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Paperclip Converting Plans To Tasks

OrganizationPopular
paperclipai
paperclip-converting-plans-to-tasks

Convert Paperclip plans into executable issue graphs. Use when asked to plan, scope, or break down Paperclip company work into assigned tasks with specialty fit, dependencies, blockers, and parallelization.

Overview

Publisherpaperclipai
Repositorypaperclip
Skill namepaperclip-converting-plans-to-tasks
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 Paperclip Converting Plans To Tasks 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/skills/paperclip-converting-plans-to-tasks
  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/skills/paperclip-converting-plans-to-tasks .claude/skills/paperclip-converting-plans-to-tasks
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Paperclip Converting Plans To Tasks 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 Paperclip Converting Plans To Tasks 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 Paperclip Converting Plans To Tasks 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.

Paperclip — Converting Plans to Tasks

A companion skill for turning a plan into executable Paperclip work. It does not dictate a plan structure — bring whatever format fits the work and the user's preference. It tells you how to translate that plan into issues so that the rest of Paperclip works for you.

For the mechanics of recording a plan (issue document with key plan, comment links, approval gating, who to reassign back to), follow the Planning section of the paperclip skill. This skill covers planning method, not the API surface.

When you're asked to plan

  • Plan deeply. Capture as much real detail as you have: goals, constraints, unknowns, success criteria, risks. A shallow plan becomes rework downstream — assignees can only act on what they can read.
  • Minimize the issue graph. Use as few tasks as possible while still completing and verifying the job. Prefer one end-to-end task with one owner over separate tasks for each step, file, component, or phase. Keep those structural details as checklists or acceptance criteria inside the owning task unless a real execution boundary requires another issue.
  • Split only for a qualifying boundary. Create a separate subtask only when at least one of these applies:
    • A different specialist, owner, permission boundary, or external actor must own the work.
    • A self-contained deliverable can usefully run in parallel with other work.
    • A hard dependency or handoff needs its own blockedByIssueIds lifecycle.
    • A review, QA pass, or governed approval gate has an independent owner.
    • Substantial follow-up work needs independent tracking or retry because it cannot safely be completed and verified in the parent.
  • Know your team. Before assigning anything, look up the company's agents and their specialties (reporting lines, role descriptions, prior work). Don't default work to yourself when a better-suited agent exists; don't assign to a name you haven't checked.
  • Assign for specialty. Hand each piece of work to the agent most relevant to it. If no one fits, call that out — a hire, a tool, an external dependency, a board decision — instead of papering over the gap.
  • Take responsibility. Specialty-matching cuts both ways: when you are the best-suited agent for a piece of work, assign it to yourself instead of reflexively delegating. Don't hand off to avoid load.
  • Use the dependency tree. Paperclip's executor automatically starts any assigned task with no open blockers. Parent/child issue nesting is structure, not execution blocking. Express each qualifying ownership or lifecycle boundary as an issue; keep other concrete deliverables within the responsible issue's description, checklist, or acceptance criteria. Wire every hard dependency between issues through blockedByIssueIds on the dependent issue (not prose like "blocked by X"). When a blocker reaches done, dependents auto-wake.
  • Order, then parallelize. Sequence work by real dependencies, not by personal preference. Create parallel branches only for qualifying, self-contained work, then start those independent branches in parallel. Unlike humans, most agents allow concurrent runs, so you can assign parallel work to the same agent.
  • Write review tasks for the reviewer's boundary. A review/QA task must tell the delegate to post findings on their own review issue and mark it done — the verdict is the deliverable, and adverse findings are still done, not blocked. Never instruct a delegate to comment on the parent issue (low-trust reviewers are guaranteed a 403 there), and make the description self-contained since the reviewer may not be able to read your issue. Wire the dependent issue's blockedByIssueIds to the review issue so the verdict wakes the right owner.
  • Enough is enough. Plans exist to unblock execution, not replace it. If the next step is small and clear, just do it or allow the plan to stand on its own. Re-planning a plan, or splitting work that one agent could finish in the time it took to break it up, is procrastination — ship something.

When converting an accepted plan into tasks

Start from one end-to-end task and add issues only for the qualifying boundaries above. Before creating tasks, write a compact task matrix with each proposed task, owner, initial status, blockers, and the specific qualifying reason it must be separate. Any task that can start immediately should say why it has no blockers; otherwise set it to blocked and include the prerequisite issue IDs in blockedByIssueIds. Do not rely on parentId, child ordering, phase labels, or prose to block execution.

Run a merge-back pass before publishing or creating the graph. Require every proposed subtask to name at least one qualifying reason from this skill. If it cannot, merge it into its parent or an adjacent task and preserve the work as an internal step, checklist item, or acceptance criterion. Repeat until every remaining issue has a real ownership, scheduling, lifecycle, or governance reason to exist.

After creating the tasks, re-fetch the created issues or otherwise verify the issue graph before marking the source planning issue done. Confirm that every separate issue still has its qualifying reason, each dependent task has the expected blockedByIssueIds, each independent task has an explicit "can start now" reason, review tasks respect the reviewer's write boundary, and the parent/child hierarchy is only being used for traceability. If the graph contains an unjustified split or expected blockers are missing, correct it or report the mismatch and leave the planning issue in in_review or blocked until the graph is fixed.

Quick checklist before you publish a plan

  • Enough detail that assignees can act without re-asking.
  • The plan uses the fewest tasks that can complete and verify the job, preferring one end-to-end owner over step/file/component/phase splits.
  • Every concrete deliverable is accounted for inside an issue or, only when a qualifying boundary applies, as its own issue.
  • Every proposed subtask names a qualifying reason; otherwise it was merged into its parent or an adjacent task.
  • Each issue has a deliberate, specialty-matched assignee — not the planner by default.
  • Each issue's real blockers are declared via blockedByIssueIds.
  • Independently owned review, QA, and governed approval tasks respect the reviewer's boundary.
  • A compact task matrix names planned task, owner, initial status, blockers, and qualifying reason.
  • Tasks without blockers have an explicit reason they can start immediately.
  • Created issues were re-fetched or otherwise verified before closing the source planning issue.
  • Qualifying independent branches can start in parallel.
  • Gaps (missing skills, hires, decisions, external inputs) are surfaced, not hidden.

What this skill is not

  • Not a plan template. Use any format — prose, outline, table, RACI, Gantt, whatever fits.
  • Not software-development–specific. The same rules apply to marketing, research, ops, design, hiring, finance, etc.
  • Not a replacement for the paperclip skill's planning mechanics. Use both.

Frequently asked questions

What does the Paperclip Converting Plans To Tasks AI skill do?

Convert Paperclip plans into executable issue graphs. Use when asked to plan, scope, or break down Paperclip company work into assigned tasks with specialty fit, dependencies, blockers, and parallelization.

Why use Paperclip Converting Plans To Tasks on TypingMind?

Because you install it once and use it with any model. Paperclip Converting Plans To Tasks 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 Paperclip Converting Plans To Tasks in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/paperclipai/paperclip/tree/master/skills/paperclip-converting-plans-to-tasks. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Paperclip Converting Plans To Tasks?

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 Paperclip Converting Plans To Tasks?

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

Is the Paperclip Converting Plans To Tasks 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.

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