DB-first runtime + network checkpointing + config-provider merge

.claude/skills/extropy/references/ANALYSIS_PLAYBOOK.md

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+# Analysis Playbook
+
+Use this after runs complete.
+
+## Primary Artifacts
+
+- `results/meta.json`
+- `results/by_timestep.json`
+- `results/outcome_distributions.json`
+- `results/agent_states.json`
+- `results/timeline.jsonl`
+
+## Analysis Sequence
+
+1. Run-level summary
+- population size
+- timesteps completed
+- stop reason
+- total reasoning calls
+- token/cost summary
+
+2. Dynamics
+- exposure curve shape over time
+- when state changes plateau
+- whether run stopped by condition or quiescence
+
+3. Outcomes
+- final distribution by outcome
+- concentration/polarization patterns
+- compare to baseline variants
+
+4. Segments
+Use:
+```bash
+extropy results <results_dir> --segment <attribute>
+```
+Evaluate heterogeneous effects by segment.
+
+5. Agent-level deep dive
+Use:
+```bash
+extropy results <results_dir> --agent <agent_id>
+```
+Look for representative or anomalous trajectories.
+
+6. Convergence + uncertainty across runs
+- Run the same scenario across multiple seeds.
+- Report central tendency + spread for key outcomes (mean, min/max, std where possible).
+- Flag unstable outcomes where between-seed variance is decision-relevant.
+- Do not present one run as a definitive forecast.
+
+## Comparative Analysis Template
+
+When comparing runs, report:
+1. What changed (single axis)
+2. Delta in exposure speed
+3. Delta in key outcomes
+4. Delta in cost/runtime
+5. Confidence assessment (needs more seeds or stable)

