'screen' 검색 결과

Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy". (gstack)

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Best practices and example-driven guidance for building SwiftUI views and components. Use when creating or refactoring SwiftUI UI, designing tab architecture with TabView, composing screens, or needing component-specific patterns and examples.

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Use XcodeBuildMCP to build, run, launch, and debug the current iOS project on a booted simulator. Trigger when asked to run an iOS app, interact with the simulator UI, inspect on-screen state, capture logs/console output, or diagnose runtime behavior using XcodeBuildMCP tools.

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Use when you need to visually interact with a GUI — test buttons, fill forms, verify visual layouts, fuzz web pages, automate user flows, take screenshots, or perform end-to-end QA on any application. Works on cloud VMs, Docker containers, local machines, and sandboxes. Install: pip install cua.

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Anti-AI-slop design skill for greenfield pages, audits, redesigns, and design extraction from URLs or screenshots. Use when the user asks to build a new app or landing page, wants to redesign something, invokes Hallmark by name, or uses audit/redesign/study.

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HTML -> UCUF (Cocos Creator UI) conversion skill. Use for turning a complete HTML source package with ui-design-tokens.json and colors_and_type.css into Cocos usable layout/skin/screen JSON, then validating with rule guard, visual fidelity risk, runtime interaction smoke, Cocos Editor final gate, and regression evidence.

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UI 自動化生產總控 SKILL — 從 UI 示意圖、參考圖、Figma snapshot 或既有頁面經驗啟動，串接 proof draft、family-map、MCQ、normalized recipe、task shard、ui-spec 與 CompositePanel / ChildPanel scaffold、靜態驗證、Browser QA 自動調整，直到交付人類驗收。USE FOR: 想把一張新 UI 從「給圖」一路推進到可驗證的 screen package 與 QA 閉環。DO NