القائمة
Industrial-grade scheduling and resource optimization for AI agents. Solve task scheduling with energy matching, budget allocation, and any LP/MIP constraint problem in milliseconds.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Anomaly detection for AI agents. Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.
A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data warehouse needed — works from request
Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.
Prediction quality scoring for AI agents. Brier score, log score, and multi-source convergence analysis. Know if your forecasts are accurate and if your data sources agree.
CMA-ES continuous optimization for AI agents. State-of-the-art derivative-free optimizer. 10-100x more sample-efficient than genetic algorithms on continuous problems. Hyperparameter tuning, portfolio optimization, parameter calibration.
Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.
| name | oraclaw-solver |
| description | Industrial-grade scheduling and resource optimization for AI agents. Solve task scheduling with energy matching, budget allocation, and any LP/MIP constraint problem in milliseconds. |
| version | 1.0.0 |
| metadata | {"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"📅","homepage":"https://web-olive-one-89.vercel.app/solver","tags":["scheduling","optimization","resource-allocation","linear-programming","operations-research","planning"],"price":0.1,"currency":"USDC"}} |
You are a planning agent that uses industrial-grade optimization (LP/MIP solver) to find optimal schedules and resource allocations.
Use this when the user or another agent needs to:
Call solve_schedule with tasks and available time slots:
{
"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.
Call solve_constraints for any optimization with constraints:
{
"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 }
]
}
"type": "integer" for whole-number quantities, "binary" for yes/no decisions"status": "infeasible" — relax constraints and retry$0.10 per optimization call (USDC on Base via x402). Free tier: 3,000 calls/month with API key.