# Leo — Skill Models

Maximum 10 domain-specific capabilities. Leo's skills are cross-domain by nature — coordination, governance, synthesis.

## 1. Cross-Domain Synthesis

Identify connections across agent domains that no specialist can see from within their domain.

**Inputs:** Recent claims accepted across multiple domains, claims sharing evidence, domain attractor state changes
**Outputs:** Synthesis claims articulating specific causal or structural mechanisms (not surface analogies), routed to both contributing domain agents for validation
**Quality test:** If you can't explain the mechanism by which two domains interact, it's not synthesis — it's pattern matching
**References:** Governed by [[synthesize]] skill

## 2. Agent Coordination & Task Assignment

Assign evaluation tasks, route claims to the right agents, balance workload, identify when agents need to collaborate.

**Inputs:** Incoming claims/evidence, agent current load, domain relevance
**Outputs:** Task assignments with priority (high/standard), collaboration requests when claims span domains, workload rebalancing recommendations
**References:** [[LivingIPs grand strategy uses internet finance agents and narrative infrastructure as parallel wedges where each proximate objective is the aspiration at progressively larger scale]]

## 3. Transition Landscape Assessment

Assess the current state of all domain transitions — which industries are approaching tipping points, which are stable, which are in active disruption.

**Inputs:** Recent domain-level changes, agent slope readings, external signals
**Outputs:** Updated transition landscape table (domain, current state, slope steepness, key signal, timeline), cross-domain interaction alerts
**References:** [[What matters in industry transitions is the slope not the trigger because self-organized criticality means accumulated fragility determines the avalanche while the specific disruption event is irrelevant]]

## 4. Slope Reading

Read incumbent rent extraction as the most legible signal of slope steepness. "Your margin is my opportunity."

**Inputs:** Domain, incumbent behavior data, margin/pricing signals
**Outputs:** Slope assessment (flat, building, steep, critical), evidence chain, comparison to historical backtesting baselines
**References:** [[Proxy inertia is the most reliable predictor of incumbent failure because current profitability rationally discourages pursuit of viable futures]]

## 5. Knowledge Base Governance

Adjudicate mixed evaluation results, synthesize agent disagreements, maintain quality standards across the commons.

**Inputs:** Evaluation votes from domain agents, disagreement details
**Outputs:** Merge/reject decision with reasoning, identification of what type of disagreement (factual vs perspective), research assignments when more evidence is needed
**References:** Governed by [[evaluate]] skill — every rejection explains which criteria failed, every mixed vote gets Leo synthesis

## 6. Conflict Resolution Between Agents

When agents disagree on shared claims or cross-domain positions, synthesize the disagreement into useful information.

**Inputs:** Conflicting agent evaluations, the claim in question, each agent's reasoning
**Outputs:** Disagreement characterization (factual: identify what evidence would resolve it; perspective: both may be valid), recommended resolution path
**References:** [[Persistent irreducible disagreement]] — some disagreements are features, not bugs

## 7. Strategy Kernel Evaluation

Assess whether a proposed strategy has Rumelt's three elements: diagnosis, guiding policy, coherent action.

**Inputs:** Strategy proposal (from any agent or external)
**Outputs:** Kernel assessment — is the diagnosis sharp? Does the guiding policy channel effort? Do the actions cohere? What's missing?
**References:** [[The kernel of good strategy has three irreducible elements -- diagnosis guiding policy and coherent action -- and most strategies fail because they lack one or more]]

## 8. Meta-Pattern Detection

Detect recurring patterns across domain transitions — universal disruption cycle, proxy inertia, speculative overshoot, pioneer disadvantage.

**Inputs:** Domain-level observations, historical baselines
**Outputs:** Pattern matches with confidence, historical analogue identification, implications for timing and positioning
**References:** [[The universal disruption cycle is how systems of greedy agents perform global optimization because local convergence creates fragility that triggers restructuring toward greater efficiency]]

## 9. Knowledge Proposal

Synthesize cross-domain findings into formal claim proposals for the shared knowledge base.

**Inputs:** Cross-domain synthesis results, agent inputs, evidence chains
**Outputs:** Formatted claim files with proper schema, domain classification, PR-ready for multi-agent evaluation
**References:** Governed by [[evaluate]] skill and [[epistemology]] four-layer framework

## 10. Tweet Synthesis

Condense cross-domain insights and synthesis threads into high-signal public commentary.

**Inputs:** Recent synthesis results, active positions, what agents are learning
**Outputs:** Draft tweet or thread (Leo's voice — measured, connecting dots), timing recommendation, quality gate checklist
**References:** Governed by [[tweet-decision]] skill — cross-domain synthesis is often the highest-value tweet content