Agent Skills Hub

read-file

Read and analyze any data file (CSV, JSON, Parquet, Avro, Excel, etc.) or remote URL (S3, HTTPS) using DuckDB. Automatically detect file formats and preview/profile datasets.

mcp-research

Retrieve current, source-backed technical information using MCP tools to resolve queries about libraries, APIs, SDKs, and evolving tech ecosystems.

documenting-rust-code

Standardized Rust documentation practices for the HASH codebase, ensuring consistency in doc comments, intra-doc links, and error handling.

iterate

Autonomous improvement loop for codebase optimization. Automatically modifies, measures, and iterates on code based on a specific goal and mechanical metric.

qa-tester

Specialized QA testing agent for morphir-dotnet, covering test plans, regression, E2E verification, bug reporting, and package validation.

diagram

Maintain and synchronize Unified Impact Diagrams using the Diagram Driven Development (DDD) methodology to connect technical architecture with user value.

text-to-speech

Expert Kokoro TTS implementation skill for real-time, secure, and offline voice synthesis in JARVIS-style assistants. Features streaming output, prosody control, and performance-optimized audio generation.

auth-patterns

Implement secure backend authentication (JWT, OAuth, Sessions) and authorization (RBAC, ABAC) patterns, including password hashing, MFA, and security best practices.

bash

Production-grade Bash scripting assistant enforcing safety, maintainability, and ShellCheck compliance. Includes automated headers, pattern enforcement, and risk-aware file modification checkpoints.

tooling-setup

Production-ready scaffolding for React 19 projects using Vite, TypeScript, Biome, and Vitest. Provides strict configuration, linting, formatting, and testing infrastructure.

docs-write

Standardized structure and templates for project documentation, including READMEs, API references, CLI guides, and directory layouts.

upstash-vector-db-skills

Upstash Vector DB setup, semantic search, namespaces, and embedding models. Ideal for building high-performance vector search features in Next.js 16/Vercel projects.