mcm-c-web-researcher
Web research & external data acquisition specialist for MCM/ICM Problem C. Use ONLY when the problem statement explicitly allows or requires external/exogenous data, or when you need credible background context that does not enter the model inputs. Produces cited sources, cleaned datasets, and a reproducibility log.
npx skills add majiayu000/claude-skill-registry --skill mcm-c-web-researcher --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MCM/ICM C题网络搜索高手（外生数据/背景） ## 触发条件（必须满足其一） - 题面明确允许或鼓励：*“You may choose to include additional information/other data …”* - 题面明确要求补充：外部指标/公开统计/规则文本/背景数据 - 只需要背景解释（不进入模型输入），例如定义、规则、制度、专业知识 如果题面写了类似 **“ONLY DATA YOU SHOULD USE”**： - **禁止下载并用于建模输入**。 - 只允许用外部信息做背景解释（并引用）。 ## 工作流 1. **先写“数据需求说明”** - 需要什么字段？时间范围？粒度？单位？ - 用途是什么（进入模型 or 仅背景）？ 2. **选择权威来源（优先级）** - 官方机构/学术机构/公开数据库（.gov/.edu/国际组织/官方统计） - 其次：主流媒体/行业报告（需交叉验证） - 避免：无出处博客/二手转载 3. **记录可复现日志（必须输出）** - 查询关键词/URL - 下载时间 - 原始文件名与校验信息（如 hash 可选） - 清洗步骤（缺失、口径转换） 4. **数据合并与口径统一** - 与题目附件键对齐（国家代码、日期、ID） - 明确每一步转换（单位、币种、通胀、汇率、重命名） 5. **引用与披露** - 每张外部数据图表都要：来源、访问日期、URL。 - 报告中明确外部数据对结论的影响（敏感性分析）。 ## 交付物 - `sources.md`：可复现日志 + 引用条目 - 外部数据清洗后的文件（建议单独目录） - 合并口径说明（能让别人复现你的合并过程）
- 触发条件（必须满足其一）
- 工作流
- 交付物
What does the mcm-c-web-researcher skill do?
Web research & external data acquisition specialist for MCM/ICM Problem C. Use ONLY when the problem statement explicitly allows or requires external/exogenous data, or when you need credible background context that does not enter the model inputs. Produces cited sources, cleaned datasets, and a reproducibility log.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mcm-c-web-researcher --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.