Market Research from affaan-m’s ECC project. Use it for market sizing, competitive analysis, investor due diligence, technology scans, and other business research. It gathers sourced evidence, flags stale data, includes risks and counterarguments, separates facts from inference, and turns findings into clear, decision-oriented recommendations.
This Skill is built on practical expertise in claim-evidence mapping, source authority labeling, quote verification, citation hygiene, recency checks, and hallucination-risk control. It helps AI agents turn research, reports, summaries, briefs, and marketing drafts into auditable claim ledgers where every important claim is matched to a source, date, quote/paraphrase boundary, and confidence label.
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.
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 web research into verified answers, evidence trails, durable memory proposals, long-form notes, and reusable agent playbooks.
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.
Produce structured browser QA evidence for web flows, landing pages, and app screens.
Forces AI agents to prove a task is actually done before declaring it complete. 4-phase gate: instruction reconciliation, evidence anchoring (values only readable from the actual output), self-scoring (A/B/C/D), and reverse witness check. Eliminates "done" declarations that aren't.
Turn your pasted research proposal into clear, natural academic prose while preserving your real evidence, methods, limits, and required claims.