tech-search

You can automate deep technical research by orchestrating a phase-driven workflow that decomposes queries, performs parallel web searches, synthesizes findings, and outputs structured documentation artifacts automatically.

Yes, this research pipeline runs entirely with built-in capabilities, requiring zero external dependencies to execute web searches, extract content, and generate structured documentation.

The research pipeline structures output artifacts by saving them as documentation files under the docs/research/{YYYY-MM-DD}-{slug}/ directory, ensuring generated findings are auditable and reusable.

The workflow for decomposing technical topics involves an auto-clarify phase, parallel web searches across sub-topics, an evaluation stage, and a final synthesis step to document structured findings comprehensively.

You need a phase-driven workflow for technical research when tackling complex topics that require systematic decomposition, parallel data gathering, content evaluation, and structured synthesis into reusable documentation.

A key limitation of using built-in capabilities for content extraction is the reliance on internal webfetch mechanisms, which may lack the advanced parsing depth of specialized external scraping libraries.