587건 · 관련도순
Design experiments and quasi-experiments before analysis. Use when choosing study design, treatment/control structure, outcomes, assumptions, validation plans after scientific experiment failure, or which of DiD, ITS, synthetic control, or regression discontinuity fits the research question. For fitting models or estimating effects on existing data, use performing-causal-analysis instead.
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Plans, builds, runs, and analyzes fault injection experiments with AWS Fault Injection Service (AWS FIS) to validate application resilience through chaos engineering. Covers experiment templates (actions, targets, stop conditions), the FIS actions catalog and action selection, the scenario library (AZ power interruption, cross-Region connectivity, EC2/EKS/EBS stress), experiment lifecycle and monitoring, logging and reports, multi-account experiments, the experiment IAM role, and blast-radius safety. Applies when a user mentions AWS FIS or fault injection, asks what experiment to run for a failure mode (AZ, Region, API errors/throttling, instance/pod/DB/cache failure, latency, packet loss), wants to author an experiment template or CLI/CloudFormation, or needs to safely run chaos experiments in pre-production or production. For the broader resilience program across Resilience Hub and ARC, see aws-resilience-lifecycle; for ARC routing controls and zonal shift, see recovery-controller-setup.
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Use when designing or auditing AISTATS experiments, simulations, baselines, statistical tests, uncertainty estimates, ablations, random seeds, hyperparameters, compute, dataset handling, and claim-to-evidence fit, with emphasis on experiments that validate theorems rather than chase leaderboards.
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Use when designing or auditing AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard.
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Create or review brainstorm experiments new deliverables with traceable assumptions, decision criteria, ownership, and risk controls. Use when the user asks for brainstorm experiments new planning or analysis.
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Guide for multi-experiment batch operations: QC screening, batch download, comparison, and report generation across many ENCODE experiments simultaneously. Use when users need to process 5+ experiments together, create experiment comparison tables, perform batch quality checks, or generate summary reports. Trigger on: batch analysis, multiple experiments, bulk processing, experiment comparison, batch QC, multi-sample, batch download, experiment table, summary report, collection analysis.
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Search, query, and cross-reference NCBI GEO (Gene Expression Omnibus) datasets with ENCODE experiments. Use when the user wants to find GEO accessions for ENCODE experiments, search GEO for complementary datasets, download GEO metadata or series matrices, cross-reference ENCODE and GEO data, find supplementary files from GEO, or link GEO series to ENCODE experiments for provenance tracking. Also use when the user mentions GEO, GSE, GSM, GPL, GDS, series matrix, SOFT format, or needs to find expression data in GEO that complements their ENCODE analysis.
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Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, normalization, integration strategy selection, and provenance documentation.
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Audit PostHog experiments and feature flags for configuration issues, staleness, and best-practice violations. Read when the user asks to audit, health-check, or review experiments or feature flags, check flag hygiene, or verify experiment setup.
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머신러닝 모델의 운영 적용을 위해 데이터 규격, 재현 가능한 학습, 품질 평가, 배포, 모니터링과 롤백을 정리하고 구현 계획과 검토 항목을 만듭니다.
기계 학습·AI 연구 논문을 문헌 조사부터 실험 설계, 결과 분석, 집필, 투고 준비까지 지원합니다. 인용 검증과 심사 의견에 따른 수정도 다룹니다.
Track machine learning experiments in W&B, record training settings and metrics, and compare results in dashboards. Automate searches for training settings and keep version histories for models and datasets.
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본격적인 개발 전에 일회성 시제품으로 아이디어의 실현 가능성을 검증합니다. 질문 정리부터 조사와 실행 실험, 방식 비교, 결과와 제약 기록까지 진행합니다.
인터뷰, 문의, 설문을 정리해 제품과 디자인 결정에 쓸 HTML 화면을 만듭니다. 근거, 주제, 확신 수준, 개선안 비교와 다음 실험을 함께 제시합니다.
fal.ai의 가상 피팅 모델로 옷을 입은 사람의 모습을 이미지로 확인합니다. 전자상거래용 착용 이미지, 룩북과 스타일링 아이디어를 검토할 때 활용합니다.
AI 음악 앨범의 콘셉트 구상부터 가사 초안, 곡 순서 검토, 내보내기까지 다룹니다. 독립 제작 실험과 브랜드 음악 기획에 활용합니다.
Train and run Uni-Mol Tools models to predict molecular properties from CSV data. Organize experiments into projects, compare model performance, rank results, and manage storage through interactive commands or machine-readable JSON output.
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Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.
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Simulate concentration changes over time from biochemical models in SBML or Antimony, and compare fixed parameter conditions. Checks model units and exports CSV results plus COMBINE archives containing models and experiment settings, then replays them to compare results.
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Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.
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Search scientific papers by topic or DOI, a paper’s identifier, and organize evidence extracted from full text. Connect claims with experiments, reported statistics, limitations, and source locations for literature reviews and evidence tables.
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Build and simulate quantum circuits in Python with Cirq. Compare noise effects, test parameter values, transform circuits for hardware constraints, and prepare experiments for Google Quantum AI or supported providers when the required access is available.
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Investigate LLM application failures with Phoenix CLI by reviewing execution records, recording observations, and grouping mistakes into counted categories. Inspect datasets and experiments to identify which failure types deserve evaluation tests first.
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Plan a new product launch around finding early customers, guiding their first use, and running short experiments. The skill also helps diagnose stalled adoption, document repeatable outreach methods, and assess whether customer demand supports further expansion.
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