「diff」の検索結果

Maps metabolomics results to biological pathways via over-representation (ORA), metabolite-set enrichment (MSEA/QEA), mummichog/PSEA on raw m/z peaks, and network-diffusion enrichment (FELLA), with correct background-set construction and honest interpretive ceilings. Use when interpreting differential metabolites or an untargeted LC-MS feature table in pathway context, choosing ORA vs MSEA vs mummichog vs topology, or setting the reference/background set. For annotation confidence levels feeding ORA see metabolomics/metabolite-annotation; for gene-set concepts see pathway-analysis/go-enrichment and pathway-analysis/gsea; for joint gene+metabolite pathways see multi-omics-integration/mofa-integration.

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Analyze the competitive landscape for an SCF submission. Identifies similar projects on Stellar and other chains, assesses overlap and differentiation. Use when reviewing or drafting an SCF submission and you need to map similar projects on Stellar and other chains and articulate the differentiation.

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Comprehensive PR code review for OneKey monorepo. Use when reviewing PRs, code changes, or diffs — covers security (secrets/PII leakage, supply-chain, AuthN/AuthZ), code quality (hooks, race conditions, null safety, concurrent requests), and OneKey-specific patterns (Fabric crashes, MIUI, BigNumber). Triggers on "review PR", "review this PR", "code review", "check this diff", "审查 PR", "代码审查", "review

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Perform a diff-scoped, behavior-neutral cleanup of AI-generated slop on the current branch before code review. Use for removing trivial comment, defensive-check, type-laundering, redundant-intermediate, compatibility-shim, or local style drift from the branch diff. Do not use for full correctness or safety review, documentation work handled by technical-documentation or docs-refactor, or repo-wide refactors.

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GitHub PR の CI ステータス (`gh pr checks`)・コード品質・セキュリティ (OWASP Top 10)・Conventional Commits 準拠をレビューする。`gh pr review --approve/--request-changes/--comment` でレビュー投稿も可能。「PR #N をレビュー」「PR レビューして」などで使用。ローカル diff のレビューには implement-review を使用。

コード変更 (`git diff`) の品質・アーキテクチャ準拠・セキュリティ (OWASP Top 10) を Agent ツール委譲で読み取り専用レビューする。コミット前/PR 作成前のセルフレビュー、「変更をレビューして」「コードレビューして」などで使用。GitHub PR のレビューには implement-review-pr を使用。

Rust 製 AI/ML ライブラリ fandhe-ai のリファレンス。Burn / candle / tch 非依存の from-scratch 実装。 compat::Sequential, compat::array, tape(), tape_for(), Tape, Var, Tensor, Gradients, LinearVars, Device (Cpu / Cuda / Metal)。 tensor-core の kernel fusion, FusionPlan, BackendOps。dynamic-tape autodiff。 cfg ベースのバックエンド構成・数値一致契約。ONNX / safetensors 相互運用。 guardrail / self-repair CLI。

AppKit macOS ネイティブ UI フレームワークリファレンス。 NSApplication, NSWindow, NSViewController, NSView, NSMenu, NSToolbar, NSTableView, NSOutlineView, NSCollectionView, NSControl, NSStackView, NSScrollView, Diffable Data Source, target-action, responder chain。

Generate ideas that don't sound logical, then validate them with cheap behavioural experiments — Rory Sutherland's 'the opposite of a good idea can be a good idea' plus revealed-preference testing. Use when the obvious/logical solution is saturated and the user wants genuinely different options, asks 'what's a creative/unconventional angle', 'how do we stand out', 'should we run a test/experiment', 'how do we know what people actually want', or is about to trust a survey/focus group. Covers deliberate inversion of the obvious move, why stated preferences lie, and designing small reversible experiments that watch behaviour. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic. 日本語の相談（「競合と同じ案しか出ない」「アンケートは好評なのに売れない」「思い切った案を安く試したい」）にも使う。中文咨询（“只能想出和竞品一样的方案”“问卷好评却卖不动”）也适用。

The perception-by-culture lens for psycho-logic — establish HOW a specific region, country, or subculture reads a signal before you send it, because perception is culturally coded and the same move can mean the opposite elsewhere. Use when adapting a product, price, message, or campaign to a new market; when the user asks 'will this work in [country/region]', 'why does this land in X but flop in Y', 'how do people here perceive Z', 'how do we localise/differentiate by region', or is about to copy a US/global playbook into another market. Covers the cultural dimensions that move Sutherland's levers — uncertainty avoidance, individualism/collectivism, status/power distance, high- vs low-context communication, mental-accounting norms, trust locus, and taboo/sacredness — and how each rewires reframing, costly-signaling, and experience-design. Inspired by Rory Sutherland's *Alchemy*. Routed to from psycho-logic; pair with market-recon for live local data. 日本語・中文の相談（「この地域で通用する？」「适合本地市场吗？」）にも使う。

修正完了後に agent-review-kit でレビューHTMLを生成し、ユーザーがブラウザで書いた差分コメントを Codex または Claude Code の同一セッションで受け取り、回答・修正・resolve を未解決0件まで繰り返すレビューループ。AIレビューの指摘を add-comment でブラウザ上のコメントとして表示し、ユーザーが返信した指摘だけ修正する「AIレビューモード」も含む。実装プラン・設計書などの任意HTMLを publish-html でレンダリング済みのまま表示し、要素クリック・テキスト範囲でコメントを受け取る「HTMLレビューモード」も含む。他のレビュースキルの結果の出力先として agent-review-kit が指定された場合も、このスキルを読んで AI レビューモードの手順に従うこと。Use when the user wants to review changes in a browser (GitHub-like diff review), review a rendered HTML doc (plan/design doc) in a browser, iterate on fixes with inline comments, or display AI review findings as browser comments (output target: agent-review-kit).

コードを書く時の実装原則（deep module 設計・seam・テスタビリティ・シーム限定 TDD・コメント規約・リーダブルコード）。新しい module・関数・エンドポイント・画面の追加、DB への書き込み、認可・入力検証の追加、既存構造を変えるリファクタを含む実装の開始時に使用。CSS・HTML・テンプレート等のフロントエンド資産も対象。既存関数内の数行修正と lint/CI 等の設定ファイルでは使わない。TypeScript/Python/Go の言語別ガイドを同梱。境界: バグ・エラーの原因調査は systematic-debugging が先、提出前の diff 確認は self-review。

バグ・エラー・テスト失敗・ビルド失敗・予期しない挙動のうち、最初の修正で直らなかった時、または原因を1行で説明できない時に使う根本原因調査の規律（根本原因調査→パターン分析→仮説検証→実装の4フェーズ）。症状への場当たり修正を防ぐ。境界: 原因特定後の修正実装は writing-code、提出前の diff 確認は self-review。

現在の変更内容を確認し、適切なブランチ名を提案してブランチを作成するときに使う。ユーザーが「ブランチ作って」「作業用ブランチ切りたい」「変更に合わせてブランチ名を決めて」と言ったとき、または新規作業を始める前にブランチを作りたいときに適用する。git status / diff を確認し、プレフィックス規則（feat/fix/release/epic）に従ってブランチ名を提案し、ユーザー確認後に git checkout -b で作成する。

Detects TAD boundaries from balanced Hi-C contact matrices via the diamond-window insulation score (cooltools insulation) and HiCExplorer hicFindTADs, returning a continuous log2 insulation track, valley-prominence boundary_strength, and Li/Otsu-thresholded is_boundary flags across a list of window sizes. Covers the multi-scale window sweep (sub-TAD to compartment-domain), why the boundary is reproducible but the domain partition is not, cross-condition comparison via differential SCORE not differential partition, and the insulation-vs-compartment orthogonality. Use when calling TADs or domain boundaries, computing insulation scores, choosing a window size, ranking boundary strength, comparing boundaries across conditions, or annotating CTCF-backed boundaries; route domain rendering to hic-visualization and boundary-feature overlap to genome-intervals.

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.

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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Performs differential expression on bulk RNA-seq count data with DESeq2's negative-binomial GLM, Wald and LRT testing, apeglm/ashr/normal LFC shrinkage, independent filtering, Cook's outlier handling, VST/rlog transforms, and design formulas including paired, batch, and interaction terms. Use when running bulk DE, choosing DESeq2 over edgeR or limma-voom, building a paired or interaction design, applying LFC shrinkage for ranking or GSEA, choosing Wald vs LRT, troubleshooting padj=NA, picking VST vs rlog, importing salmon/kallisto via tximport, or analyzing prokaryotic RNA-seq.

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Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.

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Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.

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Normalizes ChIP-seq data using exogenous spike-in (ChIP-Rx with Drosophila chromatin per Orlando 2014 / Egan 2016; E. coli carryover for CUT&RUN/CUT&Tag). Distinguishes RRPM from Rx-Input scaling, integrates with DiffBind / DESeq2 / edgeR / csaw via sizeFactors and DiffBind library-size vectors, and applies the Patel et al 2024 *Nat Biotechnol* review's failure-mode framework to validate that normalization is correctly applied at the read level (not peak counts). Use when global signal shifts are expected (HDACi, BETi, EZH2i, dosage, target knockdown), when ChIPseqSpikeInFree detects post-hoc shifts, or when validating internal-control regions before publication.

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Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.

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。