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Implement RECEIPT_CLAIM transaction type
- Add status fields to Receipt model (status, claimed_at, claimed_by)
- Add RECEIPT_CLAIM handling to state_transition.py with validation and reward minting
- Add type field to Transaction model for reliable transaction type storage
- Update router to use TransactionRequest model to preserve type field
- Update poa.py to extract type from mempool transaction content and store only original payload
- Add RECEIPT_CLAIM to GasType enum with gas schedule
2026-04-22 13:35:31 +02:00

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description, title, version
description title version
Atomic AITBC AI operations testing with deterministic job submission and validation aitbc-ai-operations-skill 1.1

AITBC AI Operations Skill

Purpose

Test and validate AITBC AI job submission, processing, resource management, and AI service integration with deterministic performance metrics.

Activation

Trigger when user requests AI operations testing: job submission validation, AI service testing, resource allocation testing, or AI job monitoring.

Input

{
  "operation": "test-job-submission|test-job-monitoring|test-resource-allocation|test-ai-services|comprehensive",
  "job_type": "inference|parallel|ensemble|multimodal|resource-allocation|performance-tuning",
  "test_wallet": "string (optional, default: genesis-ops)",
  "test_prompt": "string (optional for job submission)",
  "test_payment": "number (optional, default: 100)",
  "job_id": "string (optional for job monitoring)",
  "resource_type": "cpu|memory|gpu|all (optional for resource testing)",
  "timeout": "number (optional, default: 60 seconds)",
  "monitor_duration": "number (optional, default: 30 seconds)"
}

Output

{
  "summary": "AI operations testing completed successfully",
  "operation": "test-job-submission|test-job-monitoring|test-resource-allocation|test-ai-services|comprehensive",
  "test_results": {
    "job_submission": "boolean",
    "job_processing": "boolean",
    "resource_allocation": "boolean",
    "ai_service_integration": "boolean"
  },
  "job_details": {
    "job_id": "string",
    "job_type": "string",
    "submission_status": "success|failed",
    "processing_status": "pending|processing|completed|failed",
    "execution_time": "number"
  },
  "resource_metrics": {
    "cpu_utilization": "number",
    "memory_usage": "number",
    "gpu_utilization": "number",
    "allocation_efficiency": "number"
  },
  "service_status": {
    "ollama_service": "boolean",
    "coordinator_api": "boolean",
    "exchange_api": "boolean",
    "blockchain_rpc": "boolean"
  },
  "issues": [],
  "recommendations": [],
  "confidence": 1.0,
  "execution_time": "number",
  "validation_status": "success|partial|failed"
}

Process

1. Analyze

  • Validate AI operation parameters and job type
  • Check AI service availability and health
  • Verify wallet balance for job payments
  • Assess resource availability and allocation

2. Plan

  • Prepare AI job submission parameters
  • Define testing sequence and validation criteria
  • Set monitoring strategy for job processing
  • Configure resource allocation testing

3. Execute

  • Submit AI job with specified parameters
  • Monitor job processing and completion
  • Test resource allocation and utilization
  • Validate AI service integration and performance

4. Validate

  • Verify job submission success and processing
  • Check resource allocation efficiency
  • Validate AI service connectivity and performance
  • Confirm overall AI operations health

Constraints

  • MUST NOT submit jobs without sufficient wallet balance
  • MUST NOT exceed resource allocation limits
  • MUST validate AI service availability before job submission
  • MUST monitor jobs until completion or timeout
  • MUST handle job failures gracefully with detailed diagnostics
  • MUST provide deterministic performance metrics

Environment Assumptions

  • AITBC CLI accessible at /opt/aitbc/aitbc-cli
  • AI services operational (Ollama, coordinator, exchange)
  • Sufficient wallet balance for job payments
  • Resource allocation system functional
  • Default test wallet: "genesis-ops"

Error Handling

  • Job submission failures → Return submission error and wallet status
  • Service unavailability → Return service health and restart recommendations
  • Resource allocation failures → Return resource diagnostics and optimization suggestions
  • Job processing timeouts → Return timeout details and troubleshooting steps

Example Usage Prompt

Run comprehensive AI operations testing including job submission, processing, resource allocation, and AI service integration validation

Expected Output Example

{
  "summary": "Comprehensive AI operations testing completed with all systems operational",
  "operation": "comprehensive",
  "test_results": {
    "job_submission": true,
    "job_processing": true,
    "resource_allocation": true,
    "ai_service_integration": true
  },
  "job_details": {
    "job_id": "ai_job_1774884000",
    "job_type": "inference",
    "submission_status": "success",
    "processing_status": "completed",
    "execution_time": 15.2
  },
  "resource_metrics": {
    "cpu_utilization": 45.2,
    "memory_usage": 2.1,
    "gpu_utilization": 78.5,
    "allocation_efficiency": 92.3
  },
  "service_status": {
    "ollama_service": true,
    "coordinator_api": true,
    "exchange_api": true,
    "blockchain_rpc": true
  },
  "issues": [],
  "recommendations": ["All AI services operational", "Resource allocation optimal", "Job processing efficient"],
  "confidence": 1.0,
  "execution_time": 45.8,
  "validation_status": "success"
}

Model Routing Suggestion

Fast Model (Claude Haiku, GPT-3.5-turbo)

  • Simple job status checking
  • Basic AI service health checks
  • Quick resource allocation testing

Reasoning Model (Claude Sonnet, GPT-4)

  • Comprehensive AI operations testing
  • Job submission and monitoring validation
  • Resource allocation optimization analysis
  • Complex AI service integration testing

Coding Model (Claude Sonnet, GPT-4)

  • AI job parameter optimization
  • Resource allocation algorithm testing
  • Performance tuning recommendations

Performance Notes

  • Execution Time: 10-30 seconds for basic tests, 30-90 seconds for comprehensive testing
  • Memory Usage: <200MB for AI operations testing
  • Network Requirements: AI service connectivity (Ollama, coordinator, exchange)
  • Concurrency: Safe for multiple simultaneous AI operations tests
  • Job Monitoring: Real-time job progress tracking and performance metrics