Exécutez n'importe quel Skill dans Manus
en un clic

Exécutez n'importe quel Skill dans Manus en un clic

convergence-paper-research

Étoiles378

Forks27

Mis à jour16 juin 2026 à 12:51

Full-text deep reading of methodology papers — complete understanding of algorithms, proofs, and implementation details.

Installation

Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.

Exécuter dans Manus

Source

yogsoth-ai

yogsoth-ai/de-anthropocentric-research-engine

Ouvrir le dépôt GitHub Voir les dépôts du créateur

Téléchargement

Exécuter dans Manus

Métiers associésSOC

Basé sur la classification professionnelle SOC

Analystes en gestionProfessions des affaires et des opérations financières·SOC 13-1111

SKILL.md

readonly

name	convergence-paper-research
description	Full-text deep reading of methodology papers — complete understanding of algorithms, proofs, and implementation details.
execution	import
source	skills/literature-research/SKILL.md
dependencies	{"sops":["literature-research"]}

Paper Research

Import SOP. Execution delegated to literature-engine/paper-research.

See source repo for full SKILL.md and prompt.md.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
literature-research	Deep literature research — raw full text reading and targeted PDF queries for rigorous analysis

Plus depuis ce dépôt

même dépôt

isomorphism-falsification

yogsoth-ai/de-anthropocentric-research-engine

Strategy: Attack an isomorphism claim by demanding an explicit structure-preserving map and trying to break it. Targets any multi-language claim of the form 'X ≅ Y ≅ … across N mathematical languages'. Forces the claim to either earn the word 'isomorphism' or be demoted to 'analogy'. Methods: category theory (functor/natural-iso criteria), model theory, Lakatos monster-barring.

2026-06-21378

adversarial-debate-truthseeking

yogsoth-ai/de-anthropocentric-research-engine

Strategy: Dialectic engine retuned for truth-seeking, not survival. A defender steelmans a claim into its MOST falsifiable form, a critic attacks to refute it, a judge classifies the exchange into BROKEN/CORROBORATED/UNFALSIFIABLE — the judge does NOT pick a winner or score persuasiveness. Methods: Irving debate (repurposed), Toulmin argumentation, Mayo severe testing.

2026-06-21378

circular-validation-audit

yogsoth-ai/de-anthropocentric-research-engine

Strategy: Run BEFORE building any validator (sandbox/simulation/benchmark). Builds a non-circularity matrix of theory-claim × validator-assumption to detect when a validator would 'confirm' a theory only because it was built on the theory's own premises. A circular validator's PASS carries zero evidential weight. Methods: Cartwright nomological machines, Winsberg sanctioning-of-simulations, tautology detection.

2026-06-21378

elegance-trap-probe

yogsoth-ai/de-anthropocentric-research-engine

Strategy: Attack a beautiful unified result on the suspicion that its beauty is the bug. Distinguishes EARNED simplicity (forbids/predicts/subsumes) from DECORATIVE simplicity (re-describes/relabels/accommodates). Directly serves the Occam aesthetic by making it a falsifiable bar, not a vibe. Methods: Sober parsimony-as-evidence, MDL, Meehl risky prediction, accommodation-vs-prediction.

2026-06-21378

falsification-first-stress-test

yogsoth-ai/de-anthropocentric-research-engine

Campaign: Truth-seeking adversarial validation for scientific research artifacts (NOT publication defense). Core question: Where have we fooled ourselves, and is each load-bearing claim even falsifiable? Win-condition is INVERTED from survival/resilience to active refutation. Methods: Popper falsificationism, Lakatos Proofs and Refutations, Mayo severe testing, Platt strong inference.

2026-06-21378

independent-convergence-audit

yogsoth-ai/de-anthropocentric-research-engine

Strategy: Attack the evidential weight of an 'independent convergence' claim. When N reasoning paths all reach the same conclusion, the confidence boost is real only if the paths were actually independent. Measures shared-prior / shared-blindspot contamination and corrects the over-counted confidence. Methods: Bayesian agreement-as-evidence, correlated-error analysis, jury theorem assumptions.

2026-06-21378