Agent Skills Hub

agent-architecture

Implement production-grade AI agents with LangGraph, tool-calling guardrails, SSE streaming, and episodic memory. Includes anti-patterns, fix pairs, and stateful architecture patterns.

tapestry

Unified content extraction and action planning engine. Automatically processes URLs (YouTube, articles, PDFs) into actionable plans.

xlsx

Professional-grade spreadsheet automation for Claude: create, edit, analyze, and visualize Excel and CSV files with rigorous formula integrity and financial formatting standards.

linkedin-post-generator

AI-powered LinkedIn post generator for professionals. Create engaging thought leadership, career updates, and industry-specific content using the Sloan agent.

avoid-feature-creep

A framework for software teams and AI agents to prevent feature creep, enforce scope discipline, and ship focused MVPs by applying strict validation, backlog hygiene, and clear decision-making processes.

pdf-text-replace

Update text within fillable PDF forms programmatically. Efficiently modify names, dates, addresses, and reference numbers in form fields while preserving document structure.

clinical-reports

Automate clinical report generation including CARE-compliant case reports, diagnostic summaries, clinical trial documentation (CSR/SAE), and patient notes with regulatory compliance.

claude-code

Standardized skill for Claude Code agents to dynamically query OpenRouter model recommendations and metadata via the Claudish CLI.

webmcp

Create and test AI-ready MCP tools for any web application. Inject code, automate browser interactions, and turn websites into intelligent agents.

morph-warpgrep

Morph WarpGrep and Fast Apply tools for high-speed agentic code search, deep logic analysis, and efficient AI-driven code editing.

scholar-evaluation

Systematically evaluate scholarly work using the ScholarEval framework, providing structured, quantitative, and qualitative assessment across research quality dimensions with actionable feedback.

karpathy-guidelines

Behavioral guidelines for LLMs to reduce coding mistakes, follow best practices, and improve output quality by enforcing simplicity, surgical changes, and goal-driven verification.