97건 · 관련도순
Use MiniMax to review research, address weaknesses through code changes, experiments, or analysis, and request another assessment. The workflow runs up to four rounds and preserves reviewer responses, actions, results, and unresolved blockers.
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Build a paid advertising plan around business goals and financial constraints. Define channels, campaigns, audiences, budgets, creative, tracking, and experiments, then organize the work into phases with owners, success measures, and conditions for stopping or reversing changes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Audit paid-ad landing pages for message consistency, mobile usability, accessibility, performance, forms, consent, tracking, and destination safety. Get evidence-based findings and recommendations ready for experiments to address obstacles to completing the intended action.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze product usage in PostHog and manage experiments, dashboards, surveys, and feature flags, which control feature availability. Use connected tools to investigate user behavior and errors, run queries, and summarize findings with numbers and links.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform
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Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
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Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
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Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
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Data Version Control for ML projects. Track large datasets and models alongside Git, build reproducible ML pipelines, and run experiments with metric comparison. Works with any storage backend including S3, GCS, Azure, and local filesystems.
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Use when 王建硕 wants to systematically grow his X (Twitter) followers by running numbered, A/B-testable growth experiments and tracking which ones actually work. Every action gets a number, a hypothesis, a target metric, a before-state (for rollback), and a verdict. The North-Star metric is the new-follower ÷ profile-visit ratio (conversion) — every profile change is judged against it. Daily it ingests the X Analytics CSV export, scores each running experiment, and recommends keep / rollback. Triggers — "涨粉", "增加 X 粉丝", "X 涨粉实验", "follower growth", "A/B test my profile", "今天的涨粉检查", "/wjs-x-increasing-follower".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a change trains a model, reports a metric, or touches seeds, data or model versions, the lockfile, MLflow or DVC — what every run must log, seeding, environment pinning, data versioning and keeping tests off the shared tracking server
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When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," or "hypothesis." For tracking implementation, see data-and-funnel-analytics.
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Autonomous fixed-budget ML experiment loop — setup, iterative hypothesis testing, git-based keep/discard tracking, and indefinite autonomous execution. Implements the karpathy/autoresearch protocol.
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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.
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Use when implementing analytics with PostHog in a TypeScript app — capturing events, identifying users, adding feature flags, tracking errors, or wiring API lifecycle telemetry. Triggers on edits to analytics integration files, or mentions of "PostHog", "analytics", "feature flag", "event tracking", "capture", "identify", "A/B test", or "experiment".
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Quando o usuário quer planejar, projetar ou implementar um teste A/B ou experimento. Também use quando o usuário menciona "teste A/B", "split test", "experimento", "testar essa mudança", "copy variante", "teste multivariado" ou "hipótese". Para implementação de rastreamento, veja analytics-tracking.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Visualize métricas de treinamento, depure modelos com histogramas, compare experimentos, visualize grafos de modelos e perfil de desempenho com TensorBoard - kit de visualização de ML do Google
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Rastreie experimentos de ML com logging automático, visualize treinamento em tempo real, otimize hiperparâmetros com sweeps e gerencie registro de modelos com W&B - plataforma colaborativa de MLOps
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Iteratively improve research by having Claude Code review code, results, and drafts while Codex implements fixes. Repeat until the assessment meets the stopping criteria or the round limit is reached, preserving feedback and unresolved issues.
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Triage incorrect MNN outputs and regressions by matching symptoms to focused debugging guides. Use backend comparisons, minimal reproductions, and controlled experiments to investigate memory issues, quantization, concurrency, numeric limits, caches, and inconsistent results across runs.
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Scans code for security vulnerabilities with CodeQL by tracing data across functions. Builds and checks analysis databases, models project-specific APIs, and saves findings in SARIF, a structured results format, with broad or focused scan options.
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Designs production-grade ML pipeline infrastructure with experiment tracking, Kubeflow/Airflow DAGs, feature store schemas, model registries, and automated retraining workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.