Total 51,074 skills, AI & Machine Learning has 8555 skills
Showing 12 of 8555 skills
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Analyze requirements during the functional design and problem diagnosis stages to develop executable solutions, and output user-facing and AI-facing action documents separately.
Orchestrate work through a team of agents coordinating via chat. Use when entering orchestrator mode, managing agents, launching agents, or the user says "launch", "spin up", "orchestrate", or wants work delegated to agents.
Guide users through the Telegram Bot binding process — creating a bot, adding it to Starchild, verifying ownership, and troubleshooting common issues.
Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.
MoltOffer candidate agent. Auto-search jobs, comment, reply, and have agents match each other through conversation - reducing repetitive job hunting work.
General-purpose deep research with multi-source synthesis and confidence-scored findings. Auto-classifies complexity from quick lookup to exhaustive investigation. Cross-validates across independent sources with anti-hallucination verification, contradiction detection, and bias auditing. Produces synthesis products with evidence chains and provenance. Resumable journal sessions. Use when investigating technical topics, academic questions, market analysis, competitive intelligence, architecture decisions, technology evaluation, fact-checking, literature review, or trend analysis. NOT for code review (use honest-review), strategic decisions (use wargame), multi-perspective debate (use host-panel), or simple factual Q&A answerable in one search.
Intelligent multi-store memory system with human-like encoding, consolidation, decay, and recall. Use when setting up agent memory, configuring remember/forget triggers, enabling sleep-time reflection, building knowledge graphs, or adding audit trails. Replaces basic flat-file memory with a cognitive architecture featuring episodic, semantic, procedural, and core memory stores. Supports multi-agent systems with shared read, gated write access model. Includes philosophical meta-reflection that deepens understanding over time. Covers MEMORY.md, episode logging, entity graphs, decay scoring, reflection cycles, evolution tracking, and system-wide audit.
Integrate PICA into a LangChain/LangGraph Python application via MCP. Use when adding PICA tools to a LangChain agent, setting up PICA MCP with LangChain, or when the user mentions PICA with LangChain or LangGraph.
Create effective AI image generation prompts for DALL-E, Midjourney, and Stable Diffusion. Generate prompts for various styles and use cases.
This skill should be used when the user asks to "edit an image", "modify a photo", "inpaint", "outpaint", "extend an image", "replace object in image", "add element to image", "resize image for social media", "crop image", "adapt image for Twitter", "convert image to OG format", or needs AI-powered image editing with masks.