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概要と使いどころ
Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordina
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TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data.
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PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters.
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Harmonize data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of st
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Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con
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R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package.
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Framework for running single-variable cold email experiments. Defines experiment types (list-only, copy-only, combined), confidence weighting, minimum sample sizes, and success criteria. Use when the user wants to improve a campaign, test a new list vs old one, or compare copy variants. Prevents the
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Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".
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N개 brand experiment를 한 화면에서 비교하는 gallery index.html을 생성. 각 카드는 brand name, wow rating, multi-turn refinement deltas, iframe scaled preview, standalone link 포함. '결과물 한 번에 보여줘', '갤러리 만들어', '5개 비교 뷰', 'experiment 결과 정리' 류 트리거. omd:harness가 N개 brand batch 작업 끝낸 직후 자동 호출되거나 사용자가 명시적으로 호출.
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Autonomous experiment loop: hypothesize > modify > test > evaluate > keep/discard > repeat. Run N experiments automatically with measurable metrics. Works for performance optimization, A/B testing, prompt engineering, and any measurable improvement task.
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Designs, sanity-checks and reads out product experiments (A/B tests, holdouts, pre/post, geo and incrementality tests) and produces an experiment brief plus a readout with a ship / hold / replicate call. Use when you ask 'should we A/B test this', 'how long should the test run', 'is this result real', 'sample size', 'stat sig', 'SRM', 'holdout', 'our win rate is low', or need to grade an existing test plan or readout. Draws on 191 insights from 71 Lenny's Podcast guests, led by Ronny Kohavi, Ramesh Johari and Archie Abrams.
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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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Think and work like an expert Experimental Physicist. Use when a task calls for Experimental Physicist judgment. Reasons from GUM error budgets, traceable calibration chains, and multiplied signal-chain transfer functions — separating Type A and Type B uncertainty, null runs, and ELN-linked reproducibility before precision or discovery claims.
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Apply experimental design in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses experimental design or its strengths fit the task.
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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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Designs experiments to minimize and account for batch effects using balanced layouts and blocking strategies. Use when planning multi-batch experiments, assigning samples to sequencing lanes, or designing studies where technical variation could confound biological signals.
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Expert guidance for Comet ML, the platform for tracking machine learning experiments, managing models, and monitoring production ML systems. Helps developers log experiments, compare model versions, and build reproducible ML pipelines with automatic code/data versioning.
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ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.x.
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Braintrust is a hosted platform for evaluating and tracing LLM applications: it runs a task over a dataset, scores the outputs, stores each run as an experiment and logs production traces. Use when a user asks to write or run a Braintrust eval, score LLM outputs with autoevals, compare prompt or model versions as experiments, run evals in CI and fail on regressions, trace OpenAI or Anthropic calls with Braintrust, manage evaluation datasets, or use the bt CLI.
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Plans a controlled experiment (A/B test) so its result can be trusted: writes the hypothesis, picks one primary metric and the guardrails, computes sample size and run time, specifies how visitors are assigned and when exposure is logged, and reads out the result with a confidence interval. Use when someone says "set up an A/B test", "split test this page", "how many visitors do I need", "how long should the experiment run", "is this result significant", "can I stop the test early", or wants to test a headline, price, layout or onboarding change against the current version.
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Formal Design of Experiments (DOE) methodology for maximizing information from experiments while minimizing resources. Covers factorial designs, blocking, randomization, and optimal design strategies. Use when ", " mentioned.
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A/B and multivariate testing methodology. Design experiments, calculate sample sizes, determine statistical significance, avoid common pitfalls, and interpret results. Platform-agnostic framework applicable to landing pages, emails, ads, pricing, and product features. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, or conversion experiments.
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Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".
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Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code line-by-line for fake/derived ground truth, score self-normalization, phantom results (a paper number with no backing file/key), dead/uncalled metric code, verified-scope inflation, method-described ≠ method-evaluated drift, synthesized-looking results, placeholder/fake data still wired into a released result, code-output ≠ reported-number mismatch, and missing reproducibility artifacts (an empirical/agent/LLM paper shipping neither code nor the prompts/configs its results need) — every finding span-anchored to a ledger claim_id. At L0/L1 (PDF / source only) the same patterns are surfaced as info-level 'could-not-verify' signals where the ledger gives an anchor (observability_level_required:2) — NEVER a fraud verdict from a PDF. The reviewer PROPOSES findings; tools/adjudicate_findings.py computes the verdict. Detect-only. Triggers: "experiment forensics", "audit the results", "check the eval code", "实验诚实度".
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