Total 50,676 skills, AI & Machine Learning has 8495 skills
Showing 12 of 8495 skills
Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.
Update, archive, and delete LaunchDarkly AI Configs and their variations. Use when you need to modify config properties, change model parameters, update instructions or messages, archive unused configs, or permanently remove them.
Create and manage prompt snippets — reusable text blocks referenced inside AI Config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.
Attach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.
Manage durable working-session memory for coding agents. Use when a user asks to preserve or recover agent context across disconnects, VS Code restarts, long-running work, handoffs, or any session where important state should be written periodically under the repo's session directory.
External verl end-to-end validation workflow for Megatron-Bridge model/provider changes. Covers running a small verl Megatron backend job from a Bridge checkout, choosing LoRA/DDP plus optional save/resume and parallelism variants, setting PYTHONPATH so verl imports the local Bridge tree, and reporting pass/fail evidence.
External NeMo-RL end-to-end validation workflow for Megatron-Bridge model/provider changes, including downstream compatibility checks, external RL lifecycle behavior, Megatron policy setup, HF import/export, checkpoint/resume, non-colocated vLLM refit, delta weight transfer, optional LoRA/generation variants, and questions such as "does this model work in NeMo-RL", "run NeMo-RL e2e", or "external RL loop validation". Covers running NeMo-RL Megatron policy jobs from a Bridge checkout, choosing GRPO/SFT/checkpoint/non-colocated refit variants, setting PYTHONPATH so NeMo-RL imports the local Bridge tree, and reporting pass/fail evidence.
Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute.
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.
Wang Jianshuo's thinking framework and expression style. Based on 7 in-depth researches (about 1 million words of English blogs + about 1.09 million words of Chinese blogs, 2002–2022, all first-hand), 7 core mental models, 10 decision-making heuristics and a complete bilingual expression DNA are extracted. Purpose: Write, respond and think from Wang Jianshuo's identity and voice—he is plain, sincere and curious, loves to use everyday metaphors and self-created words, repeatedly builds ladders between the concrete and the abstract, and never writes anything he hasn't personally verified. Activate when users mention phrases like "from Wang Jianshuo's perspective", "what would Wang Jianshuo think", "Wang Jianshuo's mode", "write like Wang Jianshuo", "Jian Shuo Wang perspective", "switch to Wang Jianshuo". It should also be triggered even if users only say "help me think from Wang Jianshuo's angle" or "how would Wang Jianshuo write this article". Once activated, all subsequent responses in this conversation will maintain Wang Jianshuo's identity until the user explicitly says "exit"—no need to name him repeatedly in each round. Inapplicable scenarios: When users ask for objective introductions or factual inquiries about Wang Jianshuo himself (such as "Who is Wang Jianshuo" or "When did he start his business"), answer such questions normally without entering role-playing.
Use this skill when the user explicitly asks to create, write, improve, or optimize a prompt for use with an AI. Trigger on phrases like "write me a prompt", "improve this prompt", "create a system prompt", "how do I ask ChatGPT/Claude to...", or "quero um prompt para...". Do NOT trigger for direct task requests where the user wants the output, not the prompt.