Search Skills

Processes multimedia files including video, audio, and images using FFmpeg and ImageMagick for encoding, conversion, filtering, and batch operations.

Manages Model Context Protocol (MCP) servers to discover, analyze, and execute AI model tools, prompts, and resources programmatically.

Automates browser tasks including screenshots, performance analysis, network monitoring, and form automation using Puppeteer for efficient testing and debugging.

Offers creative problem-solving techniques including collision-zone thinking, inversion, pattern recognition, and simplification to overcome analytical obstacles.

Enables programmatic creation of AI-generated notebooks, source management, and artifact generation via Google NotebookLM API.

Automates validation of hover functionality in the Dingo transpiler, ensuring accurate position mappings and debugging LSP issues after sourcemap changes.

Selects optimal external AI models for code analysis, bug investigation, and architectural decisions using empirically validated rankings and consultation strategies.

Guides HASH users through Git workflow conventions for branch naming, pull request creation, and review processes in collaborative development.

Provides Rust error handling utilities with error-stack crate, covering custom errors, context propagation, and Error trait implementation.

Manages Rust dependency configurations in Cargo.toml for HASH workspaces, including version pinning and feature configuration.

Generates Rust crate documentation to explore APIs, structure, and usage for efficient code understanding and integration.

Provides Rust documentation guidelines for the HASH codebase, covering doc comments, function/type documentation, error sections, and rustdoc conventions.

Provides testing strategies for HashQL code including unit, UI, and snapshot tests with annotation support and test management.

Guides writing HashQL J-Expr queries with syntax for #literal, #struct, #list, and function calls in .jsonc files.

Provides structured diagnostic patterns for HashQL code using the hashql-diagnostics crate.

Comprehensive guide for creating and updating AI agent skills, covering YAML structure, trigger configuration, and the 500-line rule.

Tests NodeBridge handlers via dedicated script, enabling handler listing and parameter passing for validation.

Automates adding NodeBridge handlers by updating source files and type definitions, with optional test execution.

Automates browser interactions for testing web functionality, including form filling, responsive design checks, and login flows.

Guide for implementing end-to-end observability in MLOps, covering reproducibility, lineage, monitoring, alerting, and model explainability.

Guides structuring MLOps projects for team collaboration, model sharing, and community engagement in machine learning workflows.

Guides transformation of ML prototypes into robust, distributable Python packages via src layout, hybrid paradigm, and strict configuration management.

Guides implementation of validation layers including static analysis, testing, logging, and security scanning in MLOps workflows.

Guides setup of a new MLOps project with standard tools (uv, git, VS Code) and best practices for machine learning deployment.