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Found 147 Skills
Database Query Profiler - Auto-activating skill for Performance Testing. Triggers on: database query profiler, database query profiler Part of the Performance Testing skill category.
Memory Profiler Setup - Auto-activating skill for Performance Testing. Triggers on: memory profiler setup, memory profiler setup Part of the Performance Testing skill category.
Profile-driven performance optimization with behavior proofs. Use when: optimize, slow, bottleneck, hotspot, profile, p95, latency, throughput, or algorithmic improvements.
Create an AI Evals Pack (eval PRD, test set, rubric, judge plan, results + iteration loop). Use for LLM evaluation, benchmarks, rubrics, error analysis/open coding, and ship/no-ship quality gates for AI features.
Use when running controlled perf experiments to validate hypotheses.
Artillery Config Generator - Auto-activating skill for Performance Testing. Triggers on: artillery config generator, artillery config generator Part of the Performance Testing skill category.
Go testing patterns and methodology: table-driven tests, t.Run subtests, t.Helper helpers, mocking interfaces, benchmarks, race detection, and synctest. Use when writing new Go tests, modifying existing tests, adding coverage, fixing failing tests, writing benchmarks, or creating mocks. Triggered by "go test", "_test.go", "table-driven", "t.Run", "benchmark", "mock", "race detection", "test coverage". Do NOT use for non-Go testing (use test-driven-development instead), debugging test failures (use systematic-debugging), or general Go development without test focus (use golang-general-engineer directly).
AscendC Operator End-to-End Development Orchestrator. Used when users need to develop new operators, implement custom operators, or complete the full process from requirements to testing. Keywords: operator development, end-to-end, full process, workflow orchestration, new operator creation.
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).
Add tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.
Expert skill for AI model quantization and optimization. Covers 4-bit/8-bit quantization, GGUF conversion, memory optimization, and quality-performance tradeoffs for deploying LLMs in resource-constrained JARVIS environments.
Percentile Analyzer - Auto-activating skill for Performance Testing. Triggers on: percentile analyzer, percentile analyzer Part of the Performance Testing skill category.