「orr」の検索結果

End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware screens. Use when analyzing any pooled CRISPR screen end-to-end, matching the hit-calling method to the experimental design, integrating copy-number correction into the pipeline, or branching the workflow for single-cell, combinatorial, base-editor, prime-editor, or in vivo variants.

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。

Decision-grade statistical analysis for metabolomics intensity tables. Covers transformation and scaling (Pareto vs unit-variance as a hidden hypothesis), unsupervised structure (PCA/HCA for QC), permutation-validated PLS-DA/OPLS-DA (R2 vs Q2, double CV, VIP as heuristic), univariate testing (Welch/Mann-Whitney/ANOVA/LMM with covariate adjustment), and dependence-aware multiple testing. Use when testing which metabolites differ, building or validating a discriminant model, choosing a scaling, or correcting many correlated tests. For sample-wise normalization/drift correction see metabolomics/normalization-qc; for ML classifiers and selection-inside-CV leakage see machine-learning/biomarker-discovery and machine-learning/model-validation; for pathway interpretation see metabolomics/pathway-mapping; for design/power/multiplicity regime see experimental-design/multiple-testing.

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。

Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。

Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.

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。

Runtime security for agent skills — prevents state corruption, skill overwrite attacks, denial of service, and enforces provenance tracking and no-go repo management. Load as part of the secure-* skill sequence whenever an agent processes external content or writes to the skill store. Also load when the user asks to check for state corruption, prevent skill overwrite, manage no-go repos, check provenance, audit runtime security, detect DoS patterns, or protect the skill store. Covers Issues 6, 9, 10 from the agent security threat model: instruction hierarchy enforcement, state corruption and skill overwrite, and denial of service prevention.

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。

Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.

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。

Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

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。

Detect NTLM relay attacks through Windows Security Event correlation by analyzing Event 4624 LogonType 3 for IP-to-hostname mismatches, identifying Responder/LLMNR poisoning artifacts, auditing SMB and LDAP signing enforcement across the domain, and detecting NTLM downgrade attacks from NTLMv2 to NTLMv1 using event log analysis.

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。

OpenClawからSlackのメッセージ送信やスレッド返信を行います。相手・投稿・アカウントを特定し、利用可能な操作に沿ってやり取りを進めるスキルです。

• チャンネルや相手に連絡したいとき
• 既存スレッドに返信したいとき
• 編集やリアクション前に投稿を特定

PDFの指定ページに対し、タイトルの変更や誤字の修正を自然な言葉で指示するスキル。nano-pdfを使い、編集後は対象ページと出力内容を確認します。

• PDFのページタイトルを変更したいとき
• PDFのサブタイトルの誤字修正
• タイトル変更と誤字修正をまとめて依頼

製造現場の規格不適合について、対象品の隔離から原因分析、処置の検討、是正・予防措置の効果確認までを整理し、管理図の読み取りや仕入先品質の改善も支援します。

• 検査で見つかった不適合を調査したいとき
• 根本原因の分析手法を選びたいとき
• 是正措置の計画作成と効果確認

複数のEVMチェーンでトークン残高や価格の桁がずれる問題を防ぐため、小数桁数の実行時取得、チェーン別キャッシュ、精度を保つ計算と単位統一を扱うスキル。

• トークン残高の桁ずれを調べたいとき
• 複数EVMチェーンの数量比較
• ブリッジ後の小数桁数の確認

大量データの取り込みや過去分の補充、変換、同期の遅さを工程別に調べ、処理方法を比較して高速化し、行数・ファイル数・時刻の整合性まで確認するスキル。

• 大量データの取り込み・書き出しの高速化
• 過去データの補充時間を短縮したいとき
• バッチサイズや並列処理数を比較したいとき

処理の高速化を、現状の計測、仮説ごとの変更案の比較、正しさの検証へ分解し、時間や費用の上限内で最も速い安全な案と再実行できる手順を残すスキル。

• 遅い処理を計測しながら高速化したいとき
• 同じ入力条件で実装案を比較したいとき
• 応答時間・処理量・実行費用の評価

Automate Corrently tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Render-correctness doctrine for scene-to-scene seams in HyperFrames launch videos — the prerequisites that make transitions composite correctly on the master timeline. Load when assembling the master timeline / index.html, when a white flash appears at a cut or crossfade seam (especially on dark films), when reasoning about why a transition opacity dip shows through, or when verifying the render-side mechanics of how overlapping scene wrappers blend. Covers the opaque stage-ground (#root background) white-flash guard and how the injector overlaps wrappers, holds final frames, ping-pongs tracks, and stamps lint-clean template code onto the master timeline. Does NOT contain the per-transition catalog — see the transition registry for individual transition entries.

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。

Assess the impact of a significant change during sprint execution across the PRD, epics, architecture, and UX documents, and produce a sprint change proposal. Use when the user says "correct course" or "propose sprint change"

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for DFIR chronology, cross-artifact correlation, persistence chains, and incident timeline reconstruction. Use when the user asks to build a forensic timeline, correlate EVTX, PCAP, registry, disk, memory, mailbox, or browser artifacts, explain the order of attacker actions, or pinpoint the stage where the decisive artifact appears. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Elliott Wave Theory signal engine. Detects swing points through Zigzag, matches 5-wave impulse and 3-wave corrective structures, validates them with Fibonacci wave relationships, and generates trend-top / correction-complete signals. Pure in-house pandas implementation.

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。

Deploy MISP via Docker and configure feeds from sources like abuse.ch, AlienVault OTX, and CIRCL to aggregate, correlate, and distribute threat intelligence, including automated feed synchronization and STIX/TAXII-based integration with Splunk, Elasticsearch, and SOAR platforms. Use when standing up centralized IOC management or wiring multi-source threat feeds into a SIEM.

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 automated IOC enrichment pipelines in Splunk Enterprise Security by ingesting threat feeds into KV Store collections and correlating them against security events via lookup tables, modular inputs, and the Threat Intelligence Framework. Use when wiring threat intel into Splunk correlation searches to flag IOC matches and cut SOC triage time.

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 effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.

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。

Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

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。