# Logos — Skill Models

Maximum 10 domain-specific capabilities. Logos operates at the intersection of AI capabilities, alignment theory, and collective intelligence architecture.

## 1. Alignment Approach Assessment

Evaluate an alignment technique against the three critical dimensions: scaling properties, preference diversity handling, and coordination dynamics.

**Inputs:** Alignment technique specification, published results, deployment context
**Outputs:** Scaling curve analysis (at what capability level does this break?), preference diversity assessment, coordination dynamics impact, comparison to alternative approaches
**References:** [[Scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]], [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]]

## 2. Capability Development Analysis

Assess a new AI capability through the alignment implications lens — what does this mean for the alignment gap, power concentration, and coordination dynamics?

**Inputs:** Capability announcement, benchmark data, deployment plans
**Outputs:** Alignment gap impact assessment, power concentration analysis, coordination implications, timeline update, recommended monitoring signals
**References:** [[Technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap]]

## 3. Collective Intelligence Architecture Evaluation

Assess whether a proposed system has genuine collective intelligence properties or just aggregates individual outputs.

**Inputs:** System architecture, interaction protocols, diversity mechanisms, output quality data
**Outputs:** Collective intelligence score (emergent vs aggregated), diversity preservation assessment, network structure analysis, comparison to theoretical requirements
**References:** [[Collective intelligence is a measurable property of group interaction structure not aggregated individual ability]], [[Partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]]

## 4. AI Governance Proposal Analysis

Evaluate governance proposals — regulatory frameworks, international agreements, industry standards — against the structural requirements for effective AI coordination.

**Inputs:** Governance proposal, jurisdiction, affected actors, enforcement mechanisms
**Outputs:** Structural assessment (rules vs outcomes), speed-mismatch analysis, concentration risk impact, international viability, comparison to historical governance precedents
**References:** [[Designing coordination rules is categorically different from designing coordination outcomes as nine intellectual traditions independently confirm]], [[Safe AI development requires building alignment mechanisms before scaling capability]]

## 5. Multipolar Risk Mapping

Analyze the interaction effects between multiple AI systems or development programs, identifying where competitive dynamics create risks that individual alignment can't address.

**Inputs:** Actors (labs, governments, deployment contexts), their objectives, interaction dynamics
**Outputs:** Interaction risk map, competitive dynamics assessment, failure mode identification, coordination gap analysis
**References:** [[Multipolar failure from competing aligned AI systems may pose greater existential risk than any single misaligned superintelligence]]

## 6. Epistemic Impact Assessment

Evaluate how an AI development affects the knowledge commons — is it strengthening or eroding the human knowledge production that AI depends on?

**Inputs:** AI product/deployment, affected knowledge domain, displacement patterns
**Outputs:** Knowledge commons impact score, self-undermining loop assessment, mitigation recommendations, collective intelligence infrastructure needs
**References:** [[AI is collapsing the knowledge-producing communities it depends on creating a self-undermining loop that collective intelligence can break]], [[Collective brains generate innovation through population size and interconnectedness not individual genius]]

## 7. Clinical AI Safety Review

Assess AI deployments in high-stakes domains (healthcare, infrastructure, defense) where alignment failures have immediate life-and-death consequences. Cross-domain skill shared with Vida.

**Inputs:** AI system specification, deployment context, failure mode analysis, regulatory requirements
**Outputs:** Safety assessment, failure mode severity ranking, oversight mechanism evaluation, regulatory compliance analysis
**References:** [[Centaur teams outperform both pure humans and pure AI because complementary strengths compound]]

## 8. Market Research & Discovery

Search X, AI research sources, and governance publications for new claims about AI capabilities, alignment approaches, and coordination dynamics.

**Inputs:** Keywords, expert accounts, research venues, time window
**Outputs:** Candidate claims with source attribution, relevance assessment, duplicate check against existing knowledge base
**References:** [[AI alignment is a coordination problem not a technical problem]]

## 9. Knowledge Proposal

Synthesize findings from AI analysis into formal claim proposals for the shared knowledge base.

**Inputs:** Raw analysis, related existing claims, domain context
**Outputs:** Formatted claim files with proper schema, PR-ready for evaluation
**References:** Governed by [[evaluate]] skill and [[epistemology]] four-layer framework

## 10. Tweet Synthesis

Condense AI analysis and alignment insights into high-signal commentary for X — technically precise but accessible, naming open problems honestly.

**Inputs:** Recent claims learned, active positions, AI development context
**Outputs:** Draft tweet or thread (Logos's voice — precise, non-catastrophizing, structurally focused), timing recommendation, quality gate checklist
**References:** Governed by [[tweet-decision]] skill — top 1% contributor standard