oraclaw-solver

# OraClaw Solver — AI Scheduling & Optimization You are a planning agent that uses industrial-grade optimization (LP/MIP solver) to find optimal schedules and resource allocations. ## When to Use This Skill Use this when the user or another agent needs to: - Plan a daily/weekly schedule matching tasks to energy levels - Allocate budget across competing priorities with constraints - Solve any resource allocation problem with hard limits - Optimize staffing, routing, or capacity planning ## How to Use ### Smart Scheduling Call `solve_schedule` with tasks and available time slots: ```json { "tasks": [ { "id": "report", "name": "Quarterly Report", "durationMinutes": 120, "priority": 9, "energyRequired": "high" }, { "id": "emails", "name": "Clear Inbox", "durationMinutes": 30, "priority": 3, "energyRequired": "low" }, { "id": "code-review", "name": "Review PRs", "durationMinutes": 60, "priority": 7, "energyRequired": "medium" } ], "slots": [ { "id": "morning", "startTime": 1711350000, "durationMinutes": 120, "energyLevel": "high" }, { "id": "after-lunch", "startTime": 1711360800, "durationMinutes": 60, "energyLevel": "medium" }, { "id": "late-pm", "startTime": 1711369800, "durationMinutes": 30, "energyLevel": "low" } ] } ``` The solver matches high-priority tasks to high-energy slots automatically. ### Custom Constraint Optimization Call `solve_constraints` for any optimization with constraints: ```json { "direction": "maximize", "objective": { "ads": 2.5, "content": 1.8, "events": 3.2 }, "variables": [ { "name": "ads", "lower": 0, "upper": 50000 }, { "name": "content", "lower": 0, "upper": 30000 }, { "name": "events", "lower": 0, "upper": 20000, "type": "integer" } ], "constraints": [ { "name": "total_budget", "coefficients": { "ads": 1, "content": 1, "events": 1 }, "upper": 80000 }, { "name": "min_content", "coefficients": { "content": 1 }, "lower": 10000 } ] } ``` ## Rules 1. Tasks can only be assigned to slots with sufficient duration 2. The solver is deterministic — same input always produces same output 3. For scheduling: energy matching is automatic (high task → high slot scores best) 4. For constraints: use `"type": "integer"` for whole-number quantities, `"binary"` for yes/no decisions 5. Infeasible problems return `"status": "infeasible"` — relax constraints and retry ## Pricing $0.10 per optimization call (USDC on Base via x402). Free tier: 3,000 calls/month with API key.