deepdive

To turn a vague research question into a documented investigation, you run a multi-phase pipeline that reframes the query, executes parallel sub-agent searches, and saves per-source files with verbatim quotes for traceable source triangulation.

The best way to validate strategic decision hypotheses is through adversarial review using Skeptic, Contrarian, and Gap-hunter roles, ensuring every atomic thesis is backed by at least three independent sources with credibility and bias scoring.

Source triangulation for technical deep-dives works by scoring multiple independent sources for credibility, recency, and bias, then cross-referencing their claims to produce auditable reasoning that catches overclaims.

Yes, you can use Claude Code to automate landscape mapping by generating atomic theses and per-source files saved to a reusable folder, enabling citation and traceability across future research projects.

Yes, hypothesis testing pipelines require the Anthropic and OpenAI dependencies, along with requests and pytest, to execute parallel sub-agent searches and verify citations during the investigation.

Ad-hoc web searching lacks auditable reasoning and reusable output structures, whereas a documented research pipeline enforces citation verification and saves findings to folders, preventing loss of context months later.