Skill UI

A systematic auditor designed to evaluate the resilience and quality of complex AI workflows. It scans across five critical dimensions—Prompt Quality, Context Efficiency, Tool Health, Architecture Fitness, and Safety—providing a scored report and prioritized, actionable remediation steps. Use this tool to find hidden flaws and ensure robustness before deploying any AI agent or system.

A specialist tool designed to pressure-test AI Management Systems (AIMS) against the ISO 42001 standard. It executes six critical compliance checks, covering scope definition, risk mapping, policy adherence, and framework integration. Essential for pre-certification readiness, annual internal audits, and new system onboarding.

This skill searches GitHub and the web to discover the latest Japanese Natural Language Processing (NLP) resources, tools, and libraries (e.g., NER, LLM, TTS) that are not yet included in the awesome-japanese-nlp-resources list. It is designed for developers and researchers who want to track the cutting edge of Japanese language technology or prepare a contribution.

This skill assists users in defining, adapting, and designing bounded, structured feedback loops for AI agents. Whether you need to find a suitable existing workflow, fine-tune a published loop, or design a new process from scratch, this tool guides you through a safe, iterative methodology. It emphasizes setting clear stopping rules, guardrails, and verifiable, measurable steps to ensure the automation remains controllable and focused.

A comprehensive security tool designed to detect and defend against subtle prompt injection attacks hidden within untrusted multi-modal content, such as web pages, PDFs, and images. It scans for obfuscated payloads (e.g., hidden CSS, metadata, zero-width characters) by combining content normalization, heuristic analysis, and dedicated ML detectors, ensuring that LLM agents only consume sanitized, trustworthy input.

PyRIT (Python Risk Identification Tool) is an open-source automation framework designed for advanced, multi-turn adversarial red-teaming of conversational LLMs. It simulates real-world attack scenarios by orchestrating an attacker model and a scorer model in a feedback loop. Features include Crescendo (gradual escalation) and Tree-of-Attacks with Pruning (TAP) techniques to detect complex vulnerabilities like prompt injection and jailbreaks in stateful dialogues.