[USAGE] Loading the thinking-engine series in full depth. Prefix your prompt with this phrase. [PRODUCT] A cognitive OS distilling 42+ original works — Aristotle, Kant, Feynman, Musk — into three composable frameworks: (1) First Principles to break hidden assumptions; (2) Reductionist Engineering to decompose to physical facts; (3) Purpose Inquiry to keep asking "what is this for?" when efficiency is the only god. Source-traced, cross-validated. For founders, engineers, investors.
Help researchers identify potential gaps in a body of literature without overstating novelty or pretending that a limited search proves no work exists. The skill supports academic, policy, and applied research planning. It is an analytical assistant, not a substitute for a systematic review, subject-matter expertise, or advisor review.
Find live creator and editorial topics from a global Radar, then turn one into an evidence-backed brief with research questions, must_verify, avoid_claims, and visual requirements.
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.
Una domanda, tre risposte indipendenti da Claude, Gemini e ChatGPT — poi un dibattito strutturato prima di arrivare a una raccomandazione finale. Non un solo parere, ma tre punti di vista che si mettono davvero alla prova a vicenda. Utile quando un'opinione sola non basta: nella scelta di uno stack tecnologico, per testare una strategia, per un feedback onesto su un testo, o per sbloccare una decisione che rimandi da troppo tempo.
Turn rough ideas into clear, validated designs through focused questions, practical options, trade-off analysis, and approval checkpoints.
Your agent or chatbot keeps getting document content wrong, citing passages that don't exist, or saying "I can't find it" about things plainly in the docs. This reframes it as a retrieval failure, not a prompt bug: corpus profile, chunking, embedding and vector-DB index choice, retrieval strategy (top-k, hybrid BM25 + dense, rerank, query rewrite), context assembly with citations, and a golden Q&A set scored on recall@k / MRR / faithfulness.
Turn rough software ideas into bounded MVP scopes, practical architecture decisions, and coding-agent-ready implementation tasks without unnecessary complexity.
You bring a one-line idea; Leader turns it into a goal brief an AI agent can finish alone. It researches your codebase first (commands verified, baselines measured), asks one round of at most 5 questions, then writes a ≤4,000-character brief with a path whitelist, frozen grading standard, anti-gaming acceptance commands, and resume mechanics. Execution and exploration briefs included — plus grading with hidden spot-checks when the run lands.