Deep Researchv1.0.3

A VC analyst uses Deep Research to investigate a startup's GitHub activity, release cadence, funding announcements, and conflicting press coverage before an investment decision. The skill produces a cited report with source families to avoid double-counting related URLs, labeling contested or single-source claims explicitly.

A PhD student or R&D scientist uses the skill to map an aspect graph across arXiv, OpenReview, and official benchmark pages, then adversarially searches for negative replications and limitations. Evidence IDs like [E0001] make every high-impact claim auditable in the final dissertation section.

An engineering team evaluates competing Python libraries by inspecting repositories, changelogs, open issues, and release notes for maintenance health and CVE history. The skill enforces primary-source preference and flags stale or contested compatibility claims before adoption.

A compliance officer tracks evolving data-privacy regulations across jurisdictions, cross-checking official filings, standards bodies, and legal commentary. The skill's freshness requirement and source-family rules prevent relying on a single summary blog when primary regulatory sources are available.

An investigative reporter submits a politician's or company's public claim to the skill, which seeds broad searches and runs adversarial counterevidence queries. The output is a memo that labels each claim as likely, contested, weak, or unknown with evidence IDs and source attribution.

A subscription product that wraps Deep Research in a hosted interface, letting teams run, store, and share auditable research runs with role-based access. It targets consultancies, VC firms, and corporate strategy teams that need repeatable, cited outputs.

A boutique firm sells human-overseen deep-dive due diligence, literature reviews, and technical audits delivered as PDF reports generated with this skill. The auditable ledger lets the firm price by report complexity (effort levels: quick, standard, deep, exhaustive).

A developer-facing API that exposes the research loop as a service for legal, scientific, or financial applications, returning structured claims, evidence IDs, and source graphs. It monetizes via metered API calls and enterprise SLAs for latency and source coverage.