Results for “grunt”

Finetunes the NV-Generate-CTMR MR-brain diffusion UNet from user-supplied NIfTI training volumes using a wrapper that stages configs and delegates to upstream scripts.

Generates synthetic body MRI volumes using NVIDIA's NV-Generate-CTMR rflow-mr model. Wraps the upstream diffusion inference pipeline with config staging, output validation, and NIfTI volume summarization.

Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK.

NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.

Builds production RAG and semantic search systems with Qdrant, covering collection setup, vector indexing, filtered and hybrid search, and integration with LangChain and LlamaIndex.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Detect and fix stale chunks (files that changed or were deleted since last indexing)

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Facilitates a read-only standup across git worktrees, branches, or PRs to compare changes and produce one consolidation plan.

The canonical Built GTM brand foundation for Heath Barnett. Generated from context/brand/built-gtm-brand.md. Do not edit this file by hand; edit canon and run context/sync/build-context.sh. Load before generating any public-facing content.

ONNX Runtime in Rust via the `ort` crate (2.x): loading sessions, configuring CPU/CoreML/CUDA execution providers, tensor I/O with ndarray, async-safe spawn_blocking wrapping, global thread-pool init, and debugging provider/opset issues

Runs a relentless interview that sharpens a plan or design and creates docs (ADRs and glossary) along the way. Use when the user wants to stress-test a plan or design, or produce decision records.

Review and prune the project context — archive completed work, summarize stale entries, and propose disabling unused skills.

Gatekeeping Git/PR by classifying change essence and recommending granularity, naming, and strategy. Use when PR preparation or commit strategy is needed.

Build and debug Retrieval-Augmented Generation pipelines — chunking, embedding, retrieval, reranking

Create grant budgets with line items and justifications. TRIGGERS - Use when user needs help with grant-budget related tasks.

Runs a relentless interview that sharpens a plan or design. Use when the user wants to be grilled on an idea, pressure-test a plan, or refine a design through questioning.

Reverse engineers stripped Go and Rust binaries by recovering runtime metadata, symbols, panic strings, and idiomatic decompilation patterns.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Rent, manage, and destroy GPU instances on vast.ai. Use when user says "rent gpu", "vast.ai", "rent a server", "cloud gpu", or needs on-demand GPU without owning hardware.

AI music generation with ACE-Step 1.5 — background music, vocal tracks, covers, stem extraction for video production. Use when generating music, soundtracks, jingles, or working with audio stems. Triggers include background music, soundtrack, jingle, music generation, stem extraction, cover, style transfer, or musical composition tasks.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Land and deploy workflow. (gstack)

NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-face...

Get Shit Done - Full project planning and execution workflow. Handles project initialization with deep context gathering, automated research, roadmap creation, phase planning, and execution with verification.

Stress-tests a plan against the project's domain model and documented decisions, sharpening terminology and updating DOMAIN.md and ADRs inline as decisions crystallize.

Coordinates multiple research agents to gather information on a topic across GitHub, HuggingFace, papers, competitors, and best practices, then compiles findings into a structured report.

Use the context-grooming skill to prune and compact the current project context.

Create landscape materials with layer blending, texture setup, and layer info objects (LandscapeMaterialService). Use when the user asks to build a landscape/terrain material, set up paint layers (grass/rock/dirt), create layer info objects, add a grass output, or assign a material to a landscape. For production master-material+RVT systems, load landscape-auto-material.

Generates comprehensive questions about decisions before implementing. Explores requirements, constraints, success criteria, edge cases, and hidden assumptions in a SINGLE comprehensive prompt. Use when starting any significant work to surface unknowns early.

Builds and validates DeepStream GStreamer pipelines through an interactive questionnaire and a BM25 retrieval engine over 270+ verified pipelines.

Toolkit de alta performance para análise de intervalos genômicos em Rust com bindings Python. Use ao trabalhar com regiões genômicas, arquivos BED, tracks de cobertura, detecção de sobreposições, tokenização para modelos de ML, ou análise de fragmentos em genômica computacional e aplicações de aprendizado de máquina.

Run a bounded cross-platform landscape scan before planning or implementation. Use when the real job is researching what exists, how people work around it, which solutions repeat, or how platform/tooling patterns map before deciding what to build. Produce reusable `.survey/{slug}/` artifacts, validate the artifact contract, and route planning or execution outward only after the survey is done.

Polish an existing React Native screen to feel intentional, native, and human-crafted. Use for "this screen looks off", "feels clunky on iOS", "Android version looks wrong", "jank when scrolling", "button is unreachable", or any RN-specific UX polish pass.

Audit-ready decision artifacts for LLM outputs — assumptions, risks, recommendation, and review gating (schema-valid JSON).