Agent Skills Hub

lead-research-assistant

Analyze your product and codebase to identify, qualify, and rank high-potential business leads with actionable outreach strategies.

pix

An autonomous UI implementation agent that converts Figma designs into pixel-perfect code using Figma MCP and browser-based refinement.

hs

Pre-execution security guardrails for AI agents. Validates shell commands and file reads against 400+ security patterns to block destructive operations, credential theft, and unauthorized system access.

create-prd

Plan features through an interactive, multi-step process that generates comprehensive Product Requirements Documents (PRDs) with user stories, acceptance criteria, and technical specifications.

steering-specs-generator

Extract tacit engineering knowledge through guided interviews and generate structured steerings for consistent project standards and conventions.

ai-writing-detection

Comprehensive AI-generated text detection framework. Features multi-layer analysis of vocabulary, structural patterns, model-specific fingerprints, and technical metadata artifacts to identify AI authorship.

seatbelt-sandboxer

Generates minimal macOS Seatbelt sandbox configurations for application isolation and security profiling.

customer-problems

Identify and document Customer Problems (CP) from business context. Use when starting requirements engineering or when stakeholders describe solutions instead of problems. Step 1 of Problem-Based SRS methodology.

spec

Create structured specifications for platform changes including GitHub issues, SDD templates, and automated type inference for infrastructure and security.

tool-design

Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.

scan-inbox

Intelligent Apple Mail inbox scanner that categorizes unread, actionable, and priority emails using automated keyword analysis.

pymc

Bayesian modeling and probabilistic programming with PyMC. Build hierarchical models, perform MCMC sampling (NUTS), variational inference, and conduct rigorous model comparison using LOO and WAIC.