Skill UI

This skill allows agents to ingest external resources (local files, URLs, repositories) using `ov add-resource`, and to create long-lasting searchable memories from conversations using `ov add-memory`. It is essential for building a comprehensive, persistent knowledge base for the OpenViking context database, ensuring that learned insights and external data remain available across sessions and interactions. Use when external knowledge injection or context persistence is required.

This skill acts as an advanced extension layer, implementing a self-evolution protocol, maintaining session state continuity (Compaction), and providing KPI-style performance summaries. It allows users to simulate continuous professional development, track metrics, and master complex role-playing and interaction skills across multiple sessions, ensuring performance baselines are continuously adjusted and internalized.

Generate a Google Form tailored for event or session feedback and quickly distribute it to participants through Gmail, automating the survey creation and invitation process for productivity boosts.

Audit Google Meet attendance by listing recent conferences, participants, and session durations using the `gws meet` commands to confirm who joined and for how long.

Security-first development practices for web applications. Covers threat modeling with STRIDE, OWASP Top Ten defenses, input validation, parameterized queries, authentication and session hardening, security headers, dependency auditing, and supply-chain risk assessment.

This skill generates deep, clickable URLs to various resources within the Arize monitoring UI. It supports linking to specific traces, spans, sessions, datasets, evaluators, and annotation configurations. It is essential for sharing specific debugging artifacts or navigating directly to key data points within the MLOps platform by requiring precise resource IDs and time ranges.

A powerful tool that converts complex Unified Modeling Language (UML) diagrams—such as Sequence, Class, Activity, and State diagrams—into plain text ASCII art. This is crucial for documentation in environments that do not support image embedding, such as terminal sessions, README files, or Git commit messages. Supports both standard ASCII and enhanced Unicode ASCII formats for better visual fidelity.

This skill provides comprehensive tools to download, export, and deeply inspect existing Arize traces and spans. It is essential for understanding the full execution flow of complex LLM applications, debugging runtime errors, and performing root-cause analysis. Users can retrieve detailed data filtered by Trace ID, Span ID, or Session ID, enabling thorough analysis of conversational or multi-step agent behaviors.

Build agentic applications by embedding GitHub Copilot workflows into apps using Python, TypeScript, Go, or .NET. Create custom tools, manage sessions, and implement streaming responses without building orchestration logic. Ideal for developers integrating AI agents into existing software.

The Phoenix CLI is a powerful tool for debugging complex LLM and agent workflows. It allows users to fetch detailed traces, spans, and sessions, analyze errors, and structure findings using open and axial coding techniques. It is essential for investigating complex LLM failures, building robust failure taxonomies, and guiding the next steps in application evaluation after initial instrumentation.

Instrument LLM applications with OpenInference semantic conventions in Phoenix. Covers setup, auto and manual instrumentation, 9 span types, session tracking, production batching and PII masking for Python and TypeScript.

Keeps task_plan.md, findings.md and progress.md on disk as durable working memory for multi-step AI agent runs; lifecycle hooks inject project-scoped plan context, with local-only session recovery for research tasks needing 5+ tool calls.