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rw-research-lab-router

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Original

English
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Translation

Chinese

RW Research Lab Router

RW Research Lab Router

根据科研任务、数据和运行环境选择可用工具；没有预设本地 research-lab 也能工作。
Select available tools based on research tasks, data, and operating environment; works without a preset local research-lab.

启动

Startup

  1. 读取 
    references/standalone.md
    、
    references/method.md
    和 
    references/standards.md
    。
  2. 读取用户本轮提供的材料；没有材料时，只完成当前证据允许的部分。
  3. 需要规则判断时检索 
    references/atoms.jsonl
    ；遇到相似任务时读取 
    references/cases.md
    。
  4. 需要选择方法或工具时读取 
    references/domain-guide.md
    ，并使用 
    assets/worksheet.md
    组织交付。
  5. 读取 
    references/acceptance.md
    。
    references/behavior-tests.json
    只用于测试，不作为用户任务事实。
  6. 当前文献、API、报告规范和期刊要求可能变化时，打开 
    references/source-map.md
    中的官方链接核验并记录日期。
  1. Read 
    references/standalone.md
    , 
    references/method.md
    , and 
    references/standards.md
    .
  2. Read the materials provided by the user in this round; if no materials are provided, only complete the parts allowed by current evidence.
  3. Retrieve 
    references/atoms.jsonl
    when rule judgment is required; read 
    references/cases.md
    when encountering similar tasks.
  4. Read 
    references/domain-guide.md
    when selecting methods or tools, and use 
    assets/worksheet.md
    to organize deliverables.
  5. Read 
    references/acceptance.md
    . 
    references/behavior-tests.json
    is only used for testing and not as facts for user tasks.
  6. When current literature, APIs, report specifications, or journal requirements may change, open the official links in 
    references/source-map.md
    for verification and record the date.

工作阶段

Work Stages

  1. 确认任务、输入格式、数据敏感性、运行环境、交付物和可接受失败方式。
  2. 把任务拆成发现、获取、去重、提取、评价、综合、统计、可视化或写作检查。
  3. 盘点当前可用的本机命令、Python 包、API、网页工具和用户提供的仓库。
  4. 检查许可证、认证、费用、限流、维护状态、数据外发和可复现性。
  5. 选择一个首选工具和一个回退方案，并设计最小样本测试。
  6. 运行或描述最小测试，记录版本、命令、结果、失败和停止条件。
  1. Confirm the task, input format, data sensitivity, operating environment, deliverables, and acceptable failure modes.
  2. Break down the task into discovery, acquisition, deduplication, extraction, evaluation, synthesis, statistics, visualization, or writing checks.
  3. Inventory currently available local commands, Python packages, APIs, web tools, and user-provided repositories.
  4. Check licenses, certifications, costs, rate limits, maintenance status, data outbound, and reproducibility.
  5. Select a preferred tool and a fallback solution, and design a minimal sample test.
  6. Run or describe the minimal test, recording versions, commands, results, failures, and stop conditions.

运行规则

Operating Rules

  • 先从任务和输入选择工具，不从已有 repo 名称选择任务。
  • 本地 research-lab 是可选集成，不是运行前提。
  • 代码能安装或编译不等于实时 API、模型或数据源可用。
  • 工具输出必须保留输入、版本、参数和来源。
  • 发现工具和系统综述数据库覆盖不能混为一谈。
  • API 元数据可能不完整，关键标识符要交叉核验。
  • 限流、认证、费用和服务条款要在批量运行前检查。
  • 敏感数据优先选择本地或批准的处理路径。
  • 批量任务先用最小样本测试字段和失败处理。
  • 选择工具时同时计算人工复核成本。
  • 仓库知名度、星标和最近提交不能单独证明适合。
  • 没有合适工具时，输出可执行的人工流程而不是硬选。
  • Select tools based on tasks and inputs first, not tasks based on existing repo names.
  • Local research-lab is an optional integration, not a prerequisite for operation.
  • Code being installable or compilable does not mean real-time APIs, models, or data sources are available.
  • Tool outputs must retain inputs, versions, parameters, and sources.
  • Do not confuse tool discovery and systematic review database coverage.
  • API metadata may be incomplete; cross-verify key identifiers.
  • Check rate limits, authentication, costs, and terms of service before batch runs.
  • Prioritize local or approved processing paths for sensitive data.
  • Use minimal sample tests for fields and failure handling in batch tasks.
  • Calculate manual review costs when selecting tools.
  • Repository popularity, star count, and recent commits alone cannot prove suitability.
  • When no suitable tool is available, output an executable manual process instead of forcing a selection.

输出

Output

  • 首选工具、回退和选择理由。
  • 环境、许可证、隐私和费用要求。
  • 最小测试、结果和停止条件。
  • Preferred tool, fallback solution, and selection reasons.
  • Environment, license, privacy, and cost requirements.
  • Minimal test, results, and stop conditions.

停止条件

Stop Conditions

  • 没有运行证据时，不声称工具当前可用。
  • 不在未获授权的平台上传敏感研究数据。
  • 不因缺少某个本地目录而停止工具路由。
  • 不自动安装、登录、付费或修改生产环境。
  • Do not claim that a tool is currently available without operational evidence.
  • Do not upload sensitive research data to unauthorized platforms.
  • Do not stop tool routing due to the absence of a certain local directory.
  • Do not automatically install, log in, pay, or modify the production environment.

独立运行

Standalone Operation

  • 默认不读取私人工作区、个人语料目录或预设 research-lab。
  • 用户提供的文件、文本、链接和数据是当前任务输入，不是安装依赖。
  • 网络不可用时，使用 Skill 内的稳定方法继续；需要当前事实的部分标记为待核验。
  • 本包内其他科研 Skill 存在时可以接续；单独安装时直接返回下一步说明，不停止当前任务。
  • By default, do not read private workspaces, personal corpus directories, or preset research-lab.
  • Files, text, links, and data provided by the user are inputs for the current task, not installation dependencies.
  • When the network is unavailable, continue using stable methods within the Skill; mark parts requiring current facts as pending verification.
  • Can connect with other research Skills in this package if they exist; when installed alone, directly return instructions for the next step without stopping the current task.

来源纪律

Source Discipline

  • 把用户材料、公开来源、当前推断和未知事项分开。
  • 报告规范只检查报告透明度，不自动证明设计质量。
  • 公开来源摘要保存在 Skill 内；需要版本、费用、政策、API 或期刊现状时回到官方页面。
  • 不生成不存在的论文、数据、DOI、工具运行结果或期刊要求。
  • Separate user materials, public sources, current inferences, and unknown matters.
  • Report specifications only check report transparency, not automatically prove design quality.
  • Public source abstracts are stored within the Skill; return to official pages when needing versions, costs, policies, APIs, or current journal requirements.
  • Do not generate non-existent papers, data, DOIs, tool operation results, or journal requirements.