VectorSpaceLab/AREX-Skill - Page 4

tracking-sdk

Use Aim's Python SDK to create local repositories and runs, track metrics/params/media/artifacts/logs, query sequences, and validate SDK instrumentation safely.

aimet

Route AIMET install, PyTorch/ONNX quantization, GenAILab, model access, cluster/Pod, Qualcomm SDK, optimization, export, and repository tasks to focused operating guidance.

cluster-pod-workflows

Operate AIMET cluster and Pod workflows for GPU development, source builds, GenAILab runs, sync, exec, and cleanup.

genai-lab

Run and configure AIMET GenAILab LLM/VLM quantization scorecards with Torch or ONNX backends, exports, caches, and summaries.

install-and-build

Install, build, repair, and validate AIMET package environments and repository build/test workflows.

model-access-and-credentialed-evaluation

Handle AIMET model downloads, Hugging Face tokens, GitHub Actions scorecard runs, AWS/S3 checkpoints, caches, and benchmark-result comparability.

onnx-quantization

Use AIMET ONNX for QuantizationSimModel calibration, provider selection, encodings, QDQ export, graph passes, and ONNX PTQ utilities.

optimization-analysis-deployment

Use AIMET analysis, mixed precision, compression, debugging, export validation, and deployment artifact workflows after core quantization setup.

qualcomm-sdk-deployment

Prepare AIMET exports for Qualcomm AI Hub, QAIRT/QNN conversion, profiling, inference, and SDK command generation.

torch-quantization

Use AIMET Torch for model preparation, QuantizationSimModel calibration, PTQ/QAT, encodings, and Torch export workflows.

airweave

Route Airweave tasks to the most specific sub-skill: local-development, backend-api, source-connectors, frontend-dashboard, connect-widget, mcp-search, or monke-e2e.

backend-api

Operates Airweave's backend API surface for search tiers, collections, source connections, Connect sessions, browse-tree selection, sources, webhooks, usage checks, source rate limits, and streaming search debugging.

connect-widget

Operates the embeddable Connect widget iframe, client-side session UX, parent-child postMessage, OAuth popup recovery, theme updates, connection creation, folder selection, and sync progress handling.

frontend-dashboard

Operates Airweave's React dashboard: auth, organization context, API client behavior, collections, search UI, billing/admin pages, webhooks, auth providers, usage gates, and cron utilities.

local-development

Operate the Airweave local Docker stack safely: start, reuse, restart, recreate, destroy, seed environment, check ports, and recover health failures.

mcp-search

Operate the Airweave MCP search server in stdio and Streamable HTTP modes, including tool registration, collection and organization resolution, auth modes, Prometheus metrics, deployment knobs, and search-tool behavior.

monke-e2e

Operate Airweave's Monke connector E2E framework safely: discover connectors, understand runner flow, configs, auth, bongos, generated test data, and credentialed-test boundaries.

source-connectors

Implement, validate, and troubleshoot Airweave source connectors: decorators, registry metadata, auth/config schemas, browse trees, ACLs, federated search, incremental sync, and source rate limits.

aix360

Routes AIX360 explainability tasks across local black-box attribution, counterfactuals and certification, interpretable models, time-series explanations, datasets, and explanation-quality metrics.

counterfactual-and-certification

Use AIX360 for contrastive CEM/CEM-MAF explanations, black-box certification, GLANCE recourse, and order-constrained optimal-transport matching.

datasets-and-metrics

Guides AIX360 dataset acquisition, local-path validation, preprocessing contracts, explanation-quality metrics, and offline troubleshooting for HELOC, COMPAS, CDC, MEPS, Ford, Sunspots, CIFAR, MNIST, CelebA, eSNLI, and related datasets.

interpretable-models

Choose and operate AIX360's directly interpretable models, rule learners, prototypes, model differencing, teaching explanations, and optional neural interpretable methods.

local-black-box

Guide local post-hoc explanations for black-box predictors on tabular, text, image, and generic structured inputs with AIX360 0.3.0 wrappers, output checks, and local metrics.

time-series

Use AIX360 TSICE, TSLime, and TSSaliency for local numeric explanations of univariate or multivariate time-series models, including temporal perturbations, relevant history, exogenous inputs, and integrated-gradient saliency.

aizynthfinder

Use AiZynthFinder for retrosynthetic planning, configuration, route analysis, custom extensions, and focused development workflows.

configuration-and-data

Create, validate, and troubleshoot AiZynthFinder configuration files, stocks, model assets, public-data setup commands, and data-tool commands.

extension-and-development

Extend AiZynthFinder with custom hooks, stocks, scorers, policies, plugins, search algorithms, and focused development tests.

planning-workflows

Run AiZynthFinder retrosynthesis planning and one-step expansion through CLI, Python APIs, and optional notebook interfaces.

route-analysis

Inspect AiZynthFinder output files, route collections, reaction trees, route scores, clustering, images, and downstream summaries without rerunning a search.

alae

Use the ALAE repository for Adversarial Latent Autoencoder data preparation, CUDA training, checkpoint-backed generation, latent editing, and legacy metrics.

data-preparation

Routes ALAE dataset preparation, TFRecord layout validation, sample-image setup, style-mixing image layout, and face-alignment workflows.

generation

Use ALAE checkpoints for demo, image generation, reconstructions, style mixing, traversals, and principal-direction management.

metrics

Prepare and route safe ALAE FID, reconstruction FID, PPL, and LPIPS metric workflows.

training

Train ALAE and StyleALAE with configs, TFRecords, DDP, and checkpoints.

albumentations

Build, debug, serialize, and integrate Albumentations 2.x augmentation pipelines for images, masks, bboxes, keypoints, volumes, and PyTorch-style datasets.

framework-integration

Integrate Albumentations with PyTorch-style datasets, tensor conversion, optional extras, and DataLoader reproducibility.

pipeline-composition

Build, edit, validate, seed, and debug Albumentations augmentation pipelines with Compose and composition combinators.

serialization-and-reproducibility

Save, load, replay, inspect, and reproduce Albumentations augmentation pipelines with JSON/YAML serialization, ReplayCompose, applied-parameter tracking, custom Lambda mappings, deterministic seeds, and Hub caveats.

targets-and-formats

Apply Albumentations pipelines safely to images, masks, bboxes, keypoints, multiple images, volumes, 3D masks, and labels without corrupting geometry or data formats.

transform-catalog

Select and parameterize Albumentations 2D transform families, including pixel, crop, resize, geometric, dropout, mixing/domain adaptation, spectrogram, text, target support, interpolation, and fill behavior.