Results for “yargen”

Expert guidance for Yjs, the high-performance CRDT (Conflict-free Replicated Data Type) framework for building collaborative applications. Helps developers implement real-time document editing, offline-first sync, and peer-to-peer collaboration with automatic conflict resolution.

Outlines — structured generation with guaranteed JSON/regex output from local LLMs

Creates a new Jetson target-platform profile YAML by selecting a reference devkit and optional custom carrier, then updates the active target pointer.

Switch the active Jetson target-platform pointer to an existing profile YAML. Use before customize/build/flash to change target; not for authoring profiles — use jetson-init-target instead.

Selects Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes based on Orin SM 8.7 vs Thor SM 11.0 and JetPack version.

Comprehensive guide to yarn. Master the concepts, implementation, best practices, and real-world applications of yarn in professional environments.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

XState Typegen

Design JWT strategies with refresh tokens. TRIGGERS - Use when user needs help with jwt-strategy related tasks.

Create, review, debug, test, or reproduce Python Jupyter notebooks by inspecting format, executing cells top-to-bottom in a clean kernel, and verifying outputs.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

Regenerate .codex/agents/*.toml from agenteam.yaml and plugin defaults.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

HTTP middleware, JWT authentication, auth middleware, custom guards, JWT guard, route middleware, request filter, middleware group, Acorn middleware, guard, rate limiting, CORS, CheckRole, throttle, bearer token, request filtering, middleware registration, HTTP Kernel — using Acorn's Laravel middleware stack inside WordPress/Sage/Bedrock

AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.

LEMON v2.0 — The Degen Fader. Identifies historically reckless traders (DEGEN activity + CHOPPY consistency) on Hyperliquid, monitors their live positions, and counter-trades them when they're bleeding at high leverage. If a cluster of degens goes max-leverage long on a coin and starts losing, LEMON shorts it — betting on their inevitable liquidation cascade. DSL exit managed by plugin runtime via runtime.yaml.

宫城良田（少年漫）认知与表达框架（压缩蒸馏）：小个子速度、耳钉人设、电光第一步 触发：灌篮高手 等。虚构

Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.

PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions, create sound effects, or perform melody-conditioned music generation.

Runs high-performance genomic interval analysis with gtars (databio), a Rust toolkit with Python bindings — the performance-critical backend for the geniml ML library. Use when computing overlaps/jaccard/coverage between BED region sets, indexing intervals with IGD, generating uniwig accumulation/coverage tracks, tokenizing genomic regions for ML, splitting single-cell fragments into pseudobulks, or computing GA4GH refget sequence digests. NOT for training region embeddings (use alterlab-geniml) or non-genomic spatial joins (use alterlab-geopandas). Part of the AlterLab Academic Skills suite.

Advises assessing efficacy and safety at least monthly for the first 3 months, then at least every 3 months for all patients prescribed weight‑loss medications. Triggers include clinician questions such as “How often should I check progress after starting orlistat?” or “What is the follow‑up schedule for a patient on liraglutide?”.