「diff」の検索結果

Use when designing, auditing, or simplifying Record Types and Page Layouts. Triggers: 'record type', 'page layout', 'different picklist values', 'different fields per team', 'dynamic forms', 'business process', 'recordTypeVisibilities', 'layoutAssignments', 'record type deploy failed', 'businessProcess required', 'layout assignment matrix'. NOT for layout explosion across many profiles — use admin/record-type-strategy-at-scale. NOT for sharing rules — use admin/sharing-and-visibility. NOT for FLS — use admin/permission-sets-vs-profiles.

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Systematically QA test a web application. Use when asked to "qa", "QA", "test this site", "find bugs", "dogfood", or review quality. Four modes: diff-aware (automatic on feature branches — analyzes git diff, identifies affected pages, tests them), full (systematic exploration), quick (30-second smoke test), regression (compare against baseline). Produces structured report with health score, screenshots, and repro steps.

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Mechanical consistency check for game design — answers "is the design consistent with itself?" — plus changed-scope impact analysis across all three GDD axes: systems, flows, and content types. Offline and deterministic: greps the facts registry against the documents (numbers, terms, IDs, duplicate & dangling references, roster↔disk, map freshness, Depends 3-way, status/version, AC presence, placeholder leaks, flow & content schema), and from a git diff computes which dependent systems, flows, and content a change affects. Scope is inferred: a named document checks that document; an unverified diff triggers a changed-scope pass; otherwise everything. Use when the user wants a consistency or impact check, e.g. "verify the design", "what did this change affect", "find broken references in the design". Conflicts triage into four tracks; the entailed fixes hand off to /unikit-gd-apply in one pass. This is the mechanical pass — for a subjective "is this design good" quality critique use /unikit-gd-review.

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Whole site or product — a full web accessibility (a11y) audit against WCAG 2.2, following the WCAG-EM methodology. Defines scope, samples representative pages and flows, runs the automated tier (`accessibility-scan`) and the hands-on manual tier (`accessibility-inspect`), and produces one conformance report. Grades each finding by severity and evidence basis, and states per-criterion conformance as pass, fail, or undetermined (needs a human). Use it for 'audit my site for accessibility', 'is this product accessible', 'a11y audit', 'WCAG or Section 508 conformance report', or any multi-page assessment. Assesses; does not fix (use `accessibility-fix`) or diff (use `accessibility-diff`). For a single page use `accessibility-scan`; for hands-on keyboard and screen-reader checks use `accessibility-inspect`.

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One page, automated tier — run the web accessibility (a11y) rule engine against a live page and locate every violation it can detect mechanically. Pass a URL, a config target name (e.g. `accesslint:accessibility-scan dev`), or nothing to use the default target from `accesslint.config.json`. Ensures a debuggable Chrome, runs the @accesslint/core engine over CDP, and returns a worklist of live-DOM WCAG 2.2 violations, each grounded to its DOM selector and source `file:line`. Locates; doesn't edit. Use it for 'is this page accessible', 'check a11y on this URL', 'find contrast and alt-text issues', or to verify a UI change. For hands-on keyboard and screen-reader checks use `accessibility-inspect`; for a whole site or product use `accessibility-audit`; to diff against uncommitted changes or a branch use `accessibility-diff`.

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Regression check — diff a live page's web accessibility (a11y) violations against a baseline. By default it compares your uncommitted changes (stash-based); pass `--branch [<name>]` to compare against a branch. Reports the new WCAG violations introduced, the ones fixed, and the count of pre-existing ones. Use it for 'did my change break accessibility', 'what a11y issues did this PR add', or as a CI gate. For a full scan of one page use `accessibility-scan`; for a whole site use `accessibility-audit`.

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This skill should be used when the user asks to "implement a DiD regression", "run a staggered difference-in-differences", "set up an event study", "implement IV / 2SLS", "run a regression discontinuity design", "build synthetic control", "do propensity score matching", "test parallel trends", "run Honest DiD", "wild cluster bootstrap", "Callaway-Sant'Anna", "Sun-Abraham", "Bacon decomposition", "double machine learning", "debiased machine learning", "DML / DoubleML", "cross-fitting", "causal forest", "heterogeneous treatment effects", "ATE with ML controls", "which causal design fits my data", or needs causal-inference code, diagnostics, or identification writing in Python (StatsPAI), R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape, with runnable validated templates, ML-based causal inference patterns, and Journal of Finance applications.

