Total 56,861 skills, AI & Machine Learning has 9454 skills
Showing 12 of 9454 skills
Build interactive chat agents for exploring and discussing academic research papers from ArXiv. Covers paper retrieval, content processing, question-answering, and research synthesis. Use when building research assistants, paper summarization tools, academic knowledge bases, or scientific literature chatbots.
View Langfuse trace details. Use when checking specific trace input/output, debugging LLM calls, or analyzing costs.
Edit images using AI on fal.ai. Style transfer, object removal, background changes, and more. Use when the user requests "Edit image", "Remove object", "Change background", "Apply style", or similar image editing tasks.
Solubility-optimized protein sequence design using SolubleMPNN. Use this skill when: (1) Designing for E. coli expression, (2) Optimizing solubility of designed proteins, (3) Reducing aggregation propensity, (4) Need high-yield expression, (5) Avoiding inclusion body formation. For standard design, use proteinmpnn. For ligand-aware design, use ligandmpnn.
Conversation design skill for Salesforce Agentforce. Generates persona documents, topic architectures, instruction sets, utterance libraries, escalation matrices, and guardrail configurations. Validates existing agents against conversation design best practices with 120-point scoring.
Edit opencode.json, AGENTS.md, and config files. Use proactively for provider setup, permission changes, model config, formatter rules, or environment variables. Examples: - user: "Add Anthropic as a provider" → edit opencode.json providers, add API key baseEnv var, verify with opencode run test - user: "Restrict this agent's permissions" → add permission block to agent config, set deny/allow for tools/fileAccess - user: "Set GPT-5 as default model" → edit global or agent-level model preference, verify model name format - user: "Disable gofmt formatter" → edit formatters section, set languages.gofmt.enabled = false
Analyze Claude Code sessions via Braintrust
Planning agent that creates implementation plans and handoffs from conversation context
Use when performing ralph wiggum style long-running development loops with pacing control.
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Generates illustrations for articles
Library of 18+ ready-to-use prompt templates and executable agents