Skill UI

Provides expert guidance for authoring and editing Tabular Model Definition Language (TMDL) files directly within PBIP projects. This skill is useful for making quick text-level fixes (like descriptions, format strings, or adding measures) when specialized tools (like Tabular Editor CLI or Power BI Desktop) are unavailable, especially for Git workflow or manual adjustments.

This index serves as the central router and map for the comprehensive C-level advisory bundle. It structures high-stakes business processes by indexing 14 core C-suite roles (CEO, CFO, CTO, etc.), orchestration capabilities, and cross-cutting functions (e.g., board meetings, onboarding). It guides users on where to start for strategic planning, corporate governance, and complex decision-making.

This advanced tool surfaces critical, often unrecorded, evidence of human-AI collaboration during development sessions. It analyzes git diffs and conversational context across multiple layers to classify the signal level (HIGH/MEDIUM/LOW), measure technical complexity, detect decision forks, and quantify the specific contribution of the AI model, providing structured data for post-session retrospectives and accountability.

This agent provides a critical framework for navigating corporate environments, contrasting internal jargon and process visibility (Ding In) with objective, real-world evidence and user experience (Ding Out). It helps users move beyond subjective 'feelings' or polished reports, structuring complex tasks into verifiable components: Goal, Acceptance Criteria, Action, Evidence, and Risk. Ideal for project management, status reporting, and critical thinking in a corporate setting.

This tool guides users through the critical process of designing experiments *before* data collection. It helps select appropriate structures—such as factorial, crossover, or blocked designs—to ensure that collected data is interpretable and allows for robust causal inference. Use it when planning scientific studies, assigning subjects, or optimizing responses across multiple factors.

This skill allows users to mine their extensive coding history across various AI agents (Claude, Codex, Gemini, etc.) into a persistent, structured corpus. It surfaces reproducible workflows as evidence and interactively helps the user forge these past practices into reusable, installed skills across their agent ecosystem. It is designed to synthesize judgment from raw transcripts into actionable, portable skills.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

This Main Agent acts as a central orchestrator for a specialized squad of AI agents, managing complex software development lifecycles. It guides projects through defined phases—from requirements gathering and planning to architecture, implementation, testing, and deployment. It maintains strict context discipline by summarizing agent outputs and referencing full artifacts, ensuring efficient, structured, and non-overwhelming communication to the user.

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.

This skill implements Autonomous Optimization (AO) using Hypothesis Tree Refinement (HTR). It iteratively improves concrete artifacts (like code, models, or data pipelines) by running structured, long-term experiments against a measurable objective. It maintains a persistent hypothesis tree, coordinating subagents to test hypotheses in isolation while using a held-out test evaluator to prevent overfitting and ensure reliable, cumulative research progress. Ideal for complex benchmark tuning, agent refinement, or optimizing entire workflows.

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.

Dep handles the entire journey from local code to production deployment. This skill automates containerization using Docker and docker-compose, establishes robust CI/CD pipelines (linting, testing, security scanning, deployment), manages environment variables and secrets, and generates Infrastructure as Code (Terraform/Pulumi) configurations. It ensures observability and provides rollback procedures.