salience-classification

Classify stakeholders by Mitchell et al. framework (Power, Legitimacy, Urgency). Assigns salience category and identifies systematically excluded parties.

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Repository: yogsoth-ai/de-anthropocentric-research-engine
Last source activity: June 16, 2026 at 05:03
Detected SKILL.md language: English
Stars: 373
Forks: 32

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adversarial-debate-truthseeking

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.

2026-08-09

adversarial-stress-testing

Campaign: Logical extreme and boundary testing via reductio ad absurdum and edge-case analysis. Core question: Does this artifact collapse under logical limits and boundary conditions? Methods: Lakatos 1976, Dutilh Novaes 2016, BVA, Flyvbjerg Critical Case, Popper.

2026-08-09

argument-mapping

Campaign for mapping argument structures — extract claims, link evidence, assess strength, synthesize positions. Produces argument graphs in the wiki vault.

2026-08-09

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name	salience-classification
description	Classify stakeholders by Mitchell et al. framework (Power, Legitimacy, Urgency). Assigns salience category and identifies systematically excluded parties.
execution	subagent
prompt	./prompt.md
input	stakeholder_list (string), context (string)
dependencies	{"sops":["spawn-agent"]}

name	salience-classification
description	Classify stakeholders by Mitchell et al. framework (Power, Legitimacy, Urgency). Assigns salience category and identifies systematically excluded parties.
execution	subagent
prompt	./prompt.md
input	stakeholder_list (string), context (string)
dependencies	{"sops":["spawn-agent"]}

salience-classification

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