Best fit
- 以证据为先的ECC当前状态研究工作流程。当用户希望基于当前公开证据和提供的本地上下文获取最新事实、比较、丰富信息或建议时使用。
affaan-m/ECC
research-ops
以证据为先的ECC当前状态研究工作流程。当用户希望基于当前公开证据和提供的本地上下文获取最新事实、比较、丰富信息或建议时使用。
54Collecting
See how to use itView GitHub source
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/research-ops"Automated source guide
Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
Turn research-ops's source instructions into a guide you can follow
According to the pinned SKILL.md from affaan-m/ECC: 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。
npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/research-ops"Bring this context
- A concrete task that matches the documented purpose of research-ops.
- The files, examples, or context the task depends on.
- Your constraints, target environment, and definition of done.
Expected outputs
- A result that follows the pinned research-ops instructions.
- A concise record of assumptions, inputs used, and unresolved questions.
- A final check against the source workflow and relevant permission signals.
Key source sections
Read research-ops through these 5 source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
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
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
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
工作流
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
已有证据的事实需要验证的内容未解决的问题
1. 从用户已提供的信息出发
已有证据的事实
已有证据的事实需要验证的内容未解决的问题
SkillSignal prompt templates
Provide the task, context, and acceptance criteria
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use research-ops to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned research-ops source to [task]. Pay particular attention to these source sections: “技能栈”, “使用时机”, “防护措施”, “工作流”, “1. 从用户已提供的信息出发”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current research-ops result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
Verify each item before delivery
The task matches the purpose documented in the SKILL.md.
The source section “技能栈” has been checked.
The source section “使用时机” has been checked.
The source section “防护措施” has been checked.
The source section “工作流” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
When another Skill is the better fit
research-ops
Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailresearch-ops
証拠優先のECC現状調査ワークフロー。ユーザーが現在の公開証拠と提供されたローカルコンテキストに基づいて最新の事実、比較、情報の充実、または推奨事項を求める場合に使用する。
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailab-testing
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program
A separate implementation from coreyhaines31/marketingskills; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
What does research-ops do?
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。
How do I start using research-ops?
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "docs/zh-CN/skills/research-ops". Inspect the command and pinned source before running it.
Which Agent platforms does it declare?
No dedicated Agent platform is declared in the pinned source record.
Repository stars
234,327
Repository forks
35,711
Quality
54/100
Source repository last pushed
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Documentation15/30
Specificity7/25
Maintenance20/20
Trust signals12/25
Compare before choosing
Related Agent Skills and source variants
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
research-ops by affaan-m
Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.
research-ops by affaan-m
証拠優先のECC現状調査ワークフロー。ユーザーが現在の公開証拠と提供されたローカルコンテキストに基づいて最新の事実、比較、情報の充実、または推奨事項を求める場合に使用する。
ab-testing by coreyhaines31
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program
churn-prevention by coreyhaines31
When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when the user mentions 'churn,' 'cancel flow,' 'offboarding,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'retention,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'people keep canceling,' 'churn rate is too high,' 'how do I keep users,' or 'customers are leaving.' Use this whenever someone is losing subscribers o
design-review by event4u-app
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 1 min
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-research、exa-search 或 market-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
- Source repo
- affaan-m/ECC
- Skill path
- docs/zh-CN/skills/research-ops/SKILL.md
- Commit SHA
- 4e973d3eaf92
- Repository license
- MIT
- Data collected