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Found 1,310 Skills
Scans lyrics for phrases that may match existing songs using web search and LLM knowledge. Use before release to check for unintentional borrowing.
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
Make text more genuine, natural, and feel not written by an AI or LLM by removing AI tropes and cliches. Use when asked to deslopify, naturalize, or remove AI tropes from text.
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
Design real technical solution architectures for scalable, secure, cost-aware systems by selecting patterns, components, integrations, data flows, and tradeoffs; use when asked for senior solution architecture, system architecture, SaaS architecture, LLM architecture, or architecture decisions after a spec.
Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature.
Reference Documentation for Jiekou AI Model Services, covering LLM API (OpenAI-compatible), Image/Video/Audio APIs, integration solutions, authentication/billing/pricing/rate limiting, and troubleshooting. Suitable for questions like "How to integrate Jiekou AI into tools such as OpenAI SDK / LangChain?" and issues like Jiekou AI request failures.
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.