Skill UI

This utility reads the raw content of a UTF-8 text file and emits it verbatim to stdout. It is specifically designed for meta-skills that require round-tripping an artifact through disk, allowing users to manually edit structured files (like scripts or YAML) during review pauses. Unlike standard built-in readers that add line numbering, this method preserves the original bytes, making it suitable for downstream parsers.

This powerful utility concatenates multiple numbered MP4 segments (e.g., 1_*.mp4, 2_*.mp4) into a single, cohesive video file. It supports optional fade transitions and normalizes video parameters, including resolution, FPS, and codec, ensuring a professional and unified output. It is ideal for stitching together short clips produced by automated workflows into a final reel or presentation.

Build with and use Pi, a minimal terminal coding harness. Covers install, provider and model config, TypeScript extensions, skills, packages, themes, SDK embedding, RPC and JSON event streams, session parsing, local llama.cpp models, custom providers, TUI components, and ecosystem packages like pi-subagents and pi-mcp-adapter.

This guide details the use of Deneb, a certified custom visual, to embed Vega and Vega-Lite declarative visualization specifications directly into Power BI reports (PBIR). It outlines the complete workflow, from data binding and spec authoring to injection and validation using the pbir CLI. It emphasizes best practices, such as preferring Vega-Lite and handling complex field name escaping for robust report mutation.

A comprehensive guide and workflow for embedding R scripts, primarily using the ggplot2 library, into Power BI Reports (PBIR). This tool enables the creation of high-quality, static, publication-ready statistical visualizations directly within the report canvas by executing R code on the defined dataset. It covers the full lifecycle: adding the visual, writing the script, injecting the code, and validating the output.

Provides a comprehensive CLI reference for Tabular Editor 2 (TE2). It enables automated model management, including deployment to XMLA endpoints, running embedded scripts, performing Best Practice Analysis (BPA), and handling model exports (TMDL). This skill is essential for integrating data model lifecycle management into CI/CD pipelines and automating repetitive tasks from the command line.

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 skill surfaces and prioritizes hidden assumptions embedded in product briefs, PRDs, or concept descriptions. It assesses assumptions across Desirability, Feasibility, Viability, and Usability, scoring them by Confidence and Impact to create a structured, prioritized map. It is essential for product risk auditing, validating product plans, and ensuring the robustness of early-stage concepts.

This skill generates a comprehensive, data-driven Customer Health Scorecard for any account. It assesses renewal risk, expansion potential, and overall account vitality by scoring five key dimensions: Product Adoption, Engagement, Outcomes, Support Health, and Commercial status. The output provides a weighted score (out of 100) and a RAG status (Red, Amber, Green), guiding Customer Success Managers and leadership on immediate risks and recommended actions.

Transforms complex financial model outputs (P&L, balance sheet figures) into clear, structured, and compelling written narratives. Ideal for preparing board packs, investor updates, or executive management reports by translating mere numbers into actionable stories. The output provides headline insights, detailed driver analysis, variance explanations, and robust forward-looking commentary tailored to specific audiences.

This comprehensive guide walks new engineers through setting up a complete local development environment from scratch. It covers all necessary prerequisites, including installing required tools (Git, Docker, Go, etc.), cloning repositories, configuring environment variables, and running initial tests. The goal is to ensure a consistent, working environment and allow new team members to start contributing code quickly and confidently.

Generates comprehensive, structured documentation for microservices or internal platforms. This skill covers service identity, architecture context, APIs, SLAs, data classification, and operational runbooks, making it ideal for developer portals, service READMEs, and internal service registries. It ensures new team members can quickly understand how to use and depend on a shared service.