key dependencies are identified
  • Necessary verification points are included
  • Does not secretly delegate "task execution" to the consulted Agent

If what you receive is vague advice such as "first analyze, then implement, then test", it is not sufficient -- follow up to request refinement.

Behavioral Guardrails

  • Do not turn "please help me plan" into "please do the whole thing for me"
  • Do not request the other Agent to execute code, commands, or changes on your behalf
  • Do not accept answers that only provide direction without steps
  • Do not request a plan when the task objective is not yet clear
  • Do not stop thinking after receiving the plan -- still evaluate it against your current environment

Final Principle

When you lack an execution path, request a plan first. The plan must be step-by-step, specific, and actionable. The consulted Agent is responsible for producing the plan, not for executing it. After receiving the plan, the requester executes it themselves.

Frequently asked questions

What does the Make Plan AI skill do?

For external plan request scenarios, guides the Agent to request a clear, actionable, step-by-step plan from a stronger Agent via list_agents and chat_with_agent, emphasizing that the plan is executed by the requester, not by the consulted Agent.

Why use Make Plan on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentscope-ai/QwenPaw/tree/main/src/qwenpaw/agents/skills/make_plan-en. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Make Plan?

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 Make Plan?

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

Is the Make Plan AI skill free?

Yes. It is published on GitHub by agentscope-ai under the Apache-2.0 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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