Acquire external data and credible sources for MCM/ICM Problem C analysis.
npx skills add https://github.com/twj0/2026mcm --skill mcm-c-web-researcher
Please help me install this Agent Skill. Skill: mcm-c-web-researcher Source: https://github.com/twj0/2026mcm/tree/main/.windsurf/skills/mcm-c-web-researcher Command: npx skills add https://github.com/twj0/2026mcm --skill mcm-c-web-researcher
This skill streamlines the acquisition of external data and credible sources to support MCM/ICM Problem C analyses, ensuring sources are traceable and suitable for background context that does not enter model inputs.
Specify data needs, select authoritative sources, and document a reproducibility log to accompany your Problem C analysis.
High-intent search queries and answers about installing and using this skill.
To gather credible external data for MCM/ICM Problem C analysis, define specific data requirements including fields and time ranges, select authoritative primary sources like government or academic institutions, and record reproducible steps to ensure traceability and context without injecting exogenous model inputs.
The best way to maintain citation provenance for web-research data sourcing is to produce traceable citations that explicitly record URLs and access dates, while maintaining a reproducibility log that documents all data transformations and potential limitations for auditability.
Define data requirements for reproducible web research by specifying the exact fields, time ranges, granularity, and intended use. This specification guides the collection of authoritative external data and ensures transformations remain consistent and traceable.
Yes, you can use external background data to contextualize MCM/ICM Problem C analysis without affecting model inputs. The workflow ensures gathered indicators support background context only, preventing exogenous data from directly entering the model.
Cross-verify authoritative sources for external data collection by prioritizing primary institutions such as government agencies, academic organizations, and international bodies. This approach ensures the acquired background data is credible and suitable for Problem C context.
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