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Found 106 Skills
HCCL (Huawei Collective Communication Library) performance testing for Ascend NPU clusters. Use for testing distributed communication bandwidth, verifying HCCL functionality, and benchmarking collective operations like AllReduce, AllGather. Covers MPI installation, multi-node pre-flight checks (SSH/CANN version/NPU health), and production testing workflows.
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Expert in observing, benchmarking, and optimizing AI agents. Specializes in token usage tracking, latency analysis, and quality evaluation metrics. Use when optimizing agent costs, measuring performance, or implementing evals. Triggers include "agent performance", "token usage", "latency optimization", "eval", "agent metrics", "cost optimization", "agent benchmarking".
Generate comprehensive philosophy and standards documents for any domain (UX design, landing pages, email outbound, API design, etc.). Load when user says "create philosophy doc", "generate standards for [domain]", "build best practices guide", or "create benchmarking document". Conducts deep research, synthesizes findings, and produces structured philosophy documents with principles, frameworks, anti-patterns, checklists, case studies, and metrics.
Performance review and testing: evaluate Core Web Vitals, page load times, bundle sizes, runtime performance, resource optimization, and rendering efficiency with browser-based measurement and benchmarking.
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
Go testing patterns for production-grade code: subtests, test helpers, fixtures, golden files, httptest, testcontainers, property-based testing, and fuzz testing. Covers mocking strategies, test isolation, coverage analysis, and test design philosophy. Use when writing tests, improving coverage, reviewing test quality, setting up test infrastructure, or choosing a testing approach. Trigger examples: "add tests", "improve coverage", "write tests for this", "test helpers", "mock this dependency", "integration test", "fuzz test". Do NOT use for performance benchmarking methodology (use go-performance-review), security testing (use go-security-audit), or table-driven test patterns specifically (use go-test-table-driven).
Spatial indexing and world streaming for Three.js building games with thousands of pieces. Use when optimizing building games, implementing spatial queries, chunk loading, or profiling performance. Includes spatial hash grids, octrees, chunk managers, and benchmarking tools.
Use this skill when load testing services, benchmarking API performance, planning capacity, or identifying bottlenecks under stress. Triggers on k6, Artillery, JMeter, load testing, stress testing, soak testing, spike testing, performance benchmarks, throughput testing, and any task requiring load or performance testing.
Run isolated eval and grading calls using CC 2.1.81 --bare mode. Constructs claude -p --bare invocations for skill evaluation, trigger testing, and LLM grading without plugin/hook interference. Use when running eval pipelines, grading skill outputs, benchmarking prompt quality, or testing trigger accuracy in isolation.
Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.
Expert-level performance optimization, profiling, benchmarking, and tuning