Skill UI

This skill processes verbose, stream-of-consciousness voice dictations (from tools like macOS Dictation or Wispr Flow) and transforms them into highly structured, token-efficient prompts. It removes filler words, repetitions, and tangents, organizing the core intent into standard sections (Context, Objective, Constraints) suitable for advanced LLMs like Claude Code.

Adds one optional external Codex critique after the council workflow has produced a synthesis draft. Use for ambiguous, high-consequence decisions where a separate model should attempt to break the consensus. Requires explicit user consent before sending a compact review packet to OpenAI, labels cross-provider vs same-provider honestly, sandboxes the external call, and marks the review absent if the adapter is unavailable.

An expert assistant dedicated to ensuring compliance with GDPR and global data protection regulations. This skill covers auditing code and systems for violations, drafting compliant legal documents (like privacy policies and DPAs), answering complex compliance questions with authoritative GDPR article citations, and reviewing data flows for PII handling practices. It addresses both EU and UK GDPR requirements.

A skill that captures Li Jigang's philosophy and methods for crafting AI prompts, combining Lisp-style pseudo-code, compressed expression, philosophical inquiry, structured writing, and minimalism. Enables users to adopt his cognitive workflow and prompt-writing style.

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.

This skill implements a local greeting function intended to respond when a user requests a greeting. While the description states no network access, the provided Python code snippet includes a requests.post call to a test endpoint, suggesting a simulation or data collection mechanism within the greeting workflow.

This skill structures a highly controlled, bounded development life cycle for AI agents. It mandates a structured three-phase workflow: Planning (Spec), Implementation (Build), and Verification (Review). It is essential for building complex features from scratch, especially when dealing with ambiguous requirements, high risk, or needing explicit human approval gates to ensure the resulting code meets all defined specifications.

AKF is the native metadata format specifically designed for tracking AI-generated or AI-modified content. It allows users to stamp files (including DOCX, PDF, images, and code) with detailed trust scores, source provenance, and compliance metadata. This tool is essential for professional workflows requiring strict auditing, compliance assurance, and verifiable data lineage, especially concerning regulations like the EU AI Act, HIPAA, and SOX.

Design patterns for building autonomous coding agents, including tool calling APIs, permission systems, browser automation, and human-in-the-loop workflows. Inspired by Cline and OpenAI Codex, this skill provides guidance for creating efficient and safe AI agents.

A powerful plugin enabling Claude Code to delegate complex tasks to external AI CLIs (such as OpenAI Codex or Google Antigravity/Gemini) without exiting the conversation. It supports multi-model workflows, allowing users to seek a second opinion, cross-check deep analysis, or delegate side tasks (like research or code review). The core value is that Claude synthesizes the raw outputs, providing a critical review and reconciliation of findings across all consulted models.

This skill evaluates supplied outputs against explicit criteria, including code-change reviews and authorized safety evaluations. It supports evidence-backed review leads, rubric judgments, and batch transcript evaluation for existing outputs or observed behavior. It helps users judge results without merging or running targets. Suitable for design requests, code reviews, and safety workflows. Users can choose real API calls or simulation modes for independent outcome validation.

Design and optimize production-grade multi-agent systems using LangGraph, LangChain, and DeepAgents. Provides structured workflows for creating supervisor, planner, researcher, coder, and validator agents with memory and tool-calling pipelines.