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Found 1,288 Skills
Track, optimize, and control token consumption across multi-agent systems. Covers budget allocation, real-time monitoring, cost attribution, per-agent limits, and proactive cost optimization for production LLM deployments.
Browser automation MCP server using Playwright's accessibility tree for LLM-friendly web interaction
Attach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
Create custom LLM evaluation benchmarks using the BYOB decorator framework. Use when the user wants to (1) create a new benchmark from a dataset, (2) pick or write a scorer, (3) compile and run a BYOB benchmark, (4) containerize a benchmark, or (5) use LLM-as-Judge evaluation. Triggers on mentions of BYOB, custom benchmark, bring your own benchmark, scorer, or benchmark compilation.
Deploy and run automated Attack-with-Defense (AWD) competitions where LLM-powered agents compete in real-time cybersecurity challenges
Update LLM prices in the repo: Use this skill to snapshot live LLM pricing into a checked-in file so billing or cost math can run offline with deterministic rates. Use for any language or stack (TypeScript, Python, Go, JSON registries, etc.) — not only typescript. Use when the user wants pinned prices, wants to remove a runtime dependency on the Narev API, wants to refresh a committed pricing file, or mentions "snapshot pricing", "freeze prices", "pin model rates", "regenerate pricing file", "update pricing in the repo", or "sync token pricing from Narev".
Run an autonomous Humanize-governed vLLM SOTA performance loop for one LLM model: first perform the fixed fair vLLM/SGLang/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches vLLM code, optionally uses ncu-report-skill for kernel evidence, and revalidates until vLLM matches or beats the best observed framework under the same workload and SLA.
Router skill for LLMQuant equities workflows. Use when the user needs stock analysis, equity comparison, research memos, merger-arb memos, or sell/take-profit work.
Router skill for LLMQuant portfolio-lab workflows. Use when the user needs portfolio exposure maps, what-if simulations, scenario states, or virtual portfolio comparisons.
Router skill for LLMQuant rates and FX workflows. Use when the user needs yield curve, duration, central-bank divergence, FX carry, real-rate, dollar, or cross-currency analysis.
Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.