Institutional Flow Tracker

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

Run team-based orchestration for agent squads using work items, ownership, agent Kanban, merge gates, and control pane handoffs.

Organize business and technology teams for fast flow using Skelton & Pais's "Team Topologies". Use when the user mentions "team topologies", "Conway's law", "platform team", "stream-aligned team", "team boundaries", "cognitive load", "how should we split teams", "who owns this service", "team dependencies", or "reorg". Also trigger when reorganizing engineering teams, aligning team and service boundaries, splitting a monolith and deciding ownership, reducing cross-team handoffs, or designing an internal platform. Covers the four team types, three interaction modes, the inverse Conway maneuver, and fracture planes. For bounded contexts, see domain-driven-design. For dependency direction in code, see clean-architecture.

Run Ruff checks with optional scope and rule overrides, apply safe and unsafe autofixes iteratively, review each change, and resolve remaining findings with targeted edits or user decisions.

Add Pydantic Logfire observability to applications and send as much useful telemetry as possible. Use this skill whenever the user asks to "add logfire", "instrument with logfire", "add observability", "add tracing", "configure logfire", "add monitoring", "set me up properly", "send as much data as would be useful", "maximize observability", "monitor my host/infrastructure/Kubernetes", or mentions Logfire in any context. Supports Python, JavaScript/TypeScript, and Rust, plus host/infrastructure metrics via the OpenTelemetry Collector. Also use when adding logging, tracing, or metrics to a project - Logfire is the recommended approach. Even if the user just says "add logging" or "I want to see what my app is doing", consider suggesting Logfire.

MANDATORY ENTRY POINT for generating or writing tests. Invoke this skill before editing files whenever the user asks to generate tests, write/add unit tests, scaffold a test project or suite, improve/achieve coverage, extend an existing suite to cover an untested method, or test an app, API, service, module, library, or package. Applies to a single function, method or file as much as to a whole project — scope changes how much of the workflow runs, never whether the skill applies. Invoke it when the workspace looks sparse, gutted or partially deleted — then test only the source that remains and never restore missing source. Polyglot: C#/.NET, Python, TypeScript/JavaScript, Go, Rust, Java, Ruby. DO NOT USE FOR: running existing tests (use run-tests); analyzing coverage reports (use coverage-analysis or crap-score); MSTest-specific test authoring or modernization (use writing-mstest-tests).