Skill UI

This tool automates the generation, updating, and maintenance of technical documentation. It features advanced 'drift detection' capabilities, comparing the current codebase against existing documentation to proactively flag inconsistencies. Use it when refreshing docs, updating content, or setting up recurring documentation maintenance schedules for APIs or full projects.

This tool facilitates querying structured federation audit logs, providing deep visibility into system access and changes. It supports advanced filtering based on major compliance frameworks (HIPAA, SOC2, GDPR) and event severity levels. By summarizing total events, PII detections, and trust changes, it is an essential resource for performing security audits, ensuring data privacy, and maintaining regulatory compliance.

A comprehensive audit worker that integrates Open Infrastructure Architecture (OIA) manifest analysis, threat modeling, and MCP security scanning. It computes a composite worst-case severity score and persists time-stamped audit records in a memory namespace. Primarily designed for scheduled CI/CD pipelines to detect architectural drift and security regressions over time.

This tool executes the advanced `metaharness/darwin` security benchmark (ADR-155). It rigorously evaluates the security detection capabilities of AI systems against a controlled ground-truth corpus, providing critical metrics like TPR, FPR, and patch-pass rates. It compares the champion model against multiple baselines, offering empirical data crucial for iterative security improvement and drift detection.

Systematically tracks complex, long-running objectives that span multiple sessions, weeks, or months. It ensures progress persistence through milestone checkpoints, detailed session logging, and crucial drift detection mechanisms, helping users stay on track for major project targets.

A comprehensive skill designed to fetch raw market data (Open, High, Low, Close, Volume) for a given symbol. It normalizes the time series data into relative OHLCV vectors, converts these vectors into 64-dimension embeddings, and stores them in a memory store. Crucially, it indexes the resulting vectors using the HNSW algorithm, enabling fast and precise nearest-neighbor search for pattern detection and similarity analysis in financial time series.

This tool detects and flags Personally Identifiable Information (PII) across text, code, and configuration files. It is essential to use this before committing code, writing logs, or generating model responses to prevent accidental exposure of sensitive data. It identifies various types of PII, including emails, phone numbers, SSNs, credit card numbers, API keys, and passwords, offering quick checks, detailed scans, and full context-aware analysis.

Standardized expert guidance for return authorization, inspection, grading, disposition decisions, refunds, fraud detection, and vendor recovery in retail and e-commerce operations.

This tool provides comprehensive safety scanning for untrusted inputs, such as user submissions, API payloads, and webhook data. It actively detects various security threats, including prompt injection, jailbreak attempts, instruction override attacks, and unsafe content. Beyond basic checks, it offers deep analysis, full multi-layer scanning, and even capabilities to train defensive models using detected threats. Essential for secure AI application development.

This skill automatically detects missing test coverage within a codebase and generates actionable, detailed test suggestions. It is essential for developers who need to identify untested code areas, assess coverage gaps after implementing new features, or proactively determine what unit tests should be written next to improve overall code quality and robustness.

This skill generates sophisticated trading signals by employing the neural-trader anomaly detection engine. It utilizes Z-score scoring to classify market movements into distinct patterns, such as spikes (breakout), drifts (trend forming), and oscillations (range-bound). The process integrates neural prediction and historical pattern searching to provide highly ranked, actionable signals, making it invaluable for algorithmic and quantitative trading strategies.

This skill clusters a collection of vectors (embeddings, e.g., code snippets) within a designated namespace using graph community detection algorithms (Spectral/Louvain). It is essential for discovering underlying themes, identifying natural groupings, and analyzing the relationships within large, unstructured vector datasets. Use it when you need to organize embeddings based on semantic relatedness rather than simple keyword matching.