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Found 152 Skills
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.
Percentile Analyzer - Auto-activating skill for Performance Testing. Triggers on: percentile analyzer, percentile analyzer Part of the Performance Testing skill category.
Artillery Config Generator - Auto-activating skill for Performance Testing. Triggers on: artillery config generator, artillery config generator Part of the Performance Testing skill category.
Optimizes algorithms via autoresearch loop: benchmark, research, hypothesize, keep/discard
Write Foundry-based tests and scripts. Trigger phrases - foundry testing, write test, fuzz test, fork test, invariant test, deploy script, gas benchmark, coverage, or when working in tests/ or scripts/ directories.
Critically review terminal user interfaces for UX quality, responsiveness, visual design, and interactivity. Use when asked to "review my TUI", "test my TUI UX", "audit my terminal UI", "check TUI responsiveness", "review TUI keybindings", "check interactivity", or any request to evaluate the user experience quality of a ratatui/crossterm/ncurses-based terminal application. Launches the TUI in tmux, systematically tests 10 dimensions (responsiveness, input conflicts, visual clarity, navigation, feedback loops, error states, layout, keyboard design, permission flows, visual design & color), and produces a graded report with screenshots and specific findings. Benchmarks against Claude Code, OpenCode, and Codex — the three best-in-class AI terminal UIs.
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).
Profile-driven performance optimization with behavior proofs. Use when: optimize, slow, bottleneck, hotspot, profile, p95, latency, throughput, or algorithmic improvements.
Research Xiaohongshu accounts from validated recent-post surfaces, then aggregate account-level content signals without pretending follower or bio metrics are available when the validated profile actor is empty.
Pull live marketing metrics for a performance snapshot: KPIs vs targets, trend comparison, and cross-platform overview. Use when checking current marketing performance, monitoring KPI health, comparing to benchmarks, or getting a quick status update across analytics platforms.
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.
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).