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概要と使いどころ
Use to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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Generates full consulting proposals from a brief. Input client name, problem description, and rough scope. Outputs proposal.md with executive summary, problem statement, proposed approach, timeline, team, pricing tiers, and terms. Researches client company for personalization. Multiple pricing models. Professional formatting matching consulting standards.
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。
Quota-triggered failover from Claude Code, Codex, Cursor, Hermes or OpenClaw. When armed, it reads the sender quota and tries other-vendor models in place, then receiving agents in the configured order. Use when a long run must finish even if one vendor's quota runs out. Not a monitor for anything else.
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。
Create production-quality Android applications following Google's official architecture guidance and NowInAndroid best practices. Use when building Android apps with Kotlin, Jetpack Compose, MVVM architecture, Hilt dependency injection, Room database, or multi-module projects. Triggers on requests to create Android projects, screens, ViewModels, repositories, feature modules, or when asked about Android architecture patterns.
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。
Verifies math-heavy code for algorithmic correctness and numerical stability. Use when reviewing scientific algorithms, ML models, or numerical code.
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。
Delegates tasks to Gemini CLI implementing delegation-core for Google's models. Use when delegation-core selects Gemini or 1M+ token context is needed.
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。
Delegates tasks to Qwen CLI via delegation-core for Alibaba's models. Use when delegation-core selects Qwen or large-context batch processing is needed.
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。
Models a business in its own language. Use when the domain has real business rules to capture.
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。
Assess whether to escalate models. Use when evaluating reasoning depth.
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 data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.
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 designing or auditing how an LLM or multimodal LLM is evaluated on a clinical task (report generation, VQA, text extraction). Covers reference standard, clinical-efficacy metrics beyond BLEU/ROUGE, hallucination, contamination and prompt sensitivity. Imaging models are /model-assessment.
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。
Refactor and review SwiftUI view files for consistent structure, dependency injection, and Observation usage. Use when asked to clean up a SwiftUI view’s layout/ordering, handle view models safely (non-optional when possible), or standardize how dependencies and @Observable state are initialized and passed.
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 a pipeline DOT file has passed structural validation but may still have flow inefficiencies, spec fidelity gaps, or information loss in prompts. Use after dotfile-audit or dotfile-from-spec when you want to deeply verify a pipeline captures everything from the original spec. Use when prompts might be missing critical domain rules, data models, or invariants.
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 ExecuTorch to export PyTorch models to edge runtime artifacts, build host/device runtimes, choose delegates, profile/debug execution, and operate specialized Qualcomm, Cortex-M, LLM, and binary-size 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 Flash Linear Attention package workflows: setup, kernels, layers/models, KDA/context parallel, and benchmarking.
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。
Repository operating skill for using Flow Forecast's flood_forecast package for time-series forecasting, data preparation, training, inference, evaluation, and hydrology-oriented advanced models.
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 fastMRI for MRI reconstruction data loading, MRI operators, reconstruction models, Lightning training, evaluation, and submission preparation.
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。
Foolbox operating guidance for wrapping vision models, running adversarial attacks, measuring clean and robust accuracy, handling NumPy/PyTorch/TensorFlow/JAX backends, and loading Foolbox-compatible model-zoo repositories.
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 DGL-LifeSci and dgllife for molecular graph learning, molecule featurization, datasets, property prediction, reaction prediction, binding affinity, model-zoo/pretrained models, and molecular generation 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。
Use DeepMind dm_control for MuJoCo simulation, Control Suite environments, PyMJCF models, Composer tasks, rendering, and high-level locomotion/manipulation 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 DLTK 0.2.1 medical-imaging workflows across legacy TensorFlow 1.x data, models, Estimators, and deployment utilities.
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。
Guides CUDA-accelerated cuRobo v2 robotics workflows for robot models, differentiable kinematics, collision-aware IK and motion planning, trajectory optimization, MPC, perception mapping, and motion retargeting.
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 DeepStream-Yolo deployment, model conversion, multi-GIE, and INT8 workflows for supported YOLO-family models.
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 DataSciencePython tutorial/example collection through modern self-contained helpers for Python data-science resources, statsmodels logistic regression, scikit-learn Kaggle-style tabular classifiers, and Twitter JSONL extraction.
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。