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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.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。