Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Guide for adding new JSI functionality to the JavaScript Interface (JSI) layer. Use when the user asks to add, create, or implement new methods or features in the JSI Runtime interface. Covers all required files across JSI core, Hermes implementation, and SynthTrace replay support.
Use when needing to reorder, split, drop, or amend git commits that are not the top commit, without interactive editor access. Covers programmatic rebase via GIT_SEQUENCE_EDITOR, commit splitting with automated hunk selection, and metadata changes (author, message, dates) on any commit in a range.
Deploy a Microsoft Agent Framework (MAF) workflow as a managed online endpoint to an Azure ML workspace or an Azure AI Foundry hub-based project. Wraps any workflow into an init()/run() scoring script, creates conda environment, endpoint and deployment YAMLs, deploy script, and assigns RBAC. Supports managed identity auth and Application Insights tracing. WHEN: deploy MAF workflow, deploy agent-framework workflow, create online endpoint for MAF, deploy workflow to AML, deploy workflow to Foundry project, managed online endpoint for agent workflow, wrap workflow in scoring script, deploy agent as endpoint, realtime endpoint in Foundry project.
Convert an existing Prompt Flow Parallel Run Step (PRS) pipeline submission into an Azure ML PRS pipeline that runs a Microsoft Agent Framework (MAF) workflow. Wraps the MAF workflow into a PRS init()/run() entry script, generates the parallel component YAML and conda environment, and rewrites the pipeline submission script. Replaces what `load_component(flow.dag.yaml)` did automatically for Prompt Flow — produces the hand-built equivalent so that downstream pipeline code (`flow_node = flow_component(...)`, `flow_node.outputs.flow_outputs`, `flow_node.outputs.debug_info`, `flow_node.mini_batch_size`, scheduler, batch endpoint) stays unchanged. WHEN: convert promptflow PRS to MAF PRS, migrate PRS pipeline to agent framework, wrap MAF workflow as parallel component, bulk run MAF workflow, run agent framework as parallel run step, batch run MAF workflow on AML, submit MAF workflow as pipeline component, replace flow.dag.yaml with MAF workflow in pipeline, load_component equivalent for MAF workflow, MAF version of flow_component, load MAF workflow as component, wrap MAF workflow as flow component, MAF flow component, replace flow_node in pipeline with MAF workflow, keep flow_outputs and debug_info ports with MAF, MAF parallel component with connections={}, run MAF workflow as flow_node in AML pipeline, load_component('workflow.py') doesn't work. DO NOT USE FOR: converting the flow itself (use promptflow-to-maf), deploying as online endpoint (use maf-online-endpoint), enabling tracing only (use maf-tracing).
Enable tracing and logging for Microsoft Agent Framework (MAF) workflows. Configures OpenTelemetry export to Azure Application Insights and/or a generic OTLP endpoint using environment variables. Adds required packages to requirements.txt. WHEN: enable tracing, add tracing, enable logging, add logging, configure telemetry, Application Insights for MAF, OTLP export, observe workflow, monitor agent workflow, trace agent framework, instrument MAF, add observability, trace workflow executions, debug workflow.
Convert Prompt Flow flow definitions to Microsoft Agent Framework (MAF) workflows. Parses flow.dag.yaml, maps nodes to Executors, and generates runnable Python code using agent-framework 1.0.x. WHEN: convert promptflow, migrate promptflow, promptflow to MAF, promptflow to agent framework, convert flow.dag.yaml, migrate flow to MAF, convert PF flow, PF to agent-framework, convert DAG flow to workflow, migrate LLM flow. DO NOT USE FOR: writing new MAF workflows from scratch (no source flow), deploying MAF workflows (use maf-online-endpoint), enabling tracing (use maf-tracing), or general agent-framework Q&A.
Working on the RomM Python backend (backend/) — a FastAPI app with SQLAlchemy 2.0, Alembic, RQ/Redis, and Socket.IO. Use when adding or changing API endpoints, handlers, ORM models, response schemas, metadata-provider adapters, background tasks, database migrations, or backend tests. Covers the layered architecture, conventions, auth/scopes, the OpenAPI→frontend type pipeline, and the uv/pytest/alembic/trunk workflow. Trigger on any work under backend/.
Internationalization for the RomM frontend (both v1 and v2). Use whenever adding, renaming, or removing any user-visible string / translation key under frontend/src/locales/. Covers the en_US-is-source rule, the requirement to add every new key to ALL locale directories in the same change, namespace layout, and the check_i18n_locales.py validator enforced in CI. Trigger on any change to frontend/src/locales/**.
Building or modifying components in the RomM v2 frontend (frontend/src/v2/). Use when creating/editing v2 primitives (R* components in src/v2/lib/), shared composites, or feature composites — covers the three-tier model, file/folder conventions, SFC structure, import order, barrels, Storybook requirements, and v2 anti-patterns. Trigger on any work under frontend/src/v2/.
Universal input (mouse, touch, keyboard, gamepad) and responsive/universal-viewport layout in the RomM v2 frontend. Use when adding interactive v2 components, focus management, spatial navigation, gamepad/keyboard handling, modality-gated focus rings, breakpoints, or responsive layout. Covers useInput, focus geometry primitives, the overlay scope stack, useBreakpoint, and the data-bp/data-input attributes. Trigger on interactive or responsive work under frontend/src/v2/.