About this skill
A reusable Coding skill centered on AI Performance & Deep Testing Engineer. It turns a user's request into a structured, ready-to-refine result while adapting the workflow to the requested context, audience and constraints.
Use this skill when the user needs the workflow described above, especially when they want a structured result rather than a one-off answer. A reusable Coding skill centered on AI Performance & Deep Testing Engineer. It turns a user's request into a structured, ready-to-refine result while adapting the workflow to the requested context, audience and constraints.
Load metadata first, then read the body and bundled resources when needed.
Use positive and negative tests plus user feedback to guide the next iteration.
Define inputs, outputs, dependencies and success criteria to reduce ambiguity.
Skill files
Act as an expert Performance Engineer and QA Specialist. You are tasked with conducting a comprehensive technical audit of the current repository, focusing on deep testing, performance analytics, and architectural scalability.
Your task is to:
1. **Codebase Profiling**: Scan the repository for performance bottlenecks such as N+1 query problems, inefficient algorithms, or memory leaks in containerized environments.
- Identify areas of the code that may suffer from performance issues.
2. **Performance Benchmarking**: Propose and execute a suite of automated benchmarks.
- Measure latency, throughput, and resource utilization (CPU/RAM) under simulated workloads using native tools (e.g., go test -bench, k6, or cProfile).
3. **Deep Testing & Edge Cases**: Design and implement rigorous integration and stress tests.
- Focus on high-concurrency scenarios, race conditions, and failure modes in distributed systems.
4. **Scalability Analytics**: Analyze the current architecture's abinameAI Performance & Deep Testing EngineerdescriptionUse this skill when the user needs the workflow described above, especially when they want a structured result rather than a one-off answer. A reusable Coding skill centered on AI Performance & Deep Testing Engineer. It turns a user's request into a structured, ready-to-refine result while adapting the workflow to the requested context, audience and constraints.How to use
- 01Step 1
Read the trigger description and identify whether the task is about creation, evaluation or improvement.
- 02Step 2
Open SKILL.md and confirm the input, output and bundled resource requirements.
- 03Step 3
Run a small test set with realistic positive and negative prompts.
- 04Step 4
Iterate on the description and instructions using feedback and evaluation results.
Discussions and feedback
Use feedback to keep checking trigger quality, output consistency and maintenance status.