first-task

first-task is a skill for Claude Code, Codex from paperclipai/paperclip. It costs 47 tokens per session (1,557 once invoked), scanned A, original, MIT.

An onboarding workflow for a user's first Paperclip task. It clarifies the goal, proposes a plan or task, and waits for approval before hiring agents or doing approved work.

In plain words
What is it for?
Use it to interview a new user, turn their request into a concrete task, and agree on an agent team or plan before execution.
Why use it?
It prevents work from starting before the user's goal and proposed approach are clear.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to interview a new user, turn their request into a concrete task, and agree on an agent team or plan before execution.

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Install with agentmods
npx agentmods add skills/paperclipai/paperclip/first-task
About the project

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.

paperclipai/paperclip · 105,675 stars · on GitHub · paperclip.ing

Install

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.

Any agent
npx skills add paperclipai/paperclip --skill first-task
Clone the repo
git clone --depth 1 https://github.com/paperclipai/paperclip

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for first-task

README.md
[![agentmods](https://agentmods.dev/badge/skills/paperclipai/paperclip/first-task/github.svg)](https://agentmods.dev/skills/paperclipai/paperclip/first-task)
Your own site
<a href="https://agentmods.dev/skills/paperclipai/paperclip/first-task"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/first-task/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for first-task

Your own site · 80×15
<a href="https://agentmods.dev/skills/paperclipai/paperclip/first-task"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/first-task.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,557 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00047 $0.01557
Opus 5.5 $0.00019 $0.00623
Sonnet 5.5 $0.00009 $0.00311
Haiku 4.5 $0.00005 $0.00156

Measured 7d ago against content hash 87f2394edd4c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

first-task 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 7d 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.

server/src/onboarding-assets/first-task/skills/first-task/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

First task

Use this workflow only for the onboarding task that invokes /first-task, including later replies and approval wakes on that same task. Do not apply it to the agent's other tasks just because this skill is installed. Follow it without announcing the first-task skill in routine messages, cards, or documents. Say "I'm doing X," not "I'm using the first-task skill to do X," and explain next steps directly. This is a wording preference: answer truthfully if the user asks about the workflow, and always disclose relevant permissions, security implications, and execution actions.

This is the user's first task in Paperclip. Your job is to understand what they want and propose a path forward. A greeting and an opening question card were already posted for you; the card offered two choices: "Interview me and propose a plan and an agent team to execute it." (option interview) or "I have a task in mind" (option task, with a text field). You are running because the user answered that card (the answer is in your wake payload) or wrote a message instead of answering. Don't re-introduce yourself and don't post the opening card again.

Work in this order.

  1. Take the path the user picked.

    • interview → ask the user 3–4 questions in one Paperclip question card (request_human_input with interactionKind: "questions" when available, otherwise the ask_user_questions API) that pin down what their organization does, what they want to achieve first, any constraints (time, budget, tools), and what "done" looks like. Don't guess; ask. Don't post anything else before the card. The answers lead to the plan-and-team path in step 2.

    • task → the text they typed is the task. If it is clear enough to propose on, go straight to step 2. If not, reply by asking 2–3 questions specific to their message (concrete goal, constraints, what "done" looks like), then go to step 2.

    • If they wrote a message instead of answering the card, treat the message as the task path.

  2. Propose, then wait for acceptance.

    • Choose the proposal form from the user’s request first: an explicit plan request or the interview path always requires a saved plan, even when the task description says confirmation.
    • If they want a plan, save a plan document on this onboarding task describing the goal, scope, steps, proposed team, and what done means. Post one request_checkbox_confirmation targeting the saved plan revision. A card or thread message alone is not a saved plan. This applies to explicit plan requests regardless of the single-task proposal mode. Proposing a team does not authorize hiring it.
    • If they want one thing done, propose exactly one child task with a clear outcome and scope. Ask them to accept it before creating the child. Do not produce the requested finished work inside the proposal, even when it is quick to do.
    • For a single-task proposal, follow the Single-task proposal mode saved in the task description: confirmation means one request_confirmation card describing the child task, without a plan document; plan means save a short plan document describing that same child task and post one request_checkbox_confirmation targeting its saved revision.
    • Keep this task in_review while waiting. You may clarify, research for planning, and save or revise a plan/proposal before acceptance. Do not hire, create execution tasks, perform the deliverable, save finished output, or claim completion yet.
  3. Interpret the next reply against the latest proposal.

    • Acceptance is an accepted confirmation card or an explicit conversational reply agreeing to the proposal. The opening answer, a clear request, and answers to clarification questions supply scope; they are not acceptance of a proposal you have not yet made.
    • When acceptance or rejection arrives in chat, persist it on the corresponding pending confirmation before hiring, creating a child, executing, or closing this task. Read this task's interactions and comments, then POST /api/issues/{issueId}/interactions/{interactionId}/resolve-from-comment with { "commentId": "<user-message-id>", "decision": "accept" } (or "reject" and the user's reason). Native runners use call_api. For a checkbox card, include selectedOptionIds for the user's actual selection; never infer acceptance from defaults. Wait for the saved accepted/rejected result. Retry an interrupted write using the same message and decision; inspect a conflicting result instead of proceeding. Do not tell the user to return to the card after answering in chat.
    • An ambiguous reply with multiple pending proposals is not permission to resolve them all. Ask which proposal or options they mean. A card already answered through the UI needs no second resolution. Forms, governed actions, and human-only policies keep their existing response rules.
    • A clarification answer means update the proposal if needed and ask for acceptance. A requested revision supersedes the old scope: revise the proposal and wait for acceptance of the revised version.
    • If they reject the proposal, acknowledge and stop. Do not execute it. You may close the onboarding task after acknowledging the rejection; do not describe rejected work as completed.

Read the full file on GitHub · 56 lines

Changes

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.

  1. 7d ago Changed · +3 lines 87f2394edd4c
  2. 21d ago Changed · +2 lines b01dc21e7a74
  3. 1mo ago Changed · +1 lines 30e1120d3990
  4. 1mo ago First seen · 50 lines · 47 tokens per session scan A 1520d0f641f3

Subscribe to this mod's changes

first-task is a skill published in the GitHub repository paperclipai/paperclip (105,675 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 1,557 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-17.

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