Paperclip is a Node.js server and React application for coordinating AI agents as a business team, with goals, organizational roles, budgets, governance, and work tracking in one interface. It is used by people managing multiple agents or autonomous agent organizations across tools such as OpenClaw, Claude Code, Codex, and Cursor. The catalogue includes skills, agents, an MCP add-on, and an instruction for working with Paperclip.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/paperclipai/paperclip/task-planningnpx skills add paperclipai/paperclip --skill task-planninggit clone --depth 1 https://github.com/paperclipai/paperclipWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/paperclipai/paperclip/task-planning)<a href="https://agentmods.dev/skills/paperclipai/paperclip/task-planning"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/task-planning.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.01097 |
| Opus 5 | $0.00020 | $0.00549 |
| Sonnet 5 | $0.00008 | $0.00219 |
| Haiku 4.5 | $0.00004 | $0.00110 |
Grade A, and why
task-planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
plandocument already exists and the change is minor. Update that document; do not start fresh.
Outputs
- An updated issue document with key
plan(markdown). - A short comment on the issue that links to the plan document and names the next action.
- Where the plan requires approval, an issue-thread interaction of kind
request_confirmationbound to the latest plan revision.
Do not create implementation subtasks until the plan is accepted.
Plan structure
Required sections, in order:
- Goal — one paragraph. What changes for the user, the operator, or the system once this work lands.
- Context reviewed — bullet list of documents, files, and prior issues you read. Lets reviewers spot missing inputs.
- Constraints and non-goals — what must hold (compatibility, security, performance) and what this plan deliberately will not do.
- Approach — the chosen path, with a short rationale. If you considered alternatives, name them and why you rejected them.
- 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.
- Acceptance — the bar for the parent issue. How the user knows the whole thing is done.
- Risks and mitigations — short list. Skip if there are none.
- Deferrals — what is intentionally pushed to follow-up issues, with why.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 85 lines · 40 tokens per session scan A 9c52a44a30ec
task-planning is a skill published in the GitHub repository paperclipai/paperclip (80,074 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 1,097 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
hourly-rate-time
当用户纠结时间分配、问「这个琐事/外包值不值」「怎么安排时间」「离财务自由还差什么」时调用。 核心理念: 给时间定一个高得离谱的时薪, 低于时薪的事外包或不做; 退休=不为想象中的明天牺牲今天; 不随收入升级生活方式。 不适用于: 需要享受慢节奏的时刻（休息/家庭时间按定义不是浪费）。 Triggers: 时薪/时间管理/外包/琐事/排队/财务自由/退休/hourly rate/delegate.
delegation-core
Delegates execution to eight CLIs (Gemini, Qwen, MiniMax, GLM, Muse, Codex, OpenCode, Glimmer). Use for execution tasks. Do not use for secrets.
glm-delegation
Delegates tasks to Z.ai GLM-5.x via the stock claude binary and an endpoint swap. Use when delegation-core selects GLM or a 1M-token context is needed.
minimax-delegation
Delegates tasks to the MiniMax CLI (mmx) via delegation-core. Use when delegation-core selects MiniMax or large-context batch work is needed.
qwen-delegation
Delegates tasks to Qwen CLI via delegation-core for Alibaba's models. Use when delegation-core selects Qwen or large-context batch processing is needed.
gemini-delegation
Delegates tasks to Gemini CLI implementing delegation-core for Google's models. Use when delegation-core selects Gemini or 1M+ token context is needed.