Skill UI

A comprehensive guide to securing serverless compute platforms (AWS Lambda, Azure Functions, Google Cloud Functions). It covers critical security aspects including enforcing least-privilege IAM roles, integrating secrets management, vulnerability scanning of dependencies, input validation, and implementing runtime monitoring for robust DevSecOps practices.

A comprehensive guide and set of payloads for penetration testing web applications that process XML input. It details how to test for major vulnerabilities such as XXE, XPath injection, SSRF, and XML parsing attacks, providing step-by-step workflows and advanced payloads for identifying data exposure and security flaws.

A comprehensive guide and code suite for performing penetration testing on real-time WebSocket APIs (WS/WSS). This tool assesses vulnerabilities including Cross-Site WebSocket Hijacking (CSWSH), missing handshake authentication, message injection, input validation failures, and DoS risks. It guides users through analyzing the handshake process and testing authorization enforcement using Python and specialized tools.

Models cap table dilution, SAFE conversion, and exit waterfalls for venture rounds, generating OCF inputs and running Python scripts to calculate ownership changes and waterfall outcomes.

Soft Predict Future is a structured foresight engine that triggers whenever the user asks about future outcomes or scenarios. It enforces a nine-step analytical pipeline (validation, signal collection, scoring, driver extraction, etc.), highlights required searches and inputs, and directs the user to web-enabled reasoning for predictive analysis.

Assess any business decision through Sahil Lavingia’s Minimalist Entrepreneur lens, focusing on community value, manual-first processes, profitability, and reversible moves to keep plans lean and validating with early customer input.

A comprehensive security tool designed to detect prompt injection attacks targeting Large Language Models (LLMs). It employs a multi-layered approach combining regex signature matching, structural anomaly scoring, and advanced DeBERTa transformer classification. It is essential for securing input validation layers in chatbots, AI agents, and RAG pipelines, and for retrospectively auditing LLM interaction logs against OWASP standards.

This framework implements robust guardrails for LLM applications, validating both user inputs and model outputs. It detects prompt injection, sensitive PII (Personally Identifiable Information), toxic content, and ensures structured output compliance using advanced tools like NVIDIA NeMo Guardrails and Guardrails AI. Essential for building secure, reliable chatbots and RAG systems.

This skill intercepts the response flow before generation, allowing the user to explicitly control the desired depth, length, and token usage of the AI's answer. It estimates the complexity of the input prompt and presents multiple options (e.g., 25%, 50%, 75%, 100%), ensuring the output matches the user's need, whether they require a brief summary or a comprehensive, detailed analysis.

Handles Feishu calendar operations like creating and updating events, inviting attendees, querying availability, and managing repeat instances with timezone-aware ISO 8601 inputs, ensuring hosts stay in sync with RSVPs.

Compresses natural-language memory files such as CLAUDE.md, todo lists, and preferences into terse caveman-style text to reduce input token usage. Preserves code blocks, inline code, URLs, headings, and important technical terms while backing up the original to an out-of-tree data directory.

Sanitize user prompts through Google Model Armor templates via the gws CLI command, ensuring outbound safety and supporting stdin, text, or explicit JSON inputs for prompt filtering.