Skill UI

This meta-skill automates the creation of professional, distributable PDF briefings. It seamlessly chains together web searching (using multiple engines), summarizing the gathered content into bullet points, and finally rendering the structured material into a beautifully styled A4 PDF document. Ideal for quickly turning research topics or web articles into ready-to-share reports and summaries.

This meta-skill orchestrates the entire academic writing workflow, from initial requirement extraction (topic, length, audience) to drafting, citation management, and final LaTeX/PDF compilation. It supports various modes, including compact skeletons for quick drafts and full manuscripts for publication-ready work. Ideal for users needing structured help with research papers or academic manuscripts.

An internal tool designed to validate candidate meta-skills (SKILL.md) before registration. It performs comprehensive structural linting (G1 rules) and a scheduler dry-run (G2) to ensure the workflow is deterministic, references are correct, and templates resolve properly. It outputs detailed JSON diagnostics.

An internal utility designed to manage the entire lifecycle of meta-skill proposals within the opensquilla ecosystem. It allows developers to atomically write new skill proposals, list existing candidates, and formally accept them after passing rigorous gate checks (linting, smoke testing, E2E). This tool ensures structured and controlled deployment of new skills.

An internal, non-user-invocable agent used by the meta-skill creator process. It executes positive (G3) and negative (G4) smoke tests against a candidate meta-skill definition (SKILL.md). It simulates meta-resolution using LLM-generated fixtures and reports the results in a structured JSON format.

A generic entry point for delegating self-contained, multi-step tasks to specialized LLM agents (Codex, Claude Code, Pi). Utilizes background processing tools to manage the full lifecycle of AI-driven sub-tasks, such as policy review, complex report synthesis, document generation, or large-scale code refactoring. Ideal for meta-skill workflows requiring full agent agency.

This skill reads the contents of a UTF-8 text file and outputs the raw bytes verbatim to stdout. It is designed for meta-skills that require round-tripping an artifact through disk for user inspection or manual editing between steps. Unlike standard file readers that prepend line numbers, this tool preserves the original structure, making it ideal for structured data formats like YAML, scripts, or SRT files that need to be accurately parsed downstream.

This skill provides expert guidance on writing and executing C# scripts to manipulate Power BI semantic model metadata using Tabular Editor. It enables advanced automation for bulk operations, managing calculation groups, setting security roles (RLS/OLS), and automating complex model changes via the TOM API. Supports both TE2 and TE3 environments.

Provides expert guidance for connecting to a local Power BI Desktop tabular model using PowerShell's Tabular Object Model (TOM) and ADOMD.NET. This skill enables model enumeration, executing complex DAX queries, modifying metadata (fields, measures, parameters), and managing the report canvas state via the Desktop Bridge. It is critical for direct model interaction when external CLIs are unavailable.

A comprehensive reference for the Power BI Enhanced Report (PBIR) JSON format. This skill details the structure of report metadata, including visual configurations (visual.json), report-level settings, theme inheritance, and complex formatting rules. It is essential for developers needing to read, interpret, or programmatically interact with the internal workings and schema of Power BI reports.

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 acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.