Search project documentation by tags, titles, and keywords using a pre-built index.
npx skills add https://github.com/1ambda/dataops-platform --skill doc-search
Please help me install this Agent Skill. Skill: doc-search Source: https://github.com/1ambda/dataops-platform/tree/main/.copilot/skills/doc-search Command: npx skills add https://github.com/1ambda/dataops-platform --skill doc-search
This Skill addresses the inefficiency and token waste associated with manually searching through large documentation files by providing a token-efficient search mechanism.
PATTERNS.md file (which could cost thousands of tokens), you can use this Skill to pinpoint the exact relevant sections, costing only a few hundred tokens.Search for documentation related to "hexagonal architecture" using the provided Python CLI command.
High-intent search queries and answers about installing and using this skill.
Searching project documentation efficiently requires a pre-built index to pinpoint exact sections by tags, titles, or keywords, saving significant tokens compared to reading full files.
Finding specific architectural patterns in large markdown files is best done by querying a pre-built index using keywords or tags, which retrieves only the precise relevant sections.
Yes, you can retrieve documentation by document type and project scope, as the search mechanism supports targeted queries across tags, titles, projects, and keywords.
Yes, you need a pre-built index to enable token-efficient searching, which allows the retrieval mechanism to locate specific keywords and sections without scanning full documents.
Token-efficient documentation search contrasts with reading full files by using a pre-built index to return exact sections, reducing token consumption from thousands to a few hundred.
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