Analyze large document corpora with multi-pass synthesis and citation provenance.
npx skills add https://github.com/Seth090502/osanwe-public --skill corpus
Please help me install this Agent Skill. Skill: corpus Source: https://github.com/Seth090502/osanwe-public/tree/main/.claude/skills/corpus Command: npx skills add https://github.com/Seth090502/osanwe-public --skill corpus
This Skill facilitates systematic analysis of multi-document corpora (10-150 documents), providing verifiable multi-pass synthesis with citation provenance.
Run the following command to initiate the analysis: /corpus init --name <slug> --source <source-dir>
High-intent search queries and answers about installing and using this skill.
Systematically analyze large document corpora by applying multi-pass synthesis and citation provenance. This approach processes 10-150 documents, ensuring every claim is cited and evidential class tagging is correct for legal discovery sets.
Multi-pass synthesis for FOIA archives is an analysis mechanism that iteratively reviews declassified records to extract verifiable claims. It includes adversarial review at multiple stages to maintain accuracy and structured output for provenance tracking.
Yes, you can process regulatory dockets and court filings using Claude Code with subagents. The Skill enforces model floor requirements and leverages subagents to handle structured output and provenance tracking for mass-document analysis.
Initiate corpus analysis on intelligence community releases by running the command `/corpus init --name <slug> --source <source-dir>`. This sets up the environment to systematically analyze the documents with verifiable multi-pass synthesis.
The maximum document limit for provenance tracking in corpus analysis is 150 documents. This Skill is designed to systematically analyze and synthesize multi-document corpora ranging from 10 to 150 documents.
Yes, document analysis ensures citation discipline for every extracted claim. The Skill enforces strict citation provenance and evidential class tagging, incorporating adversarial review at multiple stages to maintain accuracy.
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