At McKinsey, a recommendation was only as good as the research under it. So I learned to do it right: scope the real question, pull from independent sources, triangulate what's actually true, and ground every claim in evidence. This Skill runs that discipline for you — breaking down a question, comparing sources, verifying claims, and writing a structured report with citations you can check. Decisions on evidence, not vibes.
Most AI research sounds confident and quietly makes up its citations. This does the opposite: paste your question (and any sources you have) and get a structured brief — summary, findings, synthesis, limitations, recommendations — that reasons from solid knowledge and the sources YOU provide, and never fabricates a citation. Anything it can't ground is marked [UNVERIFIED], so you know what's solid and what to check. For founders, analysts, and anyone who needs a brief that survives scrutiny.
Research Paper Writing from NousResearch’s Hermes project. Use it to turn an ML/AI research idea into a submission-ready paper, from literature review and experiment design to drafting, revision, citations, and conference formatting. Built for venues like NeurIPS, ICML, ICLR, ACL, AAAI, and COLM, with an emphasis on grounded claims and verified references.
Serenity-style supply-chain bottleneck research workflow for public-market themes. It maps capex flows, finds overlooked upstream chokepoints, audits public/private/acquired players, separates evidence from inference, scores Bottleneck Strength and Research Priority, writes bear cases first, and outputs Markdown or optional HTML dashboards. Educational research only, not financial advice.
Turn your pasted research proposal into clear, natural academic prose while preserving your real evidence, methods, limits, and required claims.
Before a client spent a dollar entering a market, my job at McKinsey was to answer one question: is this real? Excitement isn't demand, and a big TAM slide isn't a market. The discipline is to size it bottom-up, segment down to who actually buys, check what they'll pay, and weigh the competition. This Skill runs that validation on your market — sizing it, separating real demand from hype, profiling the segments worth chasing, and weighing whether to enter. A decision you can defend, not a vibe.
Revise your pasted PhD research statement into clear, evidence-bound prose while preserving your real projects, results, and research direction.
Research Context Management helps agents run long, evidence-heavy research without losing sources, claims, contradictions, or next steps. It builds a durable workspace with a source ledger, batch notes, claim matrix, contradiction register, working brief, and handoff checkpoint, so deep research, audits, market scans, and reports can be reviewed, resumed, and turned into reliable outputs with clearer evidence trails.
Turns vague software ideas into clear, implementation-ready specifications for AI coding agents, asking only the questions that matter and clearly separating your requirements from AI assumptions and engineering recommendations.