Skill UI

skill-doctor grades the performance and quality of an AI agent setup using real, local conversation history (e.g., Claude Code/Codex sessions). It scores skills against defined rubrics for efficiency and code quality, drafts evidence-backed suggestions for improvement, and generates a comprehensive, shareable local report. All processing is strictly local and private.

This skill audits the security and integrity of legal AI components, including skills, prompts, workflows, and tools. It proactively detects prompt injection, malicious code, data exfiltration paths, and supply-chain risks before deployment or trust. Essential for maintaining confidentiality, privilege, and adhering to legal standards in AI environments.

A comprehensive security auditing tool for AI agent skills. It combines deterministic static scanning (SkillSpector) with deep semantic analysis to assess safety, trustworthiness, and potential risks. Determines if a skill is safe to install by checking for over-permissions, malicious behavior, and hidden dependencies.

This tool guides the analysis of work sessions by detecting user feedback (corrections, successes, edge cases). It evaluates these signals against quality criteria (Complete, Precise, Atomic, Stable) to propose structured improvements, update skill documentation (SKILL.md), and maintain a changelog.

This meta-skill audits, refactors, and standardizes the structure of existing skill definition files (SKILL.md). It ensures compliance with defined style guides, enforces best practices (Hard Rules, Decision Gates), and improves the overall quality and maintainability of LLM-based skills.

This skill automatically scans all defined user and project skills (from SKILL.md files) to create a comprehensive, centralized index. It manages skill paths, triggers, descriptions, and scopes, ensuring that downstream agents and delegators always have an up-to-date source of truth for available functionalities. It is crucial for maintaining system integrity and avoiding redundant skill definitions.

Verifies consistency and plausibility of Italian and EU legal citations in text produced by legal-tech skills or lawyers. Checks format, apparent existence, internal coherence, flags suspicious or unresolvable references. Use after IT/EU skill output with legal references or when lawyer asks to verify sources.

This deployment skill is designed to be integrated into the Playwright testing workflow. Its primary function is to validate the loading process of shared skills across the entire environment during the startup phase. It ensures that all necessary components and modules are correctly registered and available for reliable End-to-End (E2E) testing execution.

This is a synthetic, instructional fixture designed solely for verifying the delivery and access of community skills within the OpenSquilla live Skill Hub canary environment. It is crucial for testing platform capability (e.g., resource access validation) and should not be executed for actual functionality or command processing.

Convert text, markdown, or summaries into listenable MP3 audio using local CPU-only neural TTS. Rewrites prose for the ear, synthesizes with pocket-tts, and supports chaining with other skills for spoken briefings.

This skill acts as a Systems Engineer for the Andru.ia platform. It is designed to create, draft, and deploy new AI skills within a specific repository while adhering to the Diamond Standard. The workflow involves defining technical names, expert roles, and key outputs, then generating necessary documentation like README files and registry snippets. It ensures high-quality, non-generic skills with defined expert roles and proper folder structures.

Coordinates discovery, matching, and workflow orchestration across agent ecosystems, automatically scanning SKILL.md, ranking relevant skills, and executing multi-step plans for complex requests.