Run the controlled bakeoff benchmark on this machine. Prepares inputs, builds the Swift pipeline, runs the counterbalanced harness for all available configs (A, D, E, F), records results, and updates performance-notes.md. Use when the user says "run the bakeoff", "benchmark this machine", or invokes $bakeoff.
Adopts the Ilya Sutskever persona and judgment for on-device ML work in kokoro-coreml: PyTorch tracing, Core ML conversion, MIL/op compatibility, ANE/GPU/CPU scheduling, precision and parity validation, and bakeoffs vs reference PyTorch. Use when the user asks for that stance, mentions **Ilya**, **Sutskever**, **Bitter Lesson**, **scale vs hand-engineering**, or wants architecture or prioritization help on export/performance—not when the task is a narrow workflow already covered by **audit**, **debug**, or **execute-plan** unless they want persona-layer reasoning on top. Do not use for work with no ML or Core ML angle (generic docs-only or unrelated-repo tasks).
Judge generated Kokoro TTS audio clips with Gemini through llm-workflows. Use when the user asks whether synthesized speech sounds good, intelligible, whispery, corrupt, or acceptable, or when comparing PyTorch reference clips against Core ML / Swift pipeline output.
Profile CoreML model execution to determine which compute units (ANE, GPU, CPU) are actually being used, detect silent fallback, and identify performance bottlenecks. Triggered by keywords like "profile", "which compute unit", "is ANE running", "why is it slow", "silent fallback".
Validate CoreML model numerical correctness against PyTorch reference. Runs identical inputs through both pipelines and reports per-output correlation, max error, and pass/fail. Triggered by keywords like "validate", "numerical parity", "does CoreML match PyTorch", "correlation", "drift".
Master skill for managing the owner-operated Botnet Mac fleet across gist, botnet, and kokoro-coreml. Use when checking fleet health, rolling Mac workers, debugging scrape.remote_browser, tts.kokoro_coreml, or precompute workers, handling LaunchAgents, SSH fan-out, Cloudflare control-plane health, Kokoro/Core ML runtime issues, botnet fleet env vars, or writing fleet debugging notes.
Ingests offline deep-research exports into README guides: normalizes messy exports (escapes, data-URI images, Swift snippets), converts to clean markdown, verifies library/API claims with Context7 MCP when needed, adds cross-links across README/Guides and README/Notes, and updates related docs to link back. Use when adding or refreshing a guide from browser-downloaded research, Deep Research output, or similar raw material; when the user says guide-ingest, ingest-guide, research-to-guide, or wants corpus cross-linking for a new guide.
Redirect: cross-repo Core ML master router lives in personal skills. Use ~/.cursor/skills/coreml/SKILL.md for intent routing and guide links across kokoro-coreml and crossfade. Delegates procedures to repo child skills (coreml-validate, coreml-profile, bakeoff, debug).