Agent Skills Hub

opper-api

Direct access to the Opper REST API for LLM orchestration, model management, task execution, and seamless migration from OpenAI, Anthropic, or OpenRouter.

voice-extractor

Extract and document authentic writing voice from samples. Create comprehensive voice guides for AI training, ghostwriting, and brand consistency.

icon-set-generator

Generate professional, cohesive, project-specific SVG icon sets with consistent style, stroke weight, and visual density. Ideal for unique web and app UI branding.

requirements-elicitation

Deep requirement analysis and documentation skill. Uncover root needs, map stakeholders, resolve conflicts, and define testable specifications with acceptance criteria for software projects.

design-excellence

A design system and anti-pattern guide to make AI-generated UI look human-crafted. Ensures professional aesthetics by managing color, typography, spacing, and animations for the Toh Framework.

qras

A local RAG semantic memory system using Qdrant and Ollama. Ideal for recalling workspace files, notes, project decisions, and user preferences with high-relevance vector search.

vibesafe

Manage Vibesafe units to scan, generate, test, and verify AI-written code with cryptographically-secure hash-locked checkpoints.

solana-vulnerability-scanner

Scans Solana programs (native/Anchor) for 6 critical vulnerabilities, including arbitrary CPI, improper PDA validation, and missing ownership checks, providing detailed fix recommendations.

synapse

Autonomous pattern detection and skill recommendation engine that monitors project memory, logs, and task lists to evolve your AI agent's capabilities automatically.

wycheproof

Validates cryptographic implementations using the Google Wycheproof test vector suite to detect security edge cases and known vulnerabilities.

markdown-token-optimizer

Analyzes markdown files to identify token-wasting patterns, providing actionable suggestions to optimize documentation for LLM consumption and token efficiency.

evaluation

Build systematic evaluation frameworks for AI agents using multi-dimensional rubrics, LLM-as-a-judge, and regression testing to measure performance, quality, and context engineering effectiveness.