deep-insight-multi-criteria-scoring
Score gaps on multiple dimensions (importance, feasibility, novelty, urgency, impact) using weighted multi-criteria decision analysis.
Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Score gaps on multiple dimensions (importance, feasibility, novelty, urgency, impact) using weighted multi-criteria decision analysis.
Simulate multiple stakeholder perspectives evaluating a research gap, method, or proposal. Identifies blind spots from single-perspective analysis.
Paper metadata and abstract-level overview. Import of literature-engine/literature-overview skill. Abstracts only — no substantive claims without deeper reading.
Full-text paper reading via three-pass Keshav method. Import of literature-engine/literature-research skill. Authoritative source for claims about paper content.
AI-powered paper summary and search. Import of literature-engine/literature-search skill. AI summary level — cite as "AI-extracted" not "paper states".
Combine multi-axis perturbation data into a multi-dimensional validity description with boundary conditions and interaction effects.
Map multi-dimensional validity envelopes — define variation axes, perturb systematically, measure degradation, construct boundary surface.
Quick web scanning for landscape understanding. Import of web-browsing/web-search skill. Snippets only — no conclusions from snippets alone.
Precise, targeted investigation of a specific sub-problem — few papers, all read in full depth. High paper-research ratio (50% deep-read rate). Use when the user knows exactly what they need to understand and requires detailed technical analysis with equations, hyperparameters, and specific claims extracted.
Full-page web reading for non-academic perspectives — blogs, tech reports, product pages, industry analysis. Spawns a subagent to read pages in isolated context. Hard constraint: at least 30 web pages read in full.