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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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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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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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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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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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帮助将机器学习实验整理为生产系统，明确数据规格、可复现训练、质量评估、部署、监控与回退，并形成实施计划和审查事项。
协助撰写机器学习与AI研究论文，涵盖文献检索、实验设计、运行进度检查、结果分析、写作和投稿准备，并核验引用、处理审稿后的修改。
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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在正式开发前，用一次性原型验证想法是否可行。协助整理疑问、调查方案、运行实验、比较方法，并记录结论、依据与限制，为下一步验证提供参考。
Turn interviews, support tickets, surveys, and product context into a single HTML workspace for decisions. Link findings to evidence, compare improvement options, show uncertainty, and outline experiments that help teams choose what to try next.
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Preview how clothes look on a person using virtual try-on models hosted by fal.ai. Use the workflow to explore clothing visuals for ecommerce, lookbooks, or experiments with different styling ideas.
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Plan an AI music album from its concept through lyric drafts, track order, and export. Intended for independent album experiments and brand soundtracks, with the full production workflow provided through a separately installed upstream bundle.
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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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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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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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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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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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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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Create and analyze AI model evaluation experiments in Arize using the ax CLI, a command-line tool. Collect real model outputs, export results, compare evaluation scores, and identify examples where a new model performs worse.
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Run repeated code improvement experiments with multiple agents. It plans and reviews candidate changes, compares benchmark results, merges candidates that pass rechecking, and records progress until a target, iteration limit, or another stopping condition is reached.
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Investigate why a PyTorch model behaves a certain way by changing its internal values with pyvene. The skill guides experiments that swap or remove activations, train interventions, compare outputs, and save or share intervention configurations.
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Use TensorBoard to visualize training loss and accuracy, compare experiments, and inspect model structure. Includes PyTorch and TensorFlow logging examples for examining weight distributions, predictions, and performance bottlenecks during machine learning training.
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