「pe」の検索結果

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。

Fix the psychologically significant moment, not the logically significant metric — Rory Sutherland on experience. Use when the outcome is fine but the journey feels bad: complaints about waiting, anxiety, uncertainty, or 'it's technically fast/cheap/correct but people still hate it'; when the user asks 'how do we reduce complaints', 'how do we make waiting/onboarding/checkout feel better', 'people are anxious during X', or is about to spend big to shave a number (wait time, load time, steps) when the real pain is how the moment feels. Covers uncertainty reduction, idle vs occupied waiting, and the peak-end rule. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「待ち時間のクレームが多い」「手続きの途中で離脱される」「お客様を不安にさせている」）にも使う。中文咨询（“等待时间投诉多”“办理中途流失”“让客户感到不安”）也适用。

dependabot 設定（.github/dependabot.yml）の新規作成・整備。リポジトリから ecosystem を自動検出し、lockfile コンフリクトを避ける group 設計と security fast-track を備えた設定を生成する。dependabot 設定の作成・見直し・追加の依頼時、/setup-dependabot 実行時に使用。境界: Renovate は対象外。dependabot が作った PR の対応は pr-review 等の通常フロー。

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

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。

Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

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。

Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.

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。

Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CTCF-only/DNase-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.

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。

permission-ledger hook が記録した permission prompt と各リポの .claude/settings.local.json から allow 昇格候補を集計し、ユーザーが選んだものだけを ai-agents/settings/claude/settings.json に反映する。 「許可ルールを見直したい」「permission prompt を減らしたい」「承認した操作を allow に入れたい」 「permission review」などと言われたときに `/permission-review` で使う。

When the user wants to create competitor comparison or alternative pages for SEO and sales enablement. Also use when the user mentions 'alternative page,' 'vs page,' 'competitor comparison,' 'comparison page,' '[Product] vs [Product],' '[Product] alternative,' 'competitive landing pages,' 'how do we compare to X,' 'battle card,' or 'competitor teardown.' Use this for any content that positions your product against competitors. Covers four formats: singular alternative, plural alternatives, you vs competitor, and competitor vs competitor. For sales-specific competitor docs, see sales-enablement.

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。

Hard readiness gate before implementation begins — cross-checks the constitution, feature spec, plan, and tasks for consistency, traceability, and unresolved ambiguity. Returns PASS or FAIL with specific findings. Load when the spec-driven-development orchestrator routes /analyze, when the user asks to cross-check spec vs plan, audit traceability, verify spec readiness, gate-check before implementation, or says "is this spec implementation-ready", "trace requirements to tasks", "/analyze", "spec sanity check", "spec readiness gate", "spec consistency check". Output: docs/reviews/YYYY-MM-DD-<slug>-spec-crosscheck.md.

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。

Write a rigorous, decision-grade experiment spec — falsifiable hypothesis, primary metric, guardrails, randomisation unit, exposure definition, method (A/B, holdout, switchback, quasi-experiment, MAB), MDE/duration plan, peek policy, validity threats, and pre-committed decision rule. Platform-agnostic. Load when the user has a candidate experiment and needs to spec it before launch, or says "spec this experiment", "write the test plan", "design this A/B test", "what's the hypothesis", "how big a sample do we need", "how long should we run this", "define the metrics for this test", or when the experimentation orchestrator routes 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。

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

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。

Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

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。

Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.

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。

Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CTCF-only/DNase-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.

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の変更内容に合うテストやチェックを選び、失敗原因を調べるスキル。ローカル検証から環境依存の確認、リリース検証の担当手順への振り分けまで扱います。

• ソース変更に合う検証を選びたいとき
• 不具合の再現と修正確認
• CIの失敗原因を調べたいとき

OpenClawのリリース情報を確認し、Discord向けの告知文を作成します。版ごとの更新手順や利用者に試してほしい点を整理し、投稿にも対応します。

• beta版の告知文を作りたいとき
• stable版の更新方法を案内したいとき
• 利用者向けのテスト項目の整理

OpenClawのGitHub Secret Scanningアラートを調べ、IssueやPRなどに残る秘密情報の伏せ字化、コメントの削除・再作成、通知とアラート処理を進めるスキル。

• 認証情報の漏えい箇所を調べたいとき
• IssueやPR本文の秘密情報の伏せ字化
• 秘密情報を含むコメントの削除・再作成

OpenClawのmacOS安定版リリースで、署名、Appleの公証、配布ファイルと更新情報の公開を進めます。事前検証や失敗した処理の再開、公開後の確認も扱います。

• OpenClawのmacOS安定版公開
• 署名・公証用の認証情報の検証
• 失敗したmacOSリリースの再開

OpenClawのDocker環境で一連の動作を確かめるテストを作成・変更するスキル。模擬サーバーと実サービスを使い分け、送信内容や最終応答まで検証します。

• DockerのE2Eテストを追加・変更
• 依存サービスへの送信内容を確認
• 実モデル提供サービスの動作検証

OpenClawのセキュリティ勧告を確認・修正し、非公開フォークの未完了PRや公開条件を検証。承認を経た公開と、公開後の状態確認まで扱うスキル。

• 勧告と非公開フォークの状態確認
• 脆弱性情報や重大度を更新したいとき
• 承認前に公開条件を確認したいとき

OpenClawへの自然な言葉の依頼をClaude Codeなどの外部コーディングエージェントへ振り分け、作業の開始や継続、スレッド内の会話をつなぐスキルです。

• Claude Codeをスレッドで開始
• 外部エージェントの作業を続けたいとき
• acpxから直接指示を渡したいとき

OpenClawからDiscordのメッセージ送信やスレッドへの返信を行い、利用可能な操作の範囲で投稿の編集やボタン付きメッセージを扱うスキル。

• 指定したDiscordチャンネルへの投稿
• 既存スレッドに返信したいとき
• 選択肢のボタン付きメッセージの送信

OpenClawのGit履歴からリリース用の変更履歴を作り直します。利用者に関わる変更をまとめ、Issue・PRへの参照や貢献者の記載漏れを検証します。

• stable版公開前の変更履歴作成
• 前回のbeta版からの差分整理
• 貢献者とPR参照の記載確認