Task Planning logo

Task Planning

OrganizationPopular
paperclipai
task-planning

Turn a Paperclip issue or request into a structured implementation plan with child task graph, blockers, owners, and acceptance criteria, then save it as the issue `plan` document.

Overview

Publisherpaperclipai
Repositorypaperclip
Skill nametask-planning
Stars
95.4K
Forks
16.2K
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 paperclipai on GitHub. Read the source before you install it.

Installation

Install the Task Planning 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/paperclipai/paperclip/tree/master/packages/skills-catalog/catalog/bundled/paperclip-operations/task-planning
  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/paperclipai/paperclip.git /tmp/paperclip
mkdir -p .claude/skills
cp -r /tmp/paperclip/packages/skills-catalog/catalog/bundled/paperclip-operations/task-planning .claude/skills/task-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Task Planning 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 Task Planning 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 Task Planning 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.

Task Planning

Produce implementation plans that the Paperclip executor can actually run: explicit child issues, real blockers, named owners, and a defined acceptance bar. Avoid plans that read well but cannot be split into work.

When to use

  • An issue asks you to "plan", "scope", "break down", "design the rollout", "propose the work", or similar.
  • A user wants a written plan before approving implementation.
  • A manager needs to delegate non-trivial work and the shape of the work is not obvious yet.
  • You inherited an issue too large to deliver in one heartbeat and need to split it.

When not to use

  • The issue is a single small change you can ship in the same heartbeat. Just ship it.
  • The issue is forensic ("why did this break"). Use a diagnosis skill first; plan only after the root cause is named.
  • A current plan document already exists and the change is minor. Update that document; do not start fresh.

Outputs

  1. An updated issue document with key plan (markdown).
  2. A short comment on the issue that links to the plan document and names the next action.
  3. Where the plan requires approval, an issue-thread interaction of kind request_confirmation bound to the latest plan revision.

Do not create implementation subtasks until the plan is accepted.

Plan structure

Required sections, in order:

  1. Goal — one paragraph. What changes for the user, the operator, or the system once this work lands.
  2. Context reviewed — bullet list of documents, files, and prior issues you read. Lets reviewers spot missing inputs.
  3. Constraints and non-goals — what must hold (compatibility, security, performance) and what this plan deliberately will not do.
  4. Approach — the chosen path, with a short rationale. If you considered alternatives, name them and why you rejected them.
  5. Work breakdown — ordered list of child issues. Each child has:
    • Title in imperative form.
    • Owner specialty (Engineer, QA, Designer, Security, DevRel, Manager, etc.).
    • Scope and deliverables.
    • Acceptance criteria.
    • Blocks/blocked-by relationships expressed by phase letter or child title.
  6. Acceptance — the bar for the parent issue. How the user knows the whole thing is done.
  7. Risks and mitigations — short list. Skip if there are none.
  8. Deferrals — what is intentionally pushed to follow-up issues, with why.

Rules of thumb for splitting

  • One child issue, one specialty. If two specialties have to coordinate inside the same issue, split it.
  • One child issue, one acceptance verdict. If a reviewer would say "this is half done", split it.
  • A child must be checkout-able by the owner from its title and description alone. Reviewers should not have to re-read the parent plan to understand a child.
  • Order children by real blocker chains, not by author preference. Parallel children should explicitly say blockers: none.
  • Avoid polish or cleanup child issues without acceptance criteria — they never close.

Filing the plan

Use the Paperclip API to write the plan document, then comment:

  • PUT /api/issues/{issueId}/documents/plan with the markdown body. If plan already exists, include the latest baseRevisionId.
  • POST /api/issues/{issueId}/comments with a short summary that links the plan: /<prefix>/issues/<issue-id>#document-plan.
  • If approval is required: POST /api/issues/{issueId}/interactions with kind: request_confirmation, targetRevisionId set to the new plan revision, continuationPolicy: wake_assignee, and idempotencyKey: "confirmation:{issueId}:plan:{revisionId}".
  • Set the issue to in_review after creating the confirmation. Stay assigned so the acceptance wakes the planner.

When the plan is accepted, see the companion skill for converting accepted plans into Paperclip executable tasks. Key requirements covered there: produce a compact task matrix (task, owner, initial status, blockers); encode every hard dependency as blockedByIssueIds — parent/child nesting alone does not block execution; and verify the created issue graph before closing the source planning issue.

Anti-patterns

  • Plan disguised as a description edit. Use the plan document.
  • "Phases A–Z" with no work breakdown inside the phases.
  • Children with descriptions that say "see parent" — they fail at delegation time.
  • Acceptance written as "code review approval". Reviewers need a behavior bar, not a process bar.
  • Plans that bury blocker chains in prose. Use explicit blocked-by lines.

Frequently asked questions

What does the Task Planning AI skill do?

Turn a Paperclip issue or request into a structured implementation plan with child task graph, blockers, owners, and acceptance criteria, then save it as the issue `plan` document.

Why use Task Planning on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/paperclipai/paperclip/tree/master/packages/skills-catalog/catalog/bundled/paperclip-operations/task-planning. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Task Planning?

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 Task Planning?

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

Is the Task Planning AI skill free?

Yes. It is published on GitHub by paperclipai 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