Skill UI

This skill provides deep technical expertise regarding the DWARF debugging format (v3-v5) and the DWARF standard. It assists in analyzing, parsing, verifying, and generating debug information from compiled binaries. Use cases include answering questions about the DWARF specification, writing code that interacts with DWARF data, and utilizing tools like `dwarfdump` or `readelf` for structural validation and quality metric extraction.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

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.

`jq` is the standard and powerful CLI tool for querying, filtering, and reshaping JSON data. This skill provides expert-level practical patterns for working with deeply nested structures, handling complex transformations (like flattening, renaming, and grouping), and integrating JSON logic into shell pipelines. Essential for parsing API outputs (AWS, GitHub), log files, and building robust Bash scripts.

Detects when a skill's frontmatter block lacks its closing delimiter so editors can fix the metadata formatting before parsing fails.

A comprehensive guide for developers migrating between different versions of the Flexport API, specifically detailing changes from v1 to v2. It covers breaking changes in request headers, response parsing (e.g., _object to data), pagination formats, and error structures, ensuring smooth integration updates.

Establishes comprehensive Continuous Integration and Continuous Deployment (CI/CD) workflows for Fathom meeting intelligence. This setup includes unit tests using mocked transcripts to validate data parsing logic and robust integration tests that connect to the live Fathom API. It ensures the reliability of webhooks and data processing for real-time meeting summaries and action items upon every pull request or merge.

A comprehensive workflow for developing and testing financial applications integrated with Fondo. It enables developers to simulate or parse real Fondo exports (CSV/Accounting tools) locally, providing a fast feedback loop for building dashboards, calculating R&D credits, and analyzing burn rates without relying on live API connectivity. Supports mock data for rapid iteration and real export parsing for production validation.

This SDK provides production-ready patterns for integrating with Fondo's tax and accounting data. It includes a Singleton Client for authenticated REST calls, robust error wrappers (handling retries and authentication), a Query Builder for flexible API requests, and structured types for parsing transactions, accounts, and reports. It is designed for building reliable internal financial tools.

This skill serves as a compatibility test to ensure that skills compiled from MDA (Metadata Definition Architecture) sources are correctly recognized and installed by the OpenClaw platform. It validates the integrity of the skill loading process, confirming successful parsing and integration.

Implement secure API design patterns including authentication, authorization, input validation, and protection against vulnerabilities. Covers JWT token contracts, refresh sessions, resource authorization, and input parsing. Suitable for backend developers securing endpoints, preventing abuse, and ensuring data integrity in production environments.

This skill provides expertise in analyzing DWARF debug files and understanding the DWARF debug format standard versions 3 through 5. It is used for parsing DWARF information from compiled binaries, answering questions about the DWARF standard, writing or reviewing code that interacts with DWARF data, and verifying DWARF data integrity using tools like dwarfdump and llvm-dwarfdump.