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概要と使いどころ
Guides agents using the datamol Python package for RDKit-first molecular IO, preparation, fingerprints, similarity, structure generation, visualization, and utility 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。
DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving.
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 Deepchecks package tasks for ML data/model validation across tabular, NLP, vision, result export, and integration 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 Darkflow object-detection, training, and export workflows for the legacy YOLO package.
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 the city2graph 1.0.0 Python package to construct, validate, transform, and troubleshoot geospatial, spatial-topology, morphology, mobility, transportation, heterogeneous, and GNN-ready graphs.
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 EEG, ECoG, MEG, and related electrophysiology deep-learning workflows through the braindecode Python package, including dataset construction, preprocessing, model training, augmentation, and interpretation.
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 Cirq to build, simulate, transform, serialize, validate, and provider-package quantum circuits.
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 AutoGluon repo tasks across tabular ML, time-series forecasting, multimodal AutoML, package setup, diagnostics, and saved predictor troubleshooting.
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 Augmentor for Pillow-based image augmentation pipelines, operation selection, mask-safe augmentation, array/generator workflows, and package-specific troubleshooting.
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 Boltz package tasks for biomolecular structure and affinity prediction, data preparation, training, and evaluation 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 BayesianOptimization package tasks for black-box Bayesian optimization, HPO, acquisition functions, constraints, typed domains, domain reduction, and checkout maintenance.
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。
CPU-only operating guidance for the BEHAVIOR-1K BDDL 3.7.0 package: symbolic activity definitions, condition parsing/evaluation, object taxonomy, and generated-data-backed knowledge-base inspection.
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 skill for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection. Covers the local AlphaFold 3 inference package, its JSON dialect, run_alphafold workflow, outputs, troubleshooting, and safe helper scripts.
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 AnyLabeling desktop annotation, auto-labeling model, dataset export, install, packaging, and release workflows for the PyQt6/ONNX image-labeling package.
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。
Operate the ai-data-science-team package for AI-assisted data loading, EDA, pandas transformations, SQL analysis, H2O/MLflow modeling, multi-agent teams, and Streamlit app 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 Argos Translate workflows for offline machine translation, package installation, CLI usage, runtime settings, and troubleshooting.
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。
Drive responsible disclosure of a proven finding to a CVE. Package the report, find the vendor contact, report privately, coordinate a timeline, request the CVE (vendor CNA / GitHub / MITRE), and publish an advisory. Closes the research loop. Triggers - "disclose", "request a cve", "report this to the vendor".
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。
Help the user decide what to charge and how to package it: the value metric, the tiers, the free-to-paid line, and finding the actual number. Use when the user is guessing at a price, priced too cheap and can't change it, is stuck on free vs paid, or believes "developers won't pay" so never charges.
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。
Evaluate Agent Skill design quality with an opinionated, practice-derived rubric informed by public specifications and examples. Use when reviewing, auditing, comparing, or improving SKILL.md files and skill packages. Produces evidence-backed findings, optional multi-dimensional scoring, and prioritized changes; treat the result as diagnostic guidance, not official certification or a universal standard.
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 when the project follows hexagonal (ports & adapters) architecture. Prevents domain code from depending on Spring or JPA. Use when you see packages like domain/, application/, infrastructure/, or adapters/ in the project structure.
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。
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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。