786 skills · sorted by relevance
Help configure optional local Whisper dictation into terminals and other apps, with language, hotkey and CPU or GPU checks.
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Applies Qt Canvas2D (QtCanvas2D / Qt Canvas Painter, Qt 6.12+) best practices when producing or working with Canvas2D QML source code. Use whenever Canvas2D, Canvas2DContext, path2d, boxshadow2d, boxgradient2d, gridpattern2d, conicalgradient2d or transform2d is the subject: writing, reviewing, fixing, optimizing, or porting Qt Quick Canvas / HTML5 canvas drawing code to Canvas2D. Also use for GPU-accelerated imperative 2D drawing in QML — gauges, dials, charts, waveforms, oscilloscopes, clocks, freehand drawing — including choosing between Canvas2D and Shape/ShapePath when the renderer has not been decided yet. Do NOT trigger for plain Qt Quick Canvas work that must stay on the old CPU element, or for reviewing existing Shape/ShapePath code where the renderer is already settled.
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CPU-only motion data processing pipeline for game animation: BVH import, contact detection, root decomposition, motion blending, FABRIK IK. No GPU required.
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Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU stats, log scrollback, checkpoint state, then states a hypothesis as a hypothesis and runs a smoke before claiming a root cause. Use when the user asks why a run is failing, diverging, OOMing, hanging, slow, producing weird metrics, has crashed, or asks to debug, diagnose, troubleshoot, or investigate a training issue.
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Pre-flight checklist for long-running ML training jobs covering config diff, run naming, path verification, monitoring setup, and restart-cleanup. Use when the user asks to launch, kick off, start, restart, or kill a training run, or mentions launching a multi-hour or multi-day GPU job (python train, accelerate launch, torchrun, deepspeed, sbatch, tmux training).
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Use when building a client-side browser exploit — V8/JSC JIT type confusion to renderer R/W, V8 heap-sandbox escape, renderer-to-browser sandbox escape (Mojo IPC, GPU/Dawn/ANGLE), Electron/webview IPC abuse, 1-click RCE chains
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Use when breaking out of a container or escalating inside Kubernetes — runc/BuildKit CVEs, privileged/capability/cgroup misconfig escapes, NVIDIA GPU toolkit escape, K8s RBAC abuse, kubelet RCE, ingress/admission-controller RCE, node-to-cluster pivot
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Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
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Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and embeddings. Design novel proteins, extract ML features, or fold sequences. Local GPU or EvolutionaryScale Forge API. Use AlphaFold for traditional folding; RDKit for small molecules.
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MaxQuant + Perseus proteomics pipeline: run MaxQuant for LFQ and SILAC; parse proteinGroups.txt in Python; filter contaminants/decoys; log2 + median-normalize; impute MNAR; t-test with FDR; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo-native processing; FragPipe/MSFragger for GPU-accelerated DB search.
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Databricks AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.
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Guides Researchers through Faiss installation, dense and binary similarity search, index training and composition, persistence and evaluation, and explicitly gated CPU/GPU interoperability workflows.
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Guide Det3D PyTorch 3D object-detection workflows across configuration, KITTI/nuScenes/Lyft data preparation, GPU training and evaluation, custom CUDA operations, and visualization.
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Use ColabFold for protein structure prediction workflows: validate FASTA/CSV/A3M inputs, generate MSAs, plan batch predictions, inspect outputs, and troubleshoot optional AlphaFold, MMseqs2, JAX, GPU, and OpenMM dependencies.
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Operate the Deep Researcher Agent repository for autonomous deep-learning experiments, execution backends, provider/tool dispatch, GPU resources, durable progress, and research-support integrations.
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Create distinctive, production-grade web frontend interfaces with high design quality. Use when the user explicitly asks to build or substantially redesign a web component, page, or application. Only applies when repository inspection confirms that the visual surface is a browser-rendered web UI. Do not use for native or GPU-rendered graphics, shaders, games, terminal UIs, or other non-web visual systems. Ignore incidental mentions and traces.
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Generate video clips on the user's own GPU through Guaardvark: text-to-video, image-to-video, first+last frame animation, clips with their own soundtrack and dialogue (MiniMax H3), short looping animations, and batch runs. Use when the user asks to make, render, animate, or batch-generate video locally.
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Generate or edit images on the user's own GPU through Guaardvark: single images, instruction edits, background cut-outs, inpaint and outpaint, consistent characters from the Cast Library, and batch runs of many prompts. Use when the user asks to create, draw, render, visualize, edit, or batch-generate images locally.
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Generate full songs with vocals or instrumentals (ACE-Step) and sound effects or ambience (Stable Audio Open) on the user's GPU through Guaardvark's Audio Foundry. Use when the user asks for a song, a beat, a jingle, background music, or a sound effect, made locally with no subscription.
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Turn a song into a beat-synced music video with Guaardvark's Director: song analysis, per-cut shot prompts, storyboard keyframes, image-to-video renders, cuts on the beat. Use when the user says "music video", "video for this track", or wants visuals timed to a song. Has an approval gate before GPU spend.
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Upscale images and video 2x to 4K/8K on the user's GPU through Guaardvark (Real-ESRGAN, HAT-L, SwinIR, two-pass). Use when the user asks to enlarge, sharpen, restore, or upscale a picture, a batch of pictures, or a video.
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Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.
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Set up the Graphsignal Profiler for inference workloads — vLLM, SGLang, PyTorch, and dstack services. Use when the user wants GPU profiling, tracing, or monitoring for inference, asks about `graphsignal-run` or `graphsignal.watch()`, or asks about CUPTI / Prometheus / OTLP setup.
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Cloudflare Workers AI for serverless GPU inference. Use for LLMs, text/image generation, embeddings, or encountering AI_ERROR, rate limits, token exceeded errors.
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