Total 56,460 skills, AI & Machine Learning has 9392 skills
Showing 12 of 9392 skills
Investigate LLM analytics evaluations of both types — `hog` (deterministic code-based) and `llm_judge` (LLM-prompt-based). Find existing evaluations, inspect their configuration, run them against specific generations, query individual pass/fail results, and generate AI-powered summaries of patterns across many runs. Use when the user asks to debug why an evaluation is failing, surface common failure modes, compare results across filters, dry-run a Hog evaluator, prototype a new LLM-judge prompt, or manage the evaluation lifecycle (create, update, enable/disable, delete).
Agent-driven physical Texas Hold'em robot skill. Uses per-state image/action folders, visual guidelines, durable hole-card and action-sequence caches, and deterministic helpers for capture, state updates, command translation, and robot execution. Use for running or maintaining this DexHoldem workflow with Codex, Claude Code, or another coding agent.
Capture the current task into a structured temporary session bundle under `.agents/sessions/` so a learning agent can later distill durable repo knowledge. Use for completed, blocked, or abandoned tasks with meaningful changes, debugging, validation, or reusable lessons.
Automates the Karpathy LLM Wiki workflow: turns web, GitHub, and YouTube URLs into well-structured, citable, wikilinked pages with automatic linting and sourcing — invoke with /pin-llm-wiki
Extract entities and relations from source files to build a knowledge graph
INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.
Decompose requirements into structured task lists and build a task management system for long-running Agents (based on the Anthropic Effective harnesses methodology). Automatically trigger when users need to manage multi-session development tasks, track feature completion progress, or request "task decomposition", "task management", or "project planning".
[Hyper] Produce a multi-source, source-backed markdown research report for fact-finding, comparisons, market/trend analysis, or evidence-backed recommendations across live web, official docs, GitHub, and local repo sources. Use when synthesis and citations are needed, not for one-source lookups.
Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
Analyze token usage patterns and recommend cost optimizations with estimated savings
Load this skill when the user mentions `ulw` or `ultrawork`.
Inworld AI integration. Manage data, records, and automate workflows. Use when the user wants to interact with Inworld AI data.