Paperclipで実際のブラウザからサーバー、データベースまで通すE2E評価を追加・拡張し、操作結果や証拠を既存のレポートで確認できるようにするスキル。
- 一連の利用操作の評価を追加したいとき
- テスト用データと判定条件の更新
- 評価レポートに画像や成果物を載せたいとき
選んだ保守用スキルをリポジトリに適用し、変更をプルリクエストにまとめるPaperclipのボットを、実行予定や作業上限、担当者まで含めて設定します。
Set up a Paperclip agent and routine that applies a selected code-maintenance skill to a repository and opens pull requests. Use for nightly maintenance bots, scheduled code gardening, or webhook-driven maintenance, using an existing agent or a new hire.
リポジトリの保守作業を定期実行し、変更をプルリクエストにまとめるPaperclipのボットを設定します。既存エージェントの利用と新規作成に対応し、対象ブランチ、使うスキル、実行環境、担当者を整理します。時刻指定、外部イベントを受け取るWebhook、手動・APIによる起動を選び、実行時間や費用、作成するプルリクエスト数の上限も設定します。
夜間のテスト点検、ドキュメント更新、依存関係の保守などを継続して回したいときに向いています。既存の設定との重複を調べ、保存後の内容や有効・停止状態まで確認したい場合にも使えます。
Paperclipへのアクセスと、実行環境から読めるリポジトリ・保守用スキル、プルリクエスト作成権限が必要です。保守内容の判断は選んだスキルが担います。設定作業には保守の実行を含まず、試運転は別途指定します。必要な承認や認証情報が未解決の場合は停止状態を保ちます。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
Start with the instance and agent choices, using answers already supplied:
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.
Ask only for missing choices; group related questions rather than presenting a long questionnaire. Discover actual projects and skills before offering them.
| Choice | Resolve before activation |
|---|---|
| Repository | Project, repository URL, target branch, execution workspace/environment |
| Work | Maintenance goal, exact skill/source, included areas and exclusions |
| Trigger | Timer with local time and IANA timezone; webhook event/source; or manual/API |
| Limits | Per-run time/cost limit, maximum PRs per run, outstanding-PR cap |
| Delivery | PR target and reviewer/owner; draft or ready; required verification |
| Lifecycle | Monitor and revise PRs until checks pass; owner and continuation path for blocked work |
“Every night” still needs a time and timezone. Propose a concrete time if useful, but do not infer one silently. A reasonable starting proposal is one coherent PR per run, a bounded timebox, no automatic merging, and no new work once the agreed outstanding-PR cap is reached. These are proposals, not fixed product limits.
Resolve the skill at a source the execution environment can actually read. A file in this chat's checkout is not proof it exists in a remote worker. Verify the committed target-branch path and all referenced resources, or install and assign the company skill. Record the chosen source/version policy. Never replace an unavailable requested skill with a loosely similar task silently.
Use references/run-instructions.md to write the complete routine description. It becomes the task given to the worker; “run maintenance” or a bare skill name is insufficient.
Inspect existing agents and routines to avoid duplicates. Match by company, repository, task/skill, and trigger; names alone are not identities. Reuse the requested agent. If an equivalent routine exists, show it and resolve whether to reuse or change it rather than creating another nightly job.
Show a compact summary with the instance link, agent choice, connection/harness, repository, skill, trigger/timezone, limits, PR behavior, and readiness gaps. Proceed within the user's existing setup authorization once choices are settled; ask only for unresolved choices or required product approvals. Draft-only scope never authorizes creation. Honor server approval gates without bypassing them.
Follow the API reference or equivalent browser UI in order: acquire authorized access, provision/reuse the agent, make the skill available, create the routine paused, add its trigger disabled, and read everything back. Record returned IDs as each step succeeds. Resume partial setup from those records; an ambiguous response is not a reason to blindly repeat creation.
Verify the agent is runnable and can use its selected connection, repository, skill, and PR credentials in the actual execution environment. A successful adapter probe alone does not prove Git push/PR permission. Keep the routine paused if a hire approval, credential, workspace, or skill is unresolved. Do not change shared project defaults just to make this bot work.
Enable the trigger and activate the routine only when readiness and scheduling authorization are established. Use Paperclip's routine trigger as the clock; do not add a second agent timer or a desktop automation for the same work. Run a maintenance trial only if requested. Read back the saved schedule and next run time; distinguish configured scheduling from an observed successful run.
Return links to the instance, agent, routine, and project; the selected skill; the trigger and timezone; active/paused state; limits and PR policy; readiness evidence and remaining gaps. If trialed, link the run issue and resulting PR or no-change result. Explain how to pause the routine and which owner handles follow-up. Never report a draft, pending hire, or paused routine as running.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Paperclipで実際のブラウザからサーバー、データベースまで通すE2E評価を追加・拡張し、操作結果や証拠を既存のレポートで確認できるようにするスキル。
Paperclip Runnerの動作評価に、判定条件や実行対象、レポート用データを追加・拡張し、根拠と実行履歴を残して検証するスキル。
実際のブラウザでWebページやアプリを操作し、表示や動作を検証します。画面の撮影、簡単なフォーム入力、エラーや通信の記録を通じて、不具合の再現と確認を支援します。
Paperclipで割り当てられたAgentMailの受信箱を使い、メールに紐づく依頼の確認、新規送信、返信、配信状況の確認までを扱うスキルです。
GitHub・GitLab・Perforceの変更を対象に、レビュー指摘、CIの失敗、説明文の不足を確認し、対応事項の整理から依頼に応じた修正まで支援するスキル。
目的に合うAIエージェントの役割や指揮系統、仕事の受け渡しを整理し、新規の構想や既存リポジトリから仕様に沿った会社・チームのパッケージを作るスキル。