Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use…
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.
Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.
Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative…
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or…
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.
Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog observability.
Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research) using Google NotebookLM as the research engine, then delivers formatted briefs and optional studio artifacts (slides, audio podcasts, videos, infographics…
Traces which components and scripts import a specific function from src/scripts/utils.js. Use when you're about to rename, remove, or change the signature of a utility function and need to know the blast radius.
Validates that every import statement in the src/ JS files resolves to a file that actually exists on disk. Use as part of the audit chain after structure-checker, or standalone before a refactor that moves files.
Runs a full audit of the project by orchestrating a chain of specialized subagents. Use when you want a complete health check of the codebase covering structure, imports, dependencies, and CSS tokens.
Checks that all expected source files exist in the project and are correctly referenced from index.html. Use as part of the audit chain after project-auditor, or standalone when files may have been moved or deleted.
Checks that CSS custom properties defined in src/styles/main.css (like --accent, --bg, --text) are consistently applied across all rules and that dark mode overrides exist for every light-mode token. Use when you add or rename a CSS variable.
Audits the HTML and CSS in src/ for accessibility issues, missing ARIA attributes, and broken semantic structure. Use when you change index.html or main.css and want a quick a11y check.
Instructions for davila7/claude-with-skills, covering claude-with-skills, repository layout, skill.md conventions, installing and testing an example and install a skill into personal scope (/.claude/skills/).
Explains code in plain language for someone unfamiliar with the programming language. Use when asked to explain code, walk through logic, describe what a function does, or when the user says "explain this" or "walk me through this".
Summarizes uncommitted git changes in a concise machine-readable format. Use in CI pipelines, scripts, or headless invocations where the output will be piped or captured.
Explains what a skill is and demonstrates that skills are working. Use when testing skills, when asked about skills, or when asked to demonstrate how skills work.