Implement Terraform provider configuration and authentication with the Plugin Framework: provider schema for credentials (Optional + Sensitive attributes), environment variable fallbacks, credential provider chains (static config, then environment variables, shared credentials file, and platform identity), unknown-value guards in Configure(), secret redaction, configure-time credential validation, and diagnostics that name every source tried. Use when implementing or reviewing a provider's Configure method or provider schema, adding authentication options (API keys, tokens, profiles, credentials files, assume-role), deciding how a provider should resolve credentials, debugging "no valid credential sources" or missing-credentials errors, or unit testing credential resolution.

Use this when scaffolding a new Terraform provider with the Plugin Framework: workspace layout, go module setup, provider server main.go, and a provider.go with schema and Configure. Also use when a user wants to start building a provider for a new API or asks how to begin a terraform-provider-* project.

Opens a GitHub Pull Request with the standard PR template: Description, Staging Links, and JIRA ticket. Infers the ticket from the branch name and generates staging preview URLs from changed files after the PR is created. TRIGGER when: user asks to open, create, submit, make, update, or edit a PR or pull request, or wants to refresh staging links on an existing PR.

Fix violations of an eslintPluginScraps rule across the codebase. Use when asked to "fix lint violations", "apply a lint rule", "fix scraps rule errors", "roll out a lint rule", "enforce a rule codebase-wide", or "fix design system lint". Covers manual fixes, autofix, batching, and codemod strategies for large-scale rollouts.

Guide for creating and maintaining outbox-based eventually consistent operations in Sentry. Most commonly used for cross-silo data replication, but applicable anywhere eventual consistency is needed — including single-silo deferred side effects, audit logging, and event fanout. Use when asked to "add outbox", "add outbox replication", "replicate model to control silo", "replicate model to cell", "add outbox category", "write outbox signal receiver", "debug stuck outboxes", "outbox not processing", "data not replicating", "test outbox", "migrate model to use outboxes", "backfill outbox data", "outbox coalescing", "ReplicatedCellModel", "ReplicatedControlModel", "OutboxCategory", "OutboxScope", or "outbox_runner". Covers model mixins, category registration, signal receivers, testing, backfill, and debugging workflows.

Gate a Sentry feature behind a FlagPole feature flag. Use when adding a feature flag, registering a flag in temporary.py, checking a flag from Python or the frontend, enabling a flag in tests, or asking where FlagPole rollout config lives. Trigger on "add a feature flag", "gate this behind a flag", "register a flag", "features.has", "api_expose", "OrganizationFeature", "ProjectFeature", "FlagPole".

Use when adding, modifying, debugging, or reviewing dd-trace-js serverless platform integrations that create root invocation spans for AWS Lambda, Azure Functions, Google Cloud Functions, or similar runtimes. Triggers: serverless integration, function invocation root span, Lambda runtime, Azure Functions, GCP Functions, type = 'serverless', DD_LAMBDA_HANDLER, datadog-lambda-js, deployed serverless verification, manual serverless test.

Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.

Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket.

Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket.

Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.

Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.

Use when wiring decimalPrecision on a coralogix_dashboard widget. It is a bool on classic widgets and an int32 on dynamic ones, so copying either side's code silently produces the wrong type.

Writes Cypress E2E/component tests, configures `cy.intercept()` and `cy.session()`, authors custom commands, and wires CI artifacts. Use when creating E2E specs, component tests, or CI test pipelines. Trigger terms: cypress, e2e, component test, cy.intercept, cy.session

Stripe payment integration patterns, Checkout Sessions, billing/subscriptions, Connect platforms, and API best practices. Use when building, modifying, or reviewing any Stripe integration — including accepting payments, building marketplaces, setting up subscriptions, or implementing secure key handling.

This skill should be used when the user asks to analyze a UI screen recording and map interaction states into Figma. Trigger for requests such as "put video frames in Figma", "extract states from my recording", "map interactions from video to Figma", "analyze this screen recording", "create a storyboard from my video", "deconstruct this interaction in Figma", "annotate the UI states in my recording", or "pull the key moments from this video into Figma". Also trigger when the user references a video file (.mp4, .mov, .webm, .avi) together with Figma, design review, interaction analysis, prototypes, or UI states. The skill extracts key visual moments from a video, infers interaction triggers, and builds an annotated Figma Design storyboard using native Figma annotations and uploaded screenshot assets.

Executes Fastly CLI commands for managing CDN services, Compute deploys, and edge infrastructure. Use when running `fastly` CLI commands, creating or managing Fastly services from the terminal, deploying Fastly Compute applications, managing backends/domains/VCL snippets via command line, purging cache, configuring log streaming, setting up TLS certificates, managing KV/config/secret stores, checking service stats, authenticating with Fastly SSO, or working with fastly.toml. Also applies when working with Fastly service IDs in CLI context, or with `fastly service`, `fastly compute`, `fastly auth`, or any Fastly CLI subcommand. Covers service CRUD, version management, autocloning, and troubleshooting common CLI errors.

Runs Fastly Compute WASM applications locally with Viceroy, specifically for Rust and Component Model projects. Use when starting a local Fastly Compute dev server with Viceroy, configuring fastly.toml for local backend overrides and store definitions, running Rust unit tests with cargo-nextest against the Compute runtime, debugging Compute apps locally, adapting core WASM modules to the Component Model, or troubleshooting local Compute testing issues (connection refused, missing backends, store config). For non-Rust Compute work or understanding the Compute API, prefer the fastlike skill instead — its source code is easier to understand as a Fastly Compute API reference.