Full-text deep reading of methodology papers — complete understanding of algorithms, proofs, and implementation details.
Skills in this repository
yogsoth-ai/de-anthropocentric-research-engine - Page 4
SkillsMP has collected 961 skills from yogsoth-ai/de-anthropocentric-research-engine. Open a skill to review its source and details.
yogsoth-ai/de-anthropocentric-research-engineShowing 40 of 961 collected skills.
Paper AI summary reading — deeper understanding of specific methodology papers without full-text commitment.
Determines when to stop iterating — coverage threshold met or marginal returns diminishing. Shared across all campaigns.
Build a complete scoring matrix through criterion definition, weighting, scoring, normalization, and sensitivity testing.
Tests conclusion robustness by perturbing parameters and observing rank changes. Shared across scoring, portfolio, and steel-manning campaigns.
Deep web research with full-page extraction — detailed methodology guides, tutorials, implementation references.
Quick web scan to discover relevant pages — methodology references, case studies, best practices for convergence methods.
Surface, perturb, and prioritize assumptions by disruption potential. Orchestrates assumption surfacing → perturbation → sacred cow identification → prioritization.
Perturb each assumption, observe system response. Systematic stress-testing of assumptions to reveal fragility and opportunity.
Enumerate implicit assumptions in a problem statement or existing solution. Produces categorized assumption inventory (physical, social, temporal, economic, technical).
Catalog all known solutions/methods in a domain with performance, applicability, and limitations.
Systematically enumerate parameter dimensions and generate viable combinations. Orchestrates parameter extraction → value enumeration → compatibility assessment → synthesis.
Pairwise consistency evaluation to reduce solution space by identifying and removing inconsistent combinations.
Evaluate pairwise value consistency (logical/empirical/normative)
Identify factors and their levels for a problem, then design an experiment matrix for systematic exploration.
Systematically catalog all failure modes in a domain or method, producing a classified failure taxonomy.
Score ideas on novelty dimensions — structural distance from known solutions, conceptual surprise, domain-crossing depth. Produces ranked novelty assessment.
Abstract-level paper scanning for broad coverage. Import of literature-engine/literature-overview skill. Abstract-level only — no methodology conclusions from abstracts.
Deep paper analysis with full text reading. Import of literature-engine/literature-research skill. Full text access — required for quoting results, verifying claims, extracting detailed methodology.
Mid-depth paper analysis via AI-generated summaries. Import of literature-engine/literature-search skill. Reads AI summary — sufficient for methodology understanding but not for quoting specific results.
Rotate through reviewer/practitioner/theorist/time-machine/novice perspectives systematically. Ensures comprehensive viewpoint coverage.
Generate PO provocations and extract constructive movement. Orchestrates assumption surfacing → provocation creation → movement extraction → idea formation.
Determine when additional ideation yields diminishing returns. Analyzes latest idea batch against existing corpus to judge continue/near-saturation/saturated.
Deep web page analysis with full content extraction. Import of web-browsing/web-research skill. Must fetch full page via apify — no shortcuts.
Quick web scanning for landscape understanding. Import of web-browsing/web-search skill. Snippets only — no conclusions from snippets alone.
Systematically identify all assumptions in a method/model — structural, parametric, distributional, and scope assumptions.
Systematically extract all assumptions (stated, implicit, boundary, mathematical, practical) from a method or model.
One-at-a-time assumption perturbation — extract assumptions, define negations, re-derive conclusions under each negation, measure sensitivity. Identifies which assumptions are load-bearing.
Systematically extract implicit assumptions from methods, frameworks, or arguments. Identifies what is taken for granted without explicit justification.
Double-loop learning escalation — surface governing variables, generate counter-assumptions, test if problem dissolves under alternatives, score wickedness if it persists.
Systematic failure mode cataloging — generate boundary inputs, observe failures, cluster by mechanism, identify triggers, estimate frequency.
Identify research gaps via PICOS frameworks, concept matrices, evidence gap maps, keyword extraction, citation analysis, and topic modeling. Systematic discovery of what is missing in the literature.
Score and rank validated gaps on importance, feasibility, novelty, and urgency. Multi-criteria decision analysis with stakeholder confirmation.
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".
Generate de Bono lateral thinking provocations to challenge dominant ideas using escape, reversal, exaggeration, and distortion.
Combine multi-axis perturbation data into a multi-dimensional validity description with boundary conditions and interaction effects.