AI Infrastructure Skill Classification System: Building a Professional Operation and Maintenance Capability Library for AI Programming Assistants

This article introduces the open-source AI infrastructure skill classification system, which aims to solve problems such as ambiguous trigger conditions and unstable output quality of traditional AI O&M assistants. The system breaks down complex AI O&M tasks into skill modules across 12 core domains, following standardized action modes and quality specifications to help AI programming assistants provide reliable O&M support in scenarios like GPU management, training debugging, and inference services.

AI infrastructure O&M covers multiple domains such as GPU capacity management, cluster scheduling, and training reliability. Traditional single assistants have four major issues: ambiguous trigger conditions (difficulty understanding users' specific needs), unstable output quality (lack of cross-domain knowledge), broad context (prone to hallucinations in reasoning), and difficulty standardizing expert workflows (hard to preserve implicit experience).

The classification system is divided into 12 core categories: 1. Capacity and Cluster Management (GPU resource planning); 2. Cluster and Scheduler O&M (scheduler health check); 3. Training Runtime and Task Reliability (training fault debugging); 4. Distributed Training and Performance Optimization (bottleneck analysis); 5. Data Pipeline and Dataset Infrastructure (ETL and data quality); 6. Model Artifacts and Registry O&M (lifecycle management); 7. Inference Services and Online Reliability (latency optimization); 8. Observability and SRE (alert handling); 9. Cost and Resource Optimization (cost attribution); 10. Security and Governance (RBAC audit); 11. Developer Experience (self-service); 12. Evaluation and Benchmarking (reproducibility).

All skills follow six action modes: Diagnoser (root cause analysis), Reviewer (configuration evaluation), Planner (resource decision-making), Optimizer (performance/cost optimization), Reporter (summary generation), and Checker (pre-launch verification). Skills must meet strict quality standards: clear trigger conditions (starting with "Use when"), usage boundaries, structured input, phased workflow, standardized output, real examples, related skill routing, common errors, and quality checklists.

1. Training task troubleshooting: Call the training task debugger to collect logs and locate root causes; 2. GPU cost optimization: Analyze resource usage via the GPU cost attributor to identify waste; 3. Inference service incident response: The service incident classifier quickly collects metrics and evaluates impacts; 4. Capacity planning decision-making: The GPU capacity planner analyzes trends and predicts demand.

Currently, 12 core skills have been released, with plans to expand to about 65 in two phases: the second phase (13 skills) includes check-and-recovery advisors, bin packing optimizers, etc.; the third phase (40 skills) will achieve full coverage. In the long term, machine-readable skill registries and example documents will be added to lower the adoption threshold.

This system transforms expert knowledge into standardized skill modules, improving the output quality and consistency of AI assistants, and providing a path for organizations to inherit O&M best practices. For teams building AI O&M capabilities, this open-source system is worth studying and referencing.