Skill UI

Use this skill to prompt the agent to zoom out from a specific code section, providing a broader, higher-level perspective. It helps map out the overall system architecture, identifying relevant modules, calling relationships, and how the current code snippet fits into the larger picture. Ideal when you are unfamiliar with a specific area or need system-level understanding.

Cavecrew provides a set of specialized subagents (investigator, builder, reviewer) designed for efficient code development delegation. Unlike vanilla agents, Cavecrew compresses output results, drastically reducing the token usage injected back into the main context. This allows users to maintain context over much longer, complex coding sessions, effectively preventing context exhaustion during large-scale refactoring, debugging, or multi-step feature development.

A deep-dive session designed to stress-test a technical plan against an existing domain model. This process forces the user to resolve ambiguities, sharpen terminology, and validate decisions by cross-referencing code and existing documentation (CONTEXT.md, ADRs). It ensures architectural rigor and completeness before implementation.

This router serves as the main entry point for comprehensive injection testing. It helps security professionals classify dangerous input flows—such as XSS, SQLi, SSRF, XXE, etc.—by determining the final sink or interpreter (e.g., browser context, database, XML parser, shell). Use it when unsure which specific injection vector to prioritize.

Provides essential database abstractions and utility classes for robust API development. This module includes components like FilterUtil, ValueFilter, and ValueFilterRegistry, allowing developers to implement standardized, efficient, and complex data querying and filtering logic across various database contexts.

A comprehensive, expert-level playbook covering XML External Entity (XXE) vulnerabilities. This guide details advanced attack techniques across multiple contexts, including SOAP, REST, SVG, and Office files. Key topics covered include blind detection, out-of-band (OOB) data exfiltration, local file reading (e.g., /etc/passwd), and chaining XXE to achieve SSRF or RCE.

A structured methodology for validating non-trivial decisions by subjecting them to an adversarial, fresh-context review. This process is crucial when correctness is paramount—such as in production, security-sensitive logic, or complex architecture—by forcing a skeptical examination that aims to disprove assumptions rather than simply approve them.

This guide assists developers in building robust Model Context Protocol (MCP) servers and clients using C#/.NET. It specifically targets the stable 1.x NuGet packages, providing detailed instructions for configuring various transports (STDIO, Streamable HTTP), defining custom tools, prompts, and resources. It helps solve common integration issues like stale preview pinning, complex HTTP wiring, and structured content handling, ensuring production-ready, standard-compliant code.

This guide details the architectural and implementation patterns for building modular applications or microservices using the tinystruct Java framework. It covers core concepts like using the @Action annotation for dual routing (CLI/HTTP), managing request state with Context, and using native Builder for JSON serialization, ensuring a lightweight and structured development environment.

A comprehensive guide to implementing schema.org structured data using JSON-LD format. Learn to correctly identify page types (Product, Article, Organization, FAQPage) and apply the necessary schema markup to help search engines understand content context. This skill covers initial assessment, core principles (accuracy, validation), and optimizing for rich results like star ratings, site search blocks, and rich snippets, significantly boosting search engine visibility and CTR.

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

This skill parses raw, verbose Simplicité log files from TXT format, filtering out noise and extracting only essential fields (e.g., timestamp, level, body). By converting logs into structured JSON, it significantly reduces the token size required for AI context optimization, making troubleshooting efficient and reliable.