VectorSpaceLab/AREX-Skill

dynamic-resources

Example skill loaded from resources_discover

Quellsprache: Englisch

create-paper-skills

Creates reusable Agent Skills from an AI research paper using the Paper2Skills Distiller workflow. Use in Creator mode when the user provides a paper PDF, paper URL, arXiv id, paper title, or paper/repo pair, or asks to convert a scientific paper into skills…

Quellsprache: Englisch

create-repo-skill

Creates a repo-specific operating Agent Skill for DisCo Researcher from a local repository by inspecting source files and an installed or auto-prepared, backend-aware Python package environment. Use when the user asks to create a skill for a repo, generate…

Quellsprache: Englisch

extend-repo-skill

Extends an existing repository-specific Agent Skill with new capabilities, deeper coverage, troubleshooting, scripts, and usability tests. Use when the user asks to expand, improve, deepen, or add coverage to an already implemented skill instead of creating a…

Quellsprache: Englisch

import-repo-skills-to-agent

Use this skill when the user asks to export DisCo's managed repository skills and repo-skills-router into another agent tool such as Codex under ~/.agents/skills, Claude Code under ~/.claude/skills, or a project-local agent directory. Handles canonical source…

Quellsprache: Englisch

refresh-repo-skill

Refreshes an existing repository-specific Agent Skill after the source repository changed. Use when the user says repo code, APIs, docs, examples, configs, dependencies, or behavior changed and an old skill may now be stale, outdated, inconsistent with…

Quellsprache: Englisch

repo-skills-router

Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality,…

Quellsprache: Englisch

verify-repo-skill

Verifies a generated or refreshed repo-specific Agent Skill by creating assertion-backed usability test cases, running content-level self-refine, checking backend-classified native repo examples/tests against the prepared CPU/GPU environment plan, enforcing…

Quellsprache: Englisch

3d-resnets-pytorch

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

Quellsprache: Englisch

data-preparation

Prepare raw videos and annotations for the 3D-ResNets-PyTorch dataset loaders.

Quellsprache: Englisch

training-and-inference

Routes training, fine-tuning, validation, checkpoint, and inference workflows for 3D ResNets PyTorch.

Quellsprache: Englisch

3ddfa

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

Quellsprache: Englisch

cpp-onnx-port

Operate the optional 3DDFA C++ OpenCV DNN port, checkpoint-to-ONNX export, weight placement, build/run expectations, and C++ troubleshooting.

Quellsprache: Englisch

geometry-rendering

Reconstruct 3DMM vertices, serialize mesh outputs, and manage 3DDFA rendering helpers.

Quellsprache: Englisch

python-inference

Operate 3DDFA Python image and video inference for landmarks, dense vertices, meshes, pose boxes, depth, PNCC, and PAF outputs.

Quellsprache: Englisch

training-evaluation

Routes 3DDFA training recipes, loss selection, checkpoint resume, dataset layout, and benchmark evaluation.

Quellsprache: Englisch

3ddfa-v2

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

Quellsprache: Englisch

onnx-and-benchmarking

Use 3DDFA_V2 ONNX Runtime acceleration and CPU latency or speed benchmark workflows.

Quellsprache: Englisch

setup-and-assets

Prepare 3DDFA_V2 runtime assets and native extension builds before demos or benchmarks.

Quellsprache: Englisch

still-image-demo

Run 3DDFA_V2 still-image alignment, rendering, pose, texture, and mesh export workflows.

Quellsprache: Englisch

video-and-tracking

Run 3DDFA_V2 video, smoothing, and manual webcam tracking workflows.

Quellsprache: Englisch

ab3dmot

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

Quellsprache: Englisch

data-conversion

Routes KITTI and nuScenes data-layout, detection-conversion, and schema-validation work for AB3DMOT inputs.

Quellsprache: Englisch

evaluation-visualization

Evaluate, threshold, combine, and visualize AB3DMOT KITTI and nuScenes tracking results.

Quellsprache: Englisch

tracking-pipeline

Run AB3DMOT tracking safely and use the core AB3DMOT tracker APIs for KITTI and nuScenes 3D MOT workflows.

Quellsprache: Englisch

accelerate

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

Quellsprache: Englisch

big-model-inference

Use Accelerate big-model inference utilities for meta initialization, device-map planning, checkpoint dispatch, CPU/disk offload, hooks, pipeline inference, and memory sizing without triggering downloads or heavyweight runs.

Quellsprache: Englisch

checkpointing-and-tracking

Save and resume Accelerate training state, register checkpoint hooks and custom state, log safely across processes, use experiment trackers, profile runs, and clean up memory.

Quellsprache: Englisch

configuration-and-cli

Use this sub-skill when working with Hugging Face Accelerate configuration files and CLI commands, including accelerate config/default/update/env/launch/test/estimate-memory/merge-weights/to-fsdp2, launch command construction, multi-node and SLURM planning,…

Quellsprache: Englisch

distributed-training-backends

Select, configure, and diagnose Accelerate distributed training backends including DeepSpeed, FSDP/FSDP2, Megatron-LM, torch native parallelism, TPU/XLA, FP8, quantization, compilation, Local SGD, and DDP communication hooks.

Quellsprache: Englisch

training-loop-integration

Migrate raw PyTorch training and evaluation loops to Hugging Face Accelerate using Accelerator, prepare(), backward(), gradient accumulation, dataloader behavior, gather/reduce, mixed precision, DDP kwargs, local SGD, communication hooks, and basic…

Quellsprache: Englisch

acme

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

Quellsprache: Englisch

core-workflows

Build and debug Acme core dm_env loops, specs, wrappers, logging, counting, observers, and simple custom Actor/Learner components.

Quellsprache: Englisch

jax-agents

Select, configure, and adapt Acme JAX agents and JAX experiment workflows.

Quellsprache: Englisch

replay-and-data

Use Acme adders, Reverb replay tables and datasets, offline data iterators, image augmentation, and replay shape troubleshooting.

Quellsprache: Englisch

tf-agents

Select, configure, debug, and adapt Acme TensorFlow/Sonnet agents, networks, savers, Launchpad examples, and TF losses.

Quellsprache: Englisch

act-plus-plus

Routes ACT++, ACT, Diffusion Policy, VINN, and MuJoCo simulation workflows for bimanual ALOHA episode data and imitation-learning tasks.

Quellsprache: Englisch

policy-training

Routes ACT, CNNMLP, Diffusion Policy, and latent-model training or evaluation workflows for ACT++ checkpoints and datasets.

Quellsprache: Englisch

simulation-data

Routes simulated ALOHA episode generation, replay, visualization, mirroring, compression, and truncation workflows for ACT++ HDF5 data.

Quellsprache: Englisch

vinn-offline

Routes VINN feature caching and non-interactive k-selection workflows for ACT++ BYOL/ResNet episode features.

Quellsprache: Englisch