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QA a change by driving the running dev app like a real QA engineer - start the server and a real browser session, write a checklist from the agreed requirements and the diff, execute every item with evidence, then go looking for anything that looks off even where the diff did not touch. Reports findings by severity and separates regressions from pre-existing problems. Use after a change is implemented and the gates pass, when the user asks to "QA this", "test it in the app", or "check it actually works".

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uncommitted-working-copy-diff-review

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rescue 브랜치 diff의 파일별 이식 판정

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base 전진 시 retained worktree diff의 3-way 재이식

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Compare two local documents and convert differences into LLM-ready Markdown in one synchronous call. Supports PDF, Word, PPT, and image documents accepted by Calliper, and returns a structured markdown diff table suitable for change review, clause comparison, downstream extraction, and rule-based validation.

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Use when tuning the numbers in a game — economy curves, progression rates, difficulty pacing, drop tables, win/loss odds, time-to-X targets, and currency velocities. Triggers on "balance pass", "tune the economy", "progression curve", "difficulty curve", "drop rate", "XP curve", "TTK", "time-to-content", "balance the economy", "spreadsheet model", "economy sim", or a system spec from game-systems-designer with `TBD` placeholders. Produces an economy spreadsheet, balance pass plan, sim outputs, and per-system balance tables. Stops at the numbers — does not change system rules or pricing. For systems design see game-systems-designer; for store catalog see iap-manager.

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Opinionated house conventions for NestJS backends — module boundaries and layering, provider wiring and injection scopes, guards/interceptors/pipes/filters, DTOs and validation, domain exceptions, config, and the Nest 11 / Express 5 behaviour that compiles cleanly and fails at runtime. Use whenever the code is NestJS (`@nestjs/*` in package.json, or `@Module`/`@Injectable`/`@Controller`/`@Cron` in the file), including when the question sounds like plain TypeScript, an OOP refactor, a code review, or an ops problem — the Nest-specific answer differs from the generic one. Typical asks: scaffolding a module, adding a controller, provider, or endpoint, wiring an enhancer, a boot-time DI or circular-import failure, a response leaking entity fields despite `@Exclude()`, a cron job firing on every replica, upgrading NestJS, or reviewing a Nest diff. Not for plain Express or Fastify, Angular, frontend, SQL, or CI.

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Navigate difficult conversations and deliver constructive feedback using structured frameworks. Covers the Preparation-Delivery-Follow-up model and Situation-Behavior-Impact (SBI) feedback technique. Use when preparing for difficult conversations, giving feedback, or managing conflicts.

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Use when designing a background-job system, choosing between BullMQ / Sidekiq / RQ / Temporal / SQS, deciding queue-vs-workflow, sizing concurrency vs rate limits, building dead-letter queues, or making handlers idempotent. Triggers: jobs running twice on retry, lost jobs after worker crash, DLQ filling up, Redis OOM from job backlog, exactly-once requested, "do we need Temporal?", visibility timeout / lockDuration confusion, exponential backoff vs jitter, fan-out fan-in workflows. NOT for outbound webhook publishing (different concerns), receiver-side webhook handling (different concerns), event-streaming/Kafka topology, or in-process async (event loop only).

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Multi-pass structural code review with adversarial personas. Input: branch diff (default), PR #123, commit, --pending. Use for 'review this PR/diff'. NOT for behavioral verification (mk:evaluate).

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Multi-pass structural code review with adversarial analysis, scope-aware dispatch, adversarial persona passes, and forced-finding protocol. Supports input modes: branch diff (default), PR number (#123), commit hash, pending changes (--pending). Use when asked to "review this PR", "code review", "pre-landing review", "check my diff", or "review #123". Proactively suggest when the user is about to merge or land code changes. NOT for behavioral verification against a running build (see mk:evaluate); NOT for post-implementation simplification (see mk:simplify).

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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.

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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.

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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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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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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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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.

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。