Results for “should-assertions”

Conducts exhaustive multi-source research on a topic, tagging each claim with source class and confidence, and includes a mandatory adversarial counterpoint and open-questions list.

Verifies claims, articles, screenshots, and URLs through source-grounded analysis with an evidence ledger, source credibility evaluation, and manipulation detection. Supports quick checks, full fact-check cards, two-source comparisons, and prebunking in multiple languages and policy contexts.

Red-team a PRD, roadmap, or strategy by attacking its load-bearing assumptions before reality does, ranking failure modes by impact, likelihood, and cheapness to test.

Verify the accuracy of claims and statements by extracting individual assertions, identifying authoritative sources, cross-referencing evidence, and assigning confidence-scored verdicts. Use when the user requests fact checking or provides relevant inputs for this workflow.

Simulates a NeurIPS/SC/ICSE-style peer review with five reviewer personas (HPC, ML, Stats, Reproducibility, Devil's Advocate) that verify every claim against actual result data before submission.

Verify the article’s factual, product, citation, and link integrity before visual production.

Create a structured H2/H3 outline with BLUF openers and MECE coverage, bound by the research dossier's beat spec. Triggered after /research and /brand-reference.

Produce an answer-first outline that makes the research decision executable.

Provides PhD-level mathematical analysis for machine learning, including optimization theory, statistical learning theory, and convergence proofs.