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Guide alphafold2-pytorch protein sequence, MSA, distogram, angle-logit, coordinate-refinement, recycling, embedding, and structure-utility workflows with version-aware troubleshooting.

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Route AlphaFold protein structure prediction setup, data preparation, local prediction, model configuration, output interpretation, and relaxation workflows.

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Operate the Align-Anything multimodal alignment package, including training, serving, remote reward models, and satellite projects.

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

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

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

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

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Route AI-Optimizer reinforcement-learning collection tasks across model-based RL, easy-MARL, offline RL, safe command builders, and repository limitations.

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Operate the Agriculture_KnowledgeGraph agricultural Neo4j graph, Django demo, entity labeling, crawler, and relation-extraction workflows.

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Guide agents through AgiBot X1 humanoid reinforcement-learning training, checkpoint playback, policy export, and MuJoCo sim2sim workflows with verified configuration contracts and explicit Isaac Gym limits.

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AgentScope repo skill for building agents, provider connectors, RAG and memory workflows, service deployments, and local or sandboxed workspaces.

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Use this repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and troubleshooting optional backends.

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Use Google ADK Python to build agents, Workflow graphs, tools, runtime services, CLI apps, evaluations, deployments, and ADK repository changes.

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Routes AdelaiDet users through legacy-compatible setup, model config selection, training/evaluation, demos, text spotting, dataset preparation, and export/conversion workflows.

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

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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.

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

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

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

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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, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.

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Router for Alpamayo R1 multimodal driving inference and trajectory sampling.

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Use Argilla 2.x for human-feedback dataset annotation, Python SDK dataset workflows, server deployment/operations, webhooks, and legacy v1/Rubrix migration.

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Routes Alibi users to the right explanation, confidence, prototype, or optional-backend workflow and points them to the bundled helpers needed to run it.

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

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