Skill UI

This internal agent skill defines the structured procedure for displaying bug triage results in terminal markdown format. It generates comprehensive summaries of bug clusters, provides detailed views of evidence and reports, and handles the parsing and execution flow for user review commands (e.g., `details`, `file`, `dismiss`, `escalate`). It is a core component for automated bug triage and defect management workflows.

A comprehensive guide for writing, debugging, and maintaining Windows batch files (.bat/.cmd). This skill covers advanced CMD scripting techniques, including environment variable management, complex control flow (IF, FOR), argument parsing, delayed expansion, and command chaining. Ideal for automating system tasks, creating command-line utilities (CLI), managing deployments, or writing scheduled task scripts in Windows environments.

This comprehensive playbook details advanced techniques for detecting Prototype Pollution vulnerabilities in JavaScript environments (Node.js and browser). It covers various attack vectors, including using `__proto__` and `constructor.prototype` paths, and provides black-box testing payloads for JSON parsing, query string merging, and deep object assignment. Use this when analyzing applications for potential Remote Code Execution (RCE) or unintended property leakage via object merging sinks.

This skill provides a comprehensive playbook for testing the security of SAML-based Single Sign-On (SSO) solutions. It focuses on validating assertion trust by checking signature coverage, audience and recipient restrictions, handling of Assertion Consumer Service (ACS), and identifying potential XML parsing weaknesses or IdP/SP confusion flaws. Use this when evaluating enterprise authentication systems for critical security vulnerabilities.

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.

LiteParse is a fast, open-source, and local document parser designed for layout-aware text extraction from PDFs, images, and office files. It outputs structured JSON with bounding boxes and metadata, making it ideal for building advanced RAG systems, citation grounding, and complex data pipelines without needing cloud APIs. Features include OCR, batch processing, and page screenshot capture.

An internal tool designed to validate candidate meta-skills (SKILL.md) before they are registered in the system. It performs comprehensive linting checks, including structural validation (G1: parsing, reference checks, XML escaping) and a scheduler dry-run (G2) using stub executors, ensuring the skill is deterministic and functional. Output diagnostics in JSON format.

Pi Agent is a highly extensible, minimal terminal coding harness designed for building sophisticated AI applications and complex workflows. It enables developers to integrate custom AI providers, local models (via llama.cpp router), skills, and TUI components. Use it for everything from basic command execution and data parsing to advanced orchestration, web search, and structured interaction via SDK, RPC, or JSON event streams.

This guide details the process of parsing core Windows forensic artifacts, including MFT, Prefetch, Registry hives, ShellBags, and Amcache. Utilizing Eric Zimmerman's specialized EZ Tools, raw evidence is converted into standardized CSV/JSON formats. This process is crucial for Digital Forensics and Incident Response (DFIR) investigations, enabling analysts to reconstruct program execution, file access history, and persistence mechanisms, which can then be loaded into Timeline Explorer for unified analysis.

Implements a robust caching strategy by using the SHA-256 hash of file content as the primary key, instead of the file path. This pattern is essential for high-cost file processing pipelines (like OCR, PDF parsing) because it ensures cache hits survive file renaming or moving, while still automatically invalidating entries when the content changes. It adheres to the Single Responsibility Principle (SRP) by isolating caching logic into a service layer wrapper.

This skill provides a detailed, side-by-side comparison of advanced LLM agent evaluation frameworks, such as ruflo and HAL. It helps researchers and developers identify capability gaps—including real code execution, full web browsing, and structured file parsing—and prioritize architectural improvements for robust agent benchmarking and performance tracking.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.