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Found 1,382 Skills
RabbitMQ integration testing with @SpringRabbitTest, RabbitListenerTestHarness, TestRabbitTemplate, and Testcontainers. Covers Java/Spring, Node.js, and Python. USE WHEN: user mentions "rabbitmq test", "@SpringRabbitTest", "RabbitListenerTestHarness", "TestRabbitTemplate", "RabbitMQContainer", "rabbitmq integration test" DO NOT USE FOR: RabbitMQ configuration - use `rabbitmq` skill; Spring AMQP usage - use `spring-amqp` skill; Generic testcontainers - use `testcontainers` skill
Operate InstaVM infrastructure: run ephemeral sessions, create or manage VMs, host or deploy apps, take snapshots, clone machines, register SSH keys, expose shares, set egress, mount volumes, and use platform APIs. Use this whenever the user mentions InstaVM, instavm.io, the `instavm` Python SDK, `ssh instavm.dev`, app hosting, or VM lifecycle work, even if they do not explicitly say "InstaVM".
MUST USE for any task involving the dotenvx CLI tool — encrypting .env files, running commands with injected env vars, managing secrets across environments, and decrypting at runtime. Use this skill whenever the user mentions dotenvx, dotenv encryption, DOTENV_PRIVATE_KEY, encrypted .env files, or the dotenvx encrypt/run/set/get/decrypt/keypair commands. Also trigger when the user wants to: commit .env files safely to git, stop sharing secrets over Slack/chat, encrypt environment variables with public-key cryptography, set up multi-environment .env configs (production/staging/ci), manage secrets in a monorepo with -fk flag, migrate from python-dotenv or plain dotenv to encrypted envs, inject env vars into any process across any language (Node, Python, Ruby, Go, Rust, etc.), or configure CI/CD pipelines (GitHub Actions, Docker) with encrypted env files. This skill contains the authoritative CLI reference — without it, responses will hallucinate non-existent commands and flags.
Build with MPP (Machine Payments Protocol) - the open protocol for machine-to-machine payments over HTTP 402. Use when developing paid APIs, payment-gated content, AI agent payment flows, MCP tool payments, pay-per-token streaming, or any service using HTTP 402 Payment Required. Covers the mppx TypeScript SDK with Hono/Express/Next.js/Elysia middleware, pympp Python SDK, and mpp Rust SDK. Supports Tempo stablecoins, Stripe cards, Lightning Bitcoin, and custom payment methods. Includes charge (one-time) and session (streaming pay-as-you-go) intents. Make sure to use this skill whenever the user mentions mpp, mppx, machine payments, HTTP 402 payments, Tempo payments, payment channels, pay-per-token, paid API endpoints, or payment-gated services.
Use this skill when working with PostHog - product analytics, web analytics, feature flags, A/B testing, experiments, session replay, error tracking, surveys, LLM observability, or data warehouse. Triggers on any PostHog-related task including capturing events, identifying users, evaluating feature flags, creating experiments, setting up surveys, tracking errors, and querying analytics data via the PostHog API or SDKs (posthog-js, posthog-node, posthog-python).
Use this skill when working with Sentry - error monitoring, performance tracing, session replay, cron monitoring, alerts, or source maps. Triggers on any Sentry-related task including SDK initialization, issue triage, custom instrumentation, uploading source maps, configuring alerts, and integrating Sentry into JavaScript, Python, Next.js, or other supported frameworks.
Logback - flexible and powerful logging framework for Java and Spring Boot applications. Successor to Log4j with native SLF4J support, async logging, and automatic file rotation. USE WHEN: user mentions "logback", "spring boot logging", "java logging configuration", asks about "logback-spring.xml", "rolling file appender", "async logging in java" DO NOT USE FOR: SLF4J API usage - use `slf4j` instead, Log4j2 - use separate Log4j2 skill, Node.js logging - use `winston` or `pino` instead, Python logging - use `python-logging` instead
NotebookLM CLI wrapper via `python3 {baseDir}/scripts/notebooklm.py` (backed by notebooklm-py). Use for auth, notebooks, chat, sources, notes, sharing, research, and artifact generation/download.
FastAPI integration testing specialist. Covers synchronous TestClient, async httpx AsyncClient, dependency injection overrides, auth testing (JWT, OAuth2, API keys), WebSocket testing, file uploads, background tasks, middleware testing, and HTTP mocking with respx, responses, and pytest-httpserver. USE WHEN: user mentions "FastAPI test", "TestClient", "httpx async test", "dependency override test", "respx mock", asks about testing FastAPI endpoints, authentication in tests, or HTTP client mocking. DO NOT USE FOR: Django - use `pytest-django`; pytest internals - use `pytest`; Container infrastructure - use `testcontainers-python`
Rust testing with cargo test, tokio-test, and mockall. Covers unit tests, integration tests, async testing, mocking, and benchmarks. USE WHEN: user mentions "rust test", "cargo test", "mockall", asks about "#[test]", "#[tokio::test]", "proptest", "criterion", "async rust testing" DO NOT USE FOR: JavaScript/TypeScript - use `vitest` or `jest`; Java - use `junit`; Python - use `pytest`; Go - use `go-testing`; E2E browser tests - use Playwright
Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use this skill whenever the user wants to create an Airflow plugin, add a custom UI page or nav entry to Airflow, build FastAPI-backed endpoints inside Airflow, serve static assets from a plugin, embed a React app in the Airflow UI, add middleware to the Airflow API server, create custom operator extra links, or call the Airflow REST API from inside a plugin. Also trigger when the user mentions AirflowPlugin, fastapi_apps, external_views, react_apps, plugin registration, or embedding a web app in Airflow 3.1+. If someone is building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface, this skill almost certainly applies.
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.