Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom protocol), build a Spark connector or plugin in Python, implement a DataSourceReader or DataSourceWriter, pull data from or push data to a system via Spark, or work with the PySpark DataSource API in any way. Even if they just say "read from X in Spark" or "write DataFrame to Y" and there's no native connector, this skill applies.

Runs Fastly Compute WASM binaries locally and serves as the authoritative reference for Compute platform internals. The fastlike source code is highly readable and covers the host ABI, caching and purging APIs, KV/config/secret store interfaces, rate limiting with counters and penalty boxes, ACL lookups, the full request lifecycle, backend fetch semantics, and a built-in per-request profiler with hostcall spans, backend waterfalls, native CPU samples, and optional deep metrics (body bytes, cache outcomes, header summaries, wasm heap curve). Use when working with Compute runtime internals or host calls, understanding how edge data stores behave at runtime, profiling local Compute apps, or testing WASM binaries locally. Prefer this skill over Viceroy for any non-Rust Compute work — its source code is easier to understand as a Fastly Compute API reference.

Lints, tests, simulates, and formats Fastly VCL code using the falco tool. Also serves as the authoritative VCL reference via the falco Go source, which implements Fastly's full VCL dialect. Use when validating VCL syntax, running VCL linting, testing VCL locally, simulating VCL request handling, formatting VCL files, writing VCL unit tests with assertions, debugging VCL logic errors, looking up VCL function signatures or variable scopes, understanding VCL subroutine behavior, or running `falco lint`/`falco simulate`/`falco test`/`falco fmt`. Also applies when working with VCL syntax errors, type mismatches in VCL, choosing which VCL subroutine to use, or setting up a local VCL development and testing environment.

Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says "DSE", "design space exploration", "sweep parameters", "optimize", "find best config", or wants iterative parameter tuning.

Use when writing or reviewing Python code in this repo that calls Deepgram audio analytics overlays on `/v1/listen` - summarize, topics, intents, sentiment, diarize, redact, detect_language, entity detection. Same endpoint as plain STT but with analytics params. Covers both REST (`client.listen.v1.media.transcribe_url`/`transcribe_file`) and the WSS-supported subset (`client.listen.v1.connect`). Use `deepgram-python-speech-to-text` for plain transcription, `deepgram-python-text-intelligence` for analytics on already-transcribed text. Triggers include "diarize", "summarize audio", "sentiment from audio", "redact PII", "topic detection audio", "audio intelligence", "detect language audio".

Use when writing or reviewing Java code in this repo that builds an interactive voice agent over `agent.deepgram.com/v1/agent/converse`. Covers `client.agent().v1().v1WebSocket()`, `AgentV1Settings`, `sendSettings`, `sendMedia`, event handlers, provider configuration, and message injection. Use `deepgram-java-text-to-speech` for one-way synthesis or the STT skills for transcription-only flows. Triggers include "voice agent", "agent converse", "full duplex", "barge in", "function call", and "agent websocket".

Use when writing or reviewing Java code in this repo that calls Deepgram Text-to-Speech v1 (`/v1/speak`) for audio synthesis. Covers one-shot REST via `client.speak().v1().audio().generate(...)` and streaming synthesis via `client.speak().v1().v1WebSocket()`. Use `deepgram-java-voice-agent` for full-duplex assistants instead of one-way synthesis. Triggers include "tts", "text to speech", "speak", "aura", "streaming tts", and "speak websocket".

Use when writing or reviewing Java code in this repo that calls Deepgram Management APIs for projects, project models, API keys, members, invites, usage, and billing. Covers `client.manage().v1().*` plus related think-model discovery under `client.agent().v1().settings().think().models()`. Use `deepgram-java-voice-agent` for live agent conversations instead of admin APIs. Triggers include "management api", "list projects", "api keys", "members", "invites", "usage", "billing", and "models".

Use when writing or reviewing Python code in this repo that calls Deepgram Management APIs - projects, API keys, members, invites, usage, billing, models, and reusable Voice Agent configurations. Covers `client.manage.v1.projects`, project-scoped resources under `client.manage.v1.projects.*` (keys, members, members.invites, usage, billing, models, requests), global `client.manage.v1.models`, think-model discovery at `client.agent.v1.settings.think.models`, and `client.voice_agent.configurations.*`. Use `deepgram-python-voice-agent` when you want to run an agent interactively, this skill to PERSIST/LIST agent configs. Triggers include "management API", "list projects", "API keys", "members", "usage stats", "billing", "list models", "agent configurations", "manage.v1".

Use when writing or reviewing JavaScript/TypeScript in this repo that calls Deepgram audio analytics overlays on `/v1/listen` - summarize, topics, intents, sentiment, diarize, redact, detect_language, and entity detection. Same endpoint as plain STT, different params. Covers REST via `client.listen.v1.media.transcribeUrl` / `transcribeFile` and the WebSocket-supported subset on `client.listen.v1.createConnection()` / `connect()`. Use `deepgram-js-speech-to-text` for plain transcription and `deepgram-js-text-intelligence` for analytics on already-transcribed text. Triggers include "audio intelligence", "summarize audio", "diarize", "sentiment from audio", "redact PII", and "detect language audio".

Generate Instagram image ad concepts with visual descriptions, copy constraints (125/40/30 chars), and HTML mockups. Visual-first, mobile-native single-image ads for Feed and Stories.

Initialize warehouse schema discovery. Generates .astro/warehouse.md with all table metadata for instant lookups. Run once per project, refresh when schema changes. Use when user says "/astronomer-data:warehouse-init" or asks to set up data discovery.

Systematic troubleshooting for Falcon Foundry CLI errors, manifest validation failures, deploy failures, and development server issues. TRIGGER when user encounters CLI errors, `foundry ui run` not working, deploy failures, authentication issues, or any unexpected behavior during Foundry app development. Also trigger for headless/CI environment setup failures.

Where diamonds spawn in Minecraft 1.20.