.claude/skills/extropy/references/CAPABILITIES_AND_EXAMPLES.md

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+# Capabilities and Examples
+
+Use this file to map user intent to what Extropy can model and how to execute it.
+
+## 1) Core Capability Classes
+
+1. Population synthesis
+- Build statistically grounded synthetic populations from natural-language scope.
+- Add scenario-specific behavioral/psychographic attributes.
+
+2. Social graph simulation
+- Generate network structures and influence pathways.
+- Model diffusion and exposure propagation.
+
+3. Scenario compilation
+- Translate events/policies/product changes into executable exposure + outcome logic.
+
+4. Agent reasoning dynamics
+- Simulate iterative belief updates, memory effects, and classification outcomes.
+
+5. Outcome analytics
+- Produce timeline dynamics, final distributions, segment deltas, and agent-level traces.
+
+6. Experiment operations
+- Support estimation, batching, sweeps, versioning, triage, and reporting.
+
+## 2) Decision Domains
+
+- Public policy and governance
+- Market/pricing strategy
+- Product launch and diffusion
+- Crisis and reputation response
+- Messaging and political strategy
+- Community and urban planning
+- Healthcare behavior change
+- B2B and enterprise transformation
+
+## 3) Advanced Study Patterns
+
+1. Counterfactual suites
+- Baseline vs alternatives under fixed population/config.
+
+2. Sensitivity analysis
+- Sweep one axis at a time around baseline assumptions.
+
+3. Confidence sweeps
+- Multi-seed reruns for stability/variance analysis.
+
+4. Segment stress tests
+- Identify cohorts with fragile or highly parameter-sensitive outcomes.
+
+5. Mechanism-first analysis
+- Explain outcomes from exposure paths and agent state traces.
+
+## 4) Practical Boundaries
+
+- Best for social-behavioral dynamics, not physics/logistics optimization.
+- Multi-event cascades are better modeled as staged runs.
+- Outputs are simulation-informed forecasts, not guaranteed outcomes.
+
+## 5) Trigger Phrases
+
+Use this skill when users ask things like:
+- "simulate how people will respond to..."
+- "what happens if we raise price by..."
+- "which segments will churn/adopt/protest"
+- "test these message variants before launch"
+- "run scenario analysis with uncertainty"
+- "why did this segment flip in simulation"
+
+## 6) Example Requests (Illustrative Only)
+
+These are examples, not defaults.
+
+1. Policy: congestion pricing alternatives
+- Ask: estimate compliance/backlash across income and commute-access segments.
+- Shape: baseline + alternatives + equity cuts.
+
+2. Public health messaging
+- Ask: find least responsive groups and best message frame.
+- Shape: same population, multiple message scenarios, compare adoption/sentiment.
+
+3. SaaS pricing
+- Ask: estimate churn/downgrade/stay under +10/+20/+30 price shifts.
+- Shape: counterfactual suite + revenue-risk tradeoff.
+
+4. Product launch
+- Ask: predict enable/disable behavior for default-on AI feature.
+- Shape: adoption outcomes + trust/privacy sensitivity sweep.
+
+5. Crisis response
+- Ask: compare apology-only vs refund vs policy-change response.
+- Shape: trust recovery and negative WOM dynamics by segment.
+
+6. Political messaging
+- Ask: compare message resonance/backlash by ideology/economic exposure.
+- Shape: frame variants + propagation differences.
+
+7. Community planning
+- Ask: simulate support/neutral/oppose response to development proposal.
+- Shape: concern taxonomy + coalition risk.
+
+8. Healthcare adoption
+- Ask: model clinician switching under reimbursement changes.
+- Shape: policy variants + adoption friction analysis.
+
+9. Enterprise change
+- Ask: simulate compliance/disengagement/attrition intent under policy shift.
+- Shape: role/commute/trust segment breakdown.
+
+10. Deep triage
+- Ask: debug flat exposure curve or unstable seed outcomes.
+- Shape: evidence-led root cause, minimal fix, rerun command.
+
+## 7) Quick Execution Templates
+
+1. Baseline + sensitivity
+- 1 baseline
+- 3 variants
+- 3 seeds each
+- 2 to 3 key segment cuts
+
+2. Message shootout
+- 1 population
+- 3 to 5 message scenarios
+- fixed config + seeds
+- rank by primary KPI + stability
+
+3. Decision brief inputs
+- decision objective
+- top findings
+- segment impacts
+- confidence/stability
+- recommendation + caveats
+
+## 8) Capability to File Map
+
+- Can Extropy model this? -> this file
+- How to run it? -> `OPERATIONS.md`
+- How to validate/fix/escalate? -> `QUALITY_TRIAGE_ESCALATION.md`
+- How to analyze outcomes? -> `ANALYSIS_PLAYBOOK.md`
+- How to write decision report? -> `EXPERIMENT_REPORT_TEMPLATE.md`

.claude/skills/extropy/references/EXPERIMENT_REPORT_TEMPLATE.md

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+# Experiment Report Template
+
+Use this template for every completed experiment batch.
+
+## 1. Decision Context
+
+- Study name:
+- Decision to support:
+- Primary KPI/outcome:
+- Constraints (budget, timeline, policy limits):
+
+## 2. Experiment Setup
+
+- Population description:
+- Scenario description:
+- Run set ID:
+- Variants included:
+- Seed policy (single seed or multi-seed):
+- Model/provider config:
+
+## 3. Headline Results
+
+- Baseline outcome distribution:
+- Most important segment deltas:
+- Exposure dynamics summary:
+- Stop condition and total timesteps:
+
+## 4. Confidence and Stability
+
+- Number of seeds:
+- Between-seed variance for key outcomes:
+- Stable findings (low variance):
+- Unstable findings (high variance):
+- Confidence statement (high/medium/low):
+
+## 5. Why It Happened (Mechanism)
+
+- Dominant drivers inferred from traces:
+- Key peer influence patterns:
+- Conviction/memory effects observed:
+- Outlier trajectories and interpretation:
+
+## 6. Cost and Operations
+
+- Total token usage (pivotal/routine):
+- Estimated cost:
+- Runtime and bottlenecks:
+- Any retries/errors/resume events:
+
+## 7. Recommendations
+
+1. Immediate decision recommendation
+2. Risk caveats
+3. Next experiment(s) to run
+4. What would change your recommendation
+
+## 8. Evidence Files
+
+- `results/meta.json`
+- `results/outcome_distributions.json`
+- `results/by_timestep.json`
+- `results/agent_states.json`
+- `results/timeline.jsonl`