900+ pure-markdown skills for autonomous AI research, organized as 9 freely-composable packages over a 4-layer hierarchy (Campaign → Strategy → Tactic → SOP). Non-linear orchestration with backtracking, 6 MCP integrations. The AI is the researcher — you set the direction.
- ✓Open-source license (Apache-2.0)
- ✓Actively maintained (<30d)
- ✓Healthy fork ratio
- ✓Clear description
- ✓Topics declared
git clone https://github.com/yogsoth-ai/de-anthropocentric-research-engine && cp de-anthropocentric-research-engine/*.md ~/.claude/agents/24 items en este repositorio
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems
Remove components one by one from a system, record the response/impact
Classify assumptions on 2 axes — load-bearing (how much conclusion depends
Extract abstract principles from concrete domain cases. Strips domain-specific
Move between concrete and abstract framings — 3 levels up (Why?) and
Abstract biological principle to design principle. Bridge from biology
Compute Risk Priority Number (RPN = S x O x D), classify failure modes
Iteratively select maximally informative pairs, execute comparisons,
Structured debate protocol that constructs an advocate, deploys critic
Strategy: Progressive pressure escalation — starts with surface-level
Strategy: Role-play attacks from hostile personas — competing lab researcher,
Tactic: Construct detailed hostile persona, attack artifact from that
Campaign: Logical extreme and boundary testing via reductio ad absurdum
Construct the strongest possible case for a rejected candidate or counter-position.
Resumen de Subagents
Lo que la gente pregunta sobre de-anthropocentric-research-engine
¿Qué es yogsoth-ai/de-anthropocentric-research-engine?
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yogsoth-ai/de-anthropocentric-research-engine es subagents para el ecosistema de Claude AI. 900+ pure-markdown skills for autonomous AI research, organized as 9 freely-composable packages over a 4-layer hierarchy (Campaign → Strategy → Tactic → SOP). Non-linear orchestration with backtracking, 6 MCP integrations. The AI is the researcher — you set the direction. Tiene 389 estrellas en GitHub y se actualizó por última vez 18d ago.
¿Cómo se instala de-anthropocentric-research-engine?
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Puedes instalar de-anthropocentric-research-engine clonando el repositorio (https://github.com/yogsoth-ai/de-anthropocentric-research-engine) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar yogsoth-ai/de-anthropocentric-research-engine?
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Nuestro agente de seguridad ha analizado yogsoth-ai/de-anthropocentric-research-engine y le ha asignado un Trust Score de 97/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene yogsoth-ai/de-anthropocentric-research-engine?
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yogsoth-ai/de-anthropocentric-research-engine es mantenido por yogsoth-ai. La última actividad registrada en GitHub es de 18d ago, con 5 issues abiertos.
¿Hay alternativas a de-anthropocentric-research-engine?
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Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.
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