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nimag AI Skills

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Browse published AI skills maintained by nimag. Compare skill descriptions and follow each source repository for setup instructions.

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Indexed skills

7

Repository stars across indexed skills

0

Repository forks

0

Average repository stars per indexed skill

0

Repository stars may repeat across skills from the same repository.

7 skills

Browse skills

GitHub

new-agent

nimag

Create a new underwriting agent (assets, credit, collateral) following established patterns. Use when implementing new agents, extending the system with new document analysis capabilities, or understanding how...

GitHub

gemini-file-api

nimag

Work with Gemini File API for document caching and efficient multi-query workflows. Use when discussing file uploads, caching strategies, token optimization, or comparing File API vs inline document approaches.

GitHub

guideline-lookup

nimag

Look up Fannie Mae underwriting guidelines for income, assets, credit, or collateral verification. Use when questions involve DTI, income documentation, asset requirements, credit standards, or any B3/B4 guidel...

GitHub

run-underwriter

nimag

Run the mortgage underwriter CLI to analyze loan documents. Use when testing document analysis, debugging underwriter output, or processing loan files. Covers setup, execution, and result interpretation.

GitHub

run-benchmark

nimag

Run and interpret the File API vs Inline benchmark for Gemini performance testing. Use when discussing performance optimization, caching strategies, or comparing document upload approaches.

GitHub

document-types

nimag

Understand and work with mortgage document types and classification. Use when asking about document types, adding new document support, debugging classification, or understanding what DocType constants mean.

GitHub

streaming-output

nimag

Output format markers for the real-time stream formatter. Use when building prompts for streaming analysis to ensure proper progress display. Documents the patterns that StreamFormatter detects and displays.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.