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Found 1,622 Skills
Use this skill when building production LLM applications, implementing guardrails, evaluating model outputs, or deciding between prompting and fine-tuning. Triggers on LLM app architecture, AI guardrails, output evaluation, model selection, embedding pipelines, vector databases, fine-tuning, function calling, tool use, and any task requiring production AI application design.
A session continuity loop where the frog is disposable but the pad is not.
Add PostHog LLM analytics to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.
Recipes and configs for serving LLMs locally on RTX 3090 GPUs using vLLM, llama.cpp, and SGLang with OpenAI-compatible API
Script-First llms.txt generator. Uses a deterministic script to crawl the project structure, identify brand guides, and catalog content files. Provides a repo manifest for the agent to draft context-aware /llms.txt and /llms-full.txt files.
End-to-end pipeline from unlabeled ml_app traces to a bootstrapped evaluator suite. Runs trace classification → root cause analysis → eval bootstrap in sequence with user checkpoints. Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", or wants a guided walkthrough from production data to evaluator code.
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.
Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). Use for building with LLMs, GPT/Claude apps, prompt engineering, RAG, and tool-using agents.
LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner). Use when testing AI features, validating LLM outputs, or building evaluation pipelines.
Security patterns for autonomous trading agents with wallet or transaction authority. Covers prompt injection, spend limits, pre-send simulation, circuit breakers, MEV protection, and key handling.