Skill UI

Configure automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for Juicebox AI data analysis integrations. This setup utilizes GitHub Actions to run comprehensive unit tests (with mocked datasets) and integration tests (connecting to live APIs) on every PR and merge to main. It ensures that data upload logic, analysis execution, and result parsing workflows are robust and reliable before deployment.

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.

Datamol wraps RDKit with Pythonic APIs tailored for drug discovery, offering SMILES/SELFIES/InChI parsing, standardization, descriptor and fingerprint calculation, clustering, 3D conformer workflows, and parallel/cloud-aware batch I/O while returning native rdkit.Chem.Mol objects.

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.

This skill details the forensic analysis of Windows LNK shortcut files and Jump Lists. It enables investigators to reconstruct user activity, track file access, and determine program execution history by parsing critical metadata like timestamps, paths, and system identifiers, even when the original artifacts are deleted.

This guide details advanced digital forensics techniques for recovering deleted file metadata and content from NTFS volumes. It focuses on analyzing the Master File Table (MFT), USN Journals, and $LogFile transaction records. Techniques include parsing MFT slack space and utilizing specialized tools like MFTECmd to reconstruct file system timelines and identify deleted evidence remnants.

This skill guides users through parsing Windows Prefetch files (.pf) to establish a comprehensive program execution history. It extracts critical forensic data such as run counts, timestamps (up to 8 events), and referenced files. This is essential for incident response, malware investigation, and building a reliable timeline of activity on a compromised system.

This comprehensive guide details the methodology for detecting and exploiting Blind Server-Side Request Forgery (SSRF) vulnerabilities. It covers crucial techniques including out-of-band (OOB) detection, internal network enumeration (port scanning, IP scanning), accessing cloud metadata endpoints (AWS, GCP, Azure), and bypassing common security filters using various encoding and parsing confusion methods. Ideal for penetration testing and cloud security assessments.

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.