Skill UI

This skill provides a comprehensive, step-by-step workflow for building complete Power BI reports from scratch using the pbir CLI. It guides users through model discovery, defining design briefs, visual layout, data validation, and final publishing. Ideal for automating the entire business intelligence (BI) reporting process and ensuring adherence to professional design standards.

Atlas Ledger is a critical companion designed to provide lasting 'memory' to project contracts. It automatically processes detected deviations or 'drift' (e.g., from audits or reviews) and distills these instances into formal, actionable contract clauses (WHEN/DON'T/INSTEAD). These clauses are written to a persistent project memory file, Atlas.md. This mechanism ensures that lessons learned from failure are systematically captured, allowing future contract cycles to incorporate these rules, thereby strengthening overall project governance and reliability.

This skill processes raw, unstructured materials (e.g., conversations, reports, controversies) into highly structured, reusable casebook entries. It focuses on identifying core facts, defining mechanism chains, and mapping responsibility links while ensuring rigorous data anonymization. It is designed for deep knowledge management and pattern recognition, helping users build a reliable, reusable knowledge base for future professional analysis, rather than generating immediate advice.

A sophisticated automated loop that uses vision or video models to evaluate subjective aesthetic quality (e.g., timing, color grade, layout). Instead of relying on numeric metrics, the system renders multiple parameterized variants into one artifact, asks the AI to rate them, and suggests optimal values, repeating the process until convergence. Ideal for design iteration where human judgment is the primary success criteria.

This skill transforms vague, unclear, or half-formed business opportunities and brief descriptions into highly structured, actionable problem statements. It guides the user through a three-stage process: reframing the core questions, rigorously defining in-scope and out-of-scope boundaries, and designing a minimum viable research plan. It ensures that ambiguous inputs are converted into clear, executable steps, allowing the user to respond with confidence rather than asking for more clarification meetings.

This skill automates a structured Product Management (PM) design review process. It assesses a Figma design against defined product requirements (PRD) and user flows, ensuring functional completeness and readiness for engineering handoff. It checks for requirements coverage, handles edge cases (empty/error/loading states), identifies functional concerns (not aesthetic), and provides a clear, actionable approval status.

This skill systematically defines all necessary variants, states, and properties for a Figma component before development. It ensures comprehensive coverage of all states (e.g., Hover, Disabled, Loading) and properties (e.g., Size, Type), generating a robust variant matrix that prevents critical design gaps and streamlines the entire component building process in Figma.

This skill acts as a sophisticated router for frontend design, providing access to 48 deeply opinionated UI styles (e.g., Material, Brutalism, Glassmorphism). It ensures professional aesthetic coherence by enforcing specific style guidelines, utilizing curated universal color palettes, and guiding the implementation process for both web and mobile applications.

This skill implements the LLM Council concept, enabling multiple open-weight language models to collaborate on complex queries. The process follows a three-stage deliberation: 1) Parallel independent responses from selected models; 2) Peer-to-peer ranking of the responses; and 3) Final synthesis by a designated Chairman LLM. Powered by Fireworks AI, it ensures deep, multi-faceted analysis and consensus building for high-stakes decision support.

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 skill designed to detect and mitigate indirect prompt injection attacks. It scans untrusted external content—including web pages, PDFs, and images—for hidden, obfuscated instructions before an LLM agent processes them. The pipeline combines content extraction (HTML, OCR), normalization, and dedicated ML models (e.g., LLM Guard, Prompt Guard 2) to ensure data safety.

A specialized diagnostic skill designed to analyze failing GitHub Actions workflow logs. It identifies the root cause of crashes, such as missing secrets, environment mismatches, or deprecated syntax. The output provides precise YAML or code diffs required to fix the pipeline and optimize CI/CD processes.