Total 50,706 skills, AI & Machine Learning has 8496 skills
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Authors and consumes feature-level domain knowledge files in ai-context/features/. Reference guide for bounded-context business rules, invariants, integration points, and known gotchas.
Use this skill when you learn one or more design pattern(s) in the Langroid (multi) agent framework, and want to make a note for future reference for yourself. Use this either autonomously, or when asked by the user to record a new pattern.
Protects LLM agent systems in real-time with a 5-tier filter (hash cache, rule engine, ML classifier, LLM judge, human approval) and an async learning engine. Synthesizes new rules from every detected attack, adding less than 50ms latency. Trigger on 'add security layer', 'prevent prompt injection', 'adaptive guard', 'runtime protection', or 'agent security'.
Use this skill whenever the user wants to generate sound effects, ambient audio, or short audio clips from a text description. Triggers include: any mention of 'sound effect', 'sfx', 'generate sound', 'make a sound', 'audio effect', 'ambient sound', 'foley', 'sound clip', 'noise', or requests to produce a specific sound (e.g. 'make a gunshot sound', 'generate thunder', 'create the sound of rain'). Also use when the user describes an action or scenario and wants the corresponding audio (e.g. 'someone getting spanked', 'a door slamming', 'cartoon boing'). Do NOT use for speech synthesis, music generation with melody/lyrics, or voice cloning.
Use when the user needs self-hosted or local Chroma for semantic search, including `ChromaClient`, `HttpClient`, or Python `EphemeralClient`, local persistence, Docker or `chroma run`, or OSS Chroma without Chroma Cloud features.
Integrate Modellix's unified API for AI image and video generation into applications. Use this skill whenever the user wants to generate images from text, create videos from text or images, edit images, do virtual try-on, or call any Modellix model API. Also trigger when the user mentions Modellix, model-as-a-service for media generation, or needs to work with providers like Qwen, Wan, Seedream, Seedance, Kling, Hailuo, or MiniMax through a unified API.
Run existing ShinkaEvolve tasks with the `shinka_run` CLI from a task directory (`evaluate.py` + `initial.<ext>`). Use when an agent needs to launch async evolution runs quickly with required `--results_dir`, generation count, and strict namespaced keyword overrides.
Replay-first debug flow for SGLang serving problems. Use when a live or recent server shows health-check failures, latency or throughput regressions, queue growth, timeouts, distributed stalls, crash dumps, wrong outputs after deploys, or PD/EP/HiCache issues, and the job is to turn the problem into a replay plus the right next debug tool.
PR-backed and current-main optimization manual for the `MiniMaxAI/MiniMax-M2` series, including M2, M2.1, M2.5, M2.7, and M2.7-highspeed. Use when Codex needs to recover, extend, or audit MiniMax-specific optimizations, TP QK norm/all-reduce behavior, parser contracts, distributed runtime behavior, quantized loading, or backend-specific validation.
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
Compare Replicate models by cost, speed, quality, and capabilities.
Use when accepted findings require bounded repair changes and a structured repair summary.