Source profileQuality 54/100

affaan-m/ECC/docs/zh-CN/skills/research-ops/SKILL.md

research-ops

Use it for research tasks; the detail page covers purpose, installation, and practical steps.

Source repository stars
234,327
Declared platforms
0
Static risk flags
0
Last source update
2026-07-27
Source checked
2026-07-28

Decision brief

What it does—and where it fits

当用户要求研究当前信息、比较选项、丰富人员或公司信息,或将重复查询转化为可监控的工作流时,使用此功能。

Best for

    Not for

    • Tasks that require unconfirmed production actions or broad system permissions.
    • Environments where the pinned source and install steps cannot be inspected.

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/research-ops"
    Safe inspection promptEditorial

    Inspect the Agent Skill "research-ops" from https://github.com/affaan-m/ECC/blob/4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38/docs/zh-CN/skills/research-ops/SKILL.md at commit 4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      技能栈

      在相关场景下,将这些 ECC 原生技能纳入工作流:

      exa-search:用于快速发现当前网络信息deep-research:用于多源综合并附带引用market-research:当最终结果应为建议或排序决策时使用
    2. 02

      使用时机

      用户提及“研究”、“查找”、“比较”、“我应该联系谁”或“最新情况” 答案依赖于当前的公开信息 用户已提供证据,并希望将其纳入新的建议中 任务可能具有重复性,应转为监控而非一次性查询

      用户提及“研究”、“查找”、“比较”、“我应该联系谁”或“最新情况”答案依赖于当前的公开信息用户已提供证据,并希望将其纳入新的建议中
    3. 03

      防护措施

      当新鲜搜索成本低廉时,不要依赖过时记忆回答当前问题 区分: 有来源的事实 用户提供的证据 推断 建议 如果答案已存在于本地代码或文档中,不要启动繁重的研究流程

      当新鲜搜索成本低廉时,不要依赖过时记忆回答当前问题区分:有来源的事实

    Permission review

    Static risk signals and limitations

    No configured static risk pattern was detected

    This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score54/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars234,327SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guidecatalog recordEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    affaan-m/ECC
    Skill path
    docs/zh-CN/skills/research-ops/SKILL.md
    Commit
    4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38
    License
    MIT
    Collected
    2026-07-28
    Default branch
    main
    View the original SKILL.md

    研究运营

    当用户要求研究当前信息、比较选项、丰富人员或公司信息,或将重复查询转化为可监控的工作流时,使用此功能。

    这是仓库研究栈的操作封装。它并非 deep-researchexa-searchmarket-research 的替代品;而是指示何时以及如何将它们结合使用。

    技能栈

    在相关场景下,将这些 ECC 原生技能纳入工作流:

    • exa-search:用于快速发现当前网络信息
    • deep-research:用于多源综合并附带引用
    • market-research:当最终结果应为建议或排序决策时使用
    • lead-intelligence:当任务针对人员/公司而非通用研究时使用
    • knowledge-ops:当结果需持久存储于后续上下文时使用

    使用时机

    • 用户提及“研究”、“查找”、“比较”、“我应该联系谁”或“最新情况”
    • 答案依赖于当前的公开信息
    • 用户已提供证据,并希望将其纳入新的建议中
    • 任务可能具有重复性,应转为监控而非一次性查询

    防护措施

    • 当新鲜搜索成本低廉时,不要依赖过时记忆回答当前问题
    • 区分:
      • 有来源的事实
      • 用户提供的证据
      • 推断
      • 建议
    • 如果答案已存在于本地代码或文档中,不要启动繁重的研究流程

    工作流

    1. 从用户已提供的信息出发

    将任何提供的材料规范化为:

    • 已有证据的事实
    • 需要验证的内容
    • 未解决的问题

    如果用户已构建部分模型,不要从零开始重新分析。

    2. 对请求进行分类

    在搜索前选择正确的路径:

    • 快速事实性回答
    • 比较或决策备忘录
    • 线索/丰富化处理
    • 重复监控候选

    3. 优先采用最轻量的有效证据路径

    • 使用 exa-search 进行快速发现
    • 当需要综合或多源信息时,升级至 deep-research
    • 当结果需以建议形式呈现时,使用 market-research
    • 当实际需求是目标排序或温暖路径发现时,转交至 lead-intelligence

    4. 报告时明确证据边界

    对于重要声明,说明其属于:

    • 有来源的事实
    • 用户提供的上下文
    • 推断
    • 建议

    对时效性敏感的答案应包含具体日期。

    5. 决定任务是否应保持手动

    如果用户可能反复提出相同的研究问题,请明确说明,并建议采用监控或工作流层,而非永远重复相同的手动搜索。

    输出格式

    问题类型
    - 事实性 / 比较性 / 补充性 / 监控性
    
    证据
    - 有来源的事实
    - 用户提供的上下文
    
    推论
    - 从证据中得出的结论
    
    建议
    - 答案或下一步行动
    - 是否应将其设为监控项
    

    常见陷阱

    • 不要将推断混入有来源的事实而不加标注
    • 不要忽略用户提供的证据
    • 不要对本地仓库上下文能回答的问题使用繁重的研究路径
    • 不要给出不含日期的时效性敏感答案

    验证

    • 重要声明需标注证据类型
    • 时效性敏感的输出需包含日期
    • 最终建议需与实际使用的研究模式匹配

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