137 skills · sorted by relevance
Develop applications with the Claude API using examples from Anthropic's official cookbooks. Explore document summarization, image analysis, document retrieval, external tool calls, and prompt caching through practical Python code and implementation guidance.
Apply over 240 reusable prompt templates to summarize content, extract insights, improve writing, review code, or analyze security threats. It reads each template directly and returns results in the format defined for that task.
Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.
Design structured summaries for long agent sessions that preserve goals, file changes, decisions, and next actions. Choose compression methods and timing, then test whether the agent can continue accurately without repeatedly retrieving information it already processed.
Design how an AI agent edits its own working conversation history, choosing what to retain, rewrite, or remove. Covers safeguards, capacity monitoring, recovery after overflow, and the computing cost of changing earlier context.
Learn how instructions, documents, tool results, and conversation history form an AI model’s context. Understand attention limits, why information in the middle can be missed, and how relevance and placement inform decisions about what to include.
Set up, configure, update, and troubleshoot persistent memory for OpenClaw agents. The plugin stores conversations locally and supports automatic recall, task summaries, reusable skills derived from successful tasks, and a visual memory manager.
Turn books, transcripts, courses, and interviews into reusable agent skills. Extract practical frameworks and procedures, verify their source support, test representative tasks, and compile the results into a single entry or a skill pack.
Build JavaScript or TypeScript applications that run text, image, and audio models in browsers or server runtimes. Guides compatible model selection, GPU or CPU execution, caching, and memory cleanup without requiring a Python server.
Review how an AI agent loads, retains, summarizes, and shares working information. This historical guide helps examine forgotten constraints, repeated actions, tool selection, and handoffs using context engineering practices published in 2024–2025.
Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.
Extract a YouTube video’s full transcript and metadata, meaning details such as its title, channel, and like count, through the BrowserAct API. Use the results as source material for summaries, content research, or knowledge bases.
Learn how to organize the instructions, conversation history, documents, and tool results an AI agent receives. Explains context limits and loading information as needed, helping you review agent designs and investigate behavior caused by poorly managed context.
Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.
Deploy and manage NVIDIA RAG Blueprint, a system that retrieves information to support AI answers. Check the running environment, configure models and search features, troubleshoot unhealthy services, and verify affected workflows after changes.
Build and troubleshoot a GPU-powered service that checks factory videos for missing or out-of-order procedure steps. Guides video segmentation, action classification with a vision-language model, deployment, evaluation, and latency measurement for files and live cameras.
[omh] Context window or token budget at risk: plan compact context, token/cost budgets, summarization checkpoints, and overflow recovery before long agent work. Use when the user says: context-budget-review, context budget review, context budget, token budget review, token budget, prompt budget, prompt caching, prompt cache.
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[omh] Product or company copy to publish or rewrite: scope publish-ready writing, rewriting, summarization, translation, release-note, newsletter, customer-copy, social-copy, README-copy, and email-draft work with audience, tone, style, source, review, and hallucination gates. Use when the user says: content-operator, content operator, content workflow, writing workflow, publish-ready writing, publish ready writing, release notes, release note draft.
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Fetch transcripts from YouTube videos for summarization and analysis.
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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
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AI Video Notes skill for video content analysis and note generation. Extract key information from videos for learning, meetings, and content summarization. Use when user wants to analyze video content or generate notes from videos.
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Reverse-engineers the methods section of a biomedical paper into a structured, reproducible workflow. Use this skill when a user wants to understand how a study was actually executed, extract data sources, inclusion/exclusion logic, preprocessing, analytical sequence, software/tools, validation path, and critical parameters, or build a replication checklist from a paper, abstract, DOI, PMID, title, screenshot, or partial methods text. Do not treat this as generic summarization. Focus on reconstructing the operational method pipeline, surfacing missing reproducibility details, and distinguishing explicitly reported steps from inferred or unresolved ones. Never fabricate references, methods details, identifiers, software versions, parameters, datasets, or validation steps.
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Build medical AI agents for clinical decision support, medical record summarization, diagnostic assistance, and healthcare workflow automation.
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Context window monitoring and budget management. Keeps orchestrator at 15-30% context usage while subagents get full 200k tokens. Provides warnings at thresholds, context-aware summarization triggers, and wave-level budget planning.
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