一键导入
agent-prompt-batch-slash-command
Instructions for orchestrating a large, parallelizable change across a codebase.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Instructions for orchestrating a large, parallelizable change across a codebase.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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| name | Agent Prompt: /batch slash command |
| description | Instructions for orchestrating a large, parallelizable change across a codebase. |
| ccVersion | 2.1.81 |
| variables | ["USER_INSTRUCTIONS","ENTER_PLAN_MODE_TOOL_NAME","MIN_5_UNITS","MAX_30_UNITS","ASK_USER_QUESTION_TOOL_NAME","EXIT_PLAN_MODE_TOOL_NAME","AGENT_TOOL_NAME","WORKER_PROMPT"] |
| allowed-tools | Read Write Edit Bash |
| license | BSD-3-Clause license |
| metadata | {"skill-author":"Lord1Egypt"} |
You are orchestrating a large, parallelizable change across this codebase.
${USER_INSTRUCTIONS}
Call the ${ENTER_PLAN_MODE_TOOL_NAME} tool now to enter plan mode, then:
Understand the scope. Launch one or more subagents (in the foreground — you need their results) to deeply research what this instruction touches. Find all the files, patterns, and call sites that need to change. Understand the existing conventions so the migration is consistent.
Decompose into independent units. Break the work into ${MIN_5_UNITS}–${MAX_30_UNITS} self-contained units. Each unit must:
Scale the count to the actual work: few files → closer to ${MIN_5_UNITS}; hundreds of files → closer to ${MAX_30_UNITS}. Prefer per-directory or per-module slicing over arbitrary file lists.
Determine the e2e test recipe. Figure out how a worker can verify its change actually works end-to-end — not just that unit tests pass. Look for:
claude-in-chrome skill or browser-automation tool (for UI changes: click through the affected flow, screenshot the result)tmux or CLI-verifier skill (for CLI changes: launch the app interactively, exercise the changed behavior)If you cannot find a concrete e2e path, use the ${ASK_USER_QUESTION_TOOL_NAME} tool to ask the user how to verify this change end-to-end. Offer 2–3 specific options based on what you found (e.g., "Screenshot via chrome extension", "Run bun run dev and curl the endpoint", "No e2e — unit tests are sufficient"). Do not skip this — the workers cannot ask the user themselves.
Write the recipe as a short, concrete set of steps that a worker can execute autonomously. Include any setup (start a dev server, build first) and the exact command/interaction to verify.
Write the plan. In your plan file, include:
Call ${EXIT_PLAN_MODE_TOOL_NAME} to present the plan for approval.
Once the plan is approved, spawn one background agent per work unit using the ${AGENT_TOOL_NAME} tool. All agents must use isolation: "worktree" and run_in_background: true. Launch them all in a single message block so they run in parallel.
For each agent, the prompt must be fully self-contained. Include:
${WORKER_PROMPT}
Use subagent_type: "general-purpose" unless a more specific agent type fits.
After launching all workers, render an initial status table:
| # | Unit | Status | PR |
|---|---|---|---|
| 1 | running | — | |
| 2 | running | — |
As background-agent completion notifications arrive, parse the PR: <url> line from each agent's result and re-render the table with updated status (done / failed) and PR links. Keep a brief failure note for any agent that did not produce a PR.
When all agents have reported, render the final table and a one-line summary (e.g., "22/24 units landed as PRs").