Automates corporate intelligence collection and synthesis into structured JSON evidence manifests for consultants.
npx skills add https://github.com/MB-uc/mercury --skill ms-brief
Please help me install this Agent Skill. Skill: ms-brief Source: https://github.com/MB-uc/mercury/tree/main/mercury/skills/ms-brief Command: npx skills add https://github.com/MB-uc/mercury --skill ms-brief
This skill eliminates the manual effort required to gather and synthesize corporate intelligence, ensuring consultants have a structured, evidence-backed foundation before client engagements.
Run the ms-brief skill by providing the target company name to initiate the automated research and evidence collection pipeline.
High-intent search queries and answers about installing and using this skill.
Automated corporate research compiles company identity, benchmark data, and digital footprint intelligence into a structured evidence manifest. It synthesizes web search and site crawling outputs to provide a structured foundation for strategic consulting engagements.
An evidence manifest is a structured JSON document compiling company identity, benchmark data, and situational awareness. It supports competitive analysis by generating a feature matrix that highlights peer competitive positioning within a specific sector.
Site structure discovery executes automated web searches and site mapping to identify key digital assets. It performs document inventory probes to detect material events, requiring integration with web search and site crawling capabilities.
Yes, you can use SQL-based benchmark datasets to facilitate comparative analysis. The pipeline identifies sector peers and queries the benchmark datasets to generate a feature matrix highlighting competitive positioning against the target company.
No, automated corporate intelligence gathering requires integration with web search, site crawling, and SQL-based benchmark datasets. These dependencies are necessary to perform multi-stage pipeline collection and produce structured evidence manifests.
Limitations of automated peer benchmarking include dependency on external web search and site crawling access. It requires SQL-based benchmark datasets for feature matrix generation and cannot synthesize corporate intelligence without these integrated data sources.
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