「xp」の検索結果

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

日本語の概要は準備中です。原文の説明を表示しています。

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

日本語の概要は準備中です。原文の説明を表示しています。

Use when writing C# / VB.NET / F# code that reads, writes, manipulates, or exports Excel (.xlsx / .xls) files, generates PDF or HTML from Excel, or produces data-driven reports with FlexCel Studio for .NET (TMS Software). Triggers include Excel/xlsx from C#, XlsFile, FlexCelReport, FlexCelPdfExport, FlexCelHtmlExport, FlexCelImgExport, ExcelFile, PdfWriter, .NET Excel export, ASP.NET Excel generation, .NET MAUI Excel, Native AOT + Excel, and Excel reporting from .NET.

日本語の概要は準備中です。原文の説明を表示しています。

Especialista em construir experiências imersivas baseadas em scroll - storytelling por paralaxe, animações de scroll, narrativas interativas e experiências web cinematográficas. Como as interativas do NY Times, páginas de produtos da Apple e experiências web premiadas. Torna websites em experiências, não apenas páginas. Use quando: animação de scroll, paralaxe, storytelling de scroll, história interativa, website cinematográfico.

日本語の概要は準備中です。原文の説明を表示しています。

Build a rich, self-contained HTML explanation of a code change — diff, branch, commit range, MR/PR — with background, intuition, a code walkthrough, diagrams, and a glossary, then quiz the reader interactively in chat. Use when the user asks to explain a change, understand a PR/MR, walk through a diff, onboard onto a branch, or says "explain this PR", "what does this branch do", "teach me this change", or "/explain-diff".

日本語の概要は準備中です。原文の説明を表示しています。

Erstellt und verwendet einen anbieterneutralen semantischen Routing-Graphen aus Personas, koordinierenden Rollen, Experten und aktiven Skill-Endpunkten. Verwenden, wenn ein LLM eine Anfrage von einer Leitrolle über einen Experten zu einem Skill routen, aus einem vorhandenen Agentensystem einen portablen Persona-Router extrahieren, eine semantische Domänenkarte mit einer lexikalischen Skill-Registry kombinieren oder fehlende Verbindungen zwischen Rolle und Skill sichtbar machen soll, statt still auf einen Ersatz zurückzufallen. Auslöser sind unter anderem semantisches Persona-Routing, Persona-Umbrella, Rollen-Router, Boss-Agent-Experten-Skill-Routing, Agentenrollen-Export oder die Wiederverwendung von Personas über mehrere LLM-Anbieter hinweg.

日本語の概要は準備中です。原文の説明を表示しています。

Turns a schedule extract (milestone list or planning tool export) and the period's status notes into a DRAFT plain-language slip explanation: which milestones and activities slipped and by how many working days, arithmetic shown, why per the notes as verbatim fragments with source and date, what each slip pushes next per the extract, and the decision each slip needs framed as a question with the options the notes propose and the owner the governance documents name, every missing fact marked UNKNOWN. Use when the user asks to "explain the schedule slip", "what slipped this month and why", "write the schedule commentary for the steering pack", "put the delay in plain language for the sponsor" or "what decisions do these slips need". Do not use for the weekly status report, use project-status-tracker instead; for cost variances, use budget-variance-explainer; for one sprint's carry-over, use sprint-review-summary. Drafts for human review; never approves, authorises or signs off.

日本語の概要は準備中です。原文の説明を表示しています。

Reads one export transaction, order or shipment description and returns a DRAFT export review pack: parties to screen with a screening checklist, an item classification worksheet, a red-flag review, licence-determination questions and open items. Use when the user asks to "prepare the export review for this order", "pre-check this shipment for export control", "who do we need to screen on this deal", "build the classification worksheet for this item" or "assemble the trade-compliance file for this technology transfer". Do not use for vendor security or due-diligence screening of a supplier's questionnaire answers; use vendor-risk-screening-brief instead. Never classifies, screens, clears, decides licence need or releases a shipment; trade compliance does. Drafts for human review; never approves, authorises or signs off.

日本語の概要は準備中です。原文の説明を表示しています。

Pre-checks one expense claim or travel plan, line by line, against the expense or travel policy the user supplies, and returns a DRAFT precheck sheet: the policy clause matched to each line, the limit quoted as the policy states it, whether the amount sits within, at or outside that limit, missing receipts, pre-approvals or justifications, and neutral questions for the approver and the claimant, with UNKNOWN wherever the policy or the claim is silent. Use when the user asks to "check this expense claim against policy", "pre-check my travel request", "is this claim within policy", "review these expenses before I submit them", "what receipts or approvals are missing" or "prepare the approver's questions for this claim". Do not use for a purchase order log or spend extract, use purchase-order-anomaly-review instead; do not use to compare a policy against a standard or regulation, use policy-gap-review instead. Drafts for human review; never approves, authorises or signs off.

日本語の概要は準備中です。原文の説明を表示しています。

Assign transactions to tax-aligned expense categories using vendor name, line items, and amount patterns. Handles ambiguous vendors like Amazon by inspecting line-item detail from the receipt source. Supports split categorization and learns from user corrections. Trigger on "categorize expenses", "sort transactions", "assign expense categories", "what category is this".

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Great Expectations — great Expectations data quality framework analysis. Covers expectation suite management, validation result review, data docs generation, checkpoint execution, datasource configuration, and batch analysis. Use when reviewing data quality results, managing expectation suites, investigating validation failures, or auditing data quality configurations.

日本語の概要は準備中です。原文の説明を表示しています。

Export Twitter/X data to CSV for analysis using Xpoz. Search by keywords, author, date range, and download complete datasets (up to 500K rows). Use when asked to "export tweets", "download Twitter data", "get tweets as CSV", "Twitter dataset", or "bulk tweet download".

日本語の概要は準備中です。原文の説明を表示しています。

Browse, inspect, re-export, embed, or iterate on videos that already exist in the user's Ozor.ai account. Use this skill whenever the user wants to work with past or in-progress Ozor videos rather than create a new one — e.g. 'list my Ozor videos', 'show my recent videos', 'what's the status of my last video', 'get the embed code for that video', 'download my last Ozor video', 'share link for the explainer I made', 'check on the export', 'send a follow-up edit to that video', 'show the plan for video X', 'render that video again at 9:16', 'is the job done yet'. This skill orchestrates list_videos, get_video, get_job, get_plan, update_plan, send_message, wait_for_job, export_video, wait_for_export, and get_embed_code. Do NOT use this skill to create a new video from scratch — use ozor-generate, ozor-document-video, or ozor-url-to-video.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences. Use when: 3D website, three.js, WebGL, react three fiber, 3D experience.

日本語の概要は準備中です。原文の説明を表示しています。

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).

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

日本語の概要は準備中です。原文の説明を表示しています。

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

日本語の概要は準備中です。原文の説明を表示しています。

Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.

日本語の概要は準備中です。原文の説明を表示しています。

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

日本語の概要は準備中です。原文の説明を表示しています。

Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

日本語の概要は準備中です。原文の説明を表示しています。

Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

日本語の概要は準備中です。原文の説明を表示しています。