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.
Borrowing it
Nothing to install: this file belongs to paperclipai/paperclip. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/paperclipai/paperclip/master/.agents/skills/setup-code-maintenance-bot/SKILL.mdgit 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/setup-code-maintenance-bot)<a href="https://agentmods.dev/skills/paperclipai/paperclip/setup-code-maintenance-bot"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/setup-code-maintenance-bot/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.
<a href="https://agentmods.dev/skills/paperclipai/paperclip/setup-code-maintenance-bot"><img src="https://agentmods.dev/badge/skills/paperclipai/paperclip/setup-code-maintenance-bot.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00055 | $0.01358 |
| Opus 5.5 | $0.00022 | $0.00543 |
| Sonnet 5.5 | $0.00011 | $0.00272 |
| Haiku 4.5 | $0.00006 | $0.00136 |
Grade A, and why
setup-code-maintenance-bot 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 10d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set Up a Code Maintenance Bot
Turn the user's maintenance intent into a runnable Paperclip routine with a named agent, accessible repository and skill, bounded work, and inspectable PRs. The maintenance skill supplies the domain judgment: test auditing, documentation upkeep, dependency maintenance, refactoring, or another chosen task.
If asked only to draft instructions or a setup plan, produce that artifact and stop before provisioning or running anything. Do not perform the maintenance while setting up the bot. An immediate trial is a separate choice from scheduling.
1. Establish the destination and agent
Start with the instance and agent choices, using answers already supplied:
- Instance: show a clickable, verified Paperclip web URL and ask which instance to use. If none is known, ask for its URL; never assume localhost, the instance used in an earlier task, or the first connected tool.
- Company: resolve the company within that instance; ask if ambiguous.
- Agent: list accessible, suitable agents with names and links, and offer use an existing agent or create a new agent. Do not silently choose.
Example, after resolving the actual URL: “Use Paperclip, company Acme? Which agent should own this: an existing agent or a new one?”
For a new agent, resolve its name, harness/adapter, provider and model, AI connection/account, execution environment, reporting line, and budget. Present available compatible choices from the selected instance. Distinguish the AI connection from repository credentials. For an existing agent, inspect these settings and preserve them unless the requested maintenance needs an authorized change. Keep maintenance-specific instructions in the routine, not by replacing an existing agent's role.
Read references/paperclip-api.md for authentication, discovery, connection binding, hiring, and exact provisioning calls. If the API is unavailable or not working and browser access is available, use the selected instance's Paperclip UI to perform the same setup. Verify the instance and company, honor UI approval gates, and inspect the saved agent and routine after saving. Reconcile any partially completed API setup before creating through the browser so the fallback does not duplicate resources. Resolve access against the selected instance before collecting secrets or making writes.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 122 lines · 55 tokens per session scan A b09a56f4f7c1
setup-code-maintenance-bot is a skill published in the GitHub repository paperclipai/paperclip (107,044 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 1,358 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 2026-10-15.
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agent-swarm
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gsd-tools
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