“experiment”的搜索结果

Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.

中文简介正在准备中。当前显示英语简介。

• Logging training metrics and viewing progress in real time
• Comparing experiments with different training settings
• Searching automatically for better training settings

Plan and analyze A/B tests, prepare tabular data, and train and evaluate prediction models. Estimate intervention effects from observational data, track model experiments with MLflow, and translate statistical findings into business decisions.

中文简介正在准备中。当前显示英语简介。

• Planning A/B test sample sizes and analyzing results
• Preparing tabular data for prediction models
• Comparing model performance and recording experiments

Plan low-effort experiments to check assumptions about an existing product before building a feature. Compare prototypes, A/B tests, and other methods, with a clear assumption, procedure, metric, and success threshold for each experiment.

中文简介正在准备中。当前显示英语简介。

• Checking feature assumptions before implementation
• Planning prototype usability tests
• Defining metrics and success thresholds for A/B tests

Evaluate whether completed experiments support a research claim using a separate Codex reviewer. Identify unsupported conclusions and missing evidence, then determine whether to revise the claim, run additional experiments, or move toward paper writing.

中文简介正在准备中。当前显示英语简介。

• Check research claims before drafting a paper
• Plan additional experiments for incomplete evidence
• Reconsider an approach after unsupported results

Plan ablation studies, experiments that remove or replace parts of a machine learning method to test their contribution. Define reviewer questions, expected outcomes, priorities, execution order, and compute estimates, then implement experiments and record findings.

中文简介正在准备中。当前显示英语简介。

• Testing the contribution of individual method components
• Planning additional experiments before paper submission
• Prioritizing experiments within a GPU budget

Check running experiments, collect logs and result metrics, and summarize progress alongside baseline comparisons. It supports SSH servers, Vast.ai, and Modal, with optional Weights & Biases metrics and cost reporting for the supported compute services.

中文简介正在准备中。当前显示英语简介。

• Check whether remote experiments have finished
• Compare collected metrics with the matching baseline
• Investigate NaN values or diverging training runs

Audit research experiments before using their results to support claims. An independent model checks reference data, metric calculations, result files, and evaluation scope, producing reports with evidence, findings, and specific actions to address concerns.

中文简介正在准备中。当前显示英语简介。

• Verify the source of reference data
• Check score normalization and metric calculations
• Match paper claims against saved results

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

中文简介正在准备中。当前显示英语简介。

• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

Coordinate AI research from literature review and hypothesis testing to findings and paper drafts. The skill alternates focused experiments with broader reflection, maintains project records, and directs specialized skills to carry out research tasks.

中文简介正在准备中。当前显示英语简介。

• Turning a research question into testable hypotheses
• Managing repeated experiments across multiple hypotheses
• Resuming research from recorded findings and project state

VS-Enhanced Quantitative Design Consultant with Materials & Sampling Enhanced VS 3-Phase process: Avoids obvious experimental designs, proposes context-optimal quantitative strategies Absorbed C4 (Experimental Materials Developer) and D1 (Sampling Strategy Advisor) capabilities Use when: selecting quantitative research design, planning experimental/survey methodology, power analysis, developing materials, sampling Triggers: RCT, quasi-experimental, experimental design, survey design, power analysis, sample size, factorial design, materials, stimuli, sampling strategy

中文简介正在准备中。当前显示原文说明。

Coordinate AI research from literature review to paper drafting. The skill alternates focused experiments with broader reviews of findings, tracks hypotheses and decisions, and uses specialist skills to carry out research tasks and prepare progress reports.

中文简介正在准备中。当前显示英语简介。

• Starting a research project from a question
• Testing and comparing multiple hypotheses
• Reassessing direction when experiments stall

Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.

中文简介正在准备中。当前显示原文说明。

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

中文简介正在准备中。当前显示原文说明。

Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

中文简介正在准备中。当前显示原文说明。

Systematic experimental results analysis workflow for ML/AI research papers. Connects experimental data to publication-ready Results sections with statistical validation, visualizations, and quality checks. Triggers on "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization".

中文简介正在准备中。当前显示原文说明。

Use when the user wants to design experiments, plan ablation studies, structure baselines, or create incremental evaluation strategies. Triggers on phrases like "design ablation", "plan experiment", "what experiments should I run", "baseline comparison", or "experiment matrix".

中文简介正在准备中。当前显示原文说明。

Audit existing experimentation infrastructure and past experiments for methodology issues. Use when asked to "audit our experiments", "is our experimentation sound", or "review past test methodology".

中文简介正在准备中。当前显示原文说明。

Use to run an autoresearch-style experiment loop — improve a measurable artifact by trying changes, measuring against one objective metric, keeping if better and reverting if not, on an isolated git branch with a logged trail. Trigger on "/experiment", "run an experiment loop", "autoresearch X", "tune/optimize X against a metric", "hill-climb X". Follows references/sops/experiment-loop.md. Enforces the autonomy gate (objective metric + reversible sandbox + no PHI/external) before any unsupervised looping.

中文简介正在准备中。当前显示原文说明。

Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.

中文简介正在准备中。当前显示原文说明。

Design product experiments to test hypotheses. Use when the user says "run an experiment", "A/B test this", "build-measure-learn", "validate this assumption", "test this hypothesis", "we don't know if this will work", "minimum viable test", "lean experiment", or wants to reduce risk before fully building a feature - even if they don't explicitly say "experiment". For sample sizes, power calculations and significance testing, use ab-test-design instead.

中文简介正在准备中。当前显示原文说明。

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

中文简介正在准备中。当前显示原文说明。

Configures the rollout shape of a PostHog experiment — the variant split (50/50, 80/20, A/B/C ratios), the overall rollout percentage that gates how many users enter the experiment, and the disambiguation when a percentage like "roll out to 25%" could mean either. Use when the user mentions a rollout percentage, variant split, or traffic distribution; gives a ratio like 60/40, 70/30, or 80/20; asks "who sees the test variant?"; wants to increase, decrease, or change the rollout or split on a draft or running experiment; weighs equal vs uneven splits; or proposes a mid-experiment split change (often an anti-pattern that needs reset or end-and-restart).

中文简介正在准备中。当前显示原文说明。

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

中文简介正在准备中。当前显示原文说明。

Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

中文简介正在准备中。当前显示原文说明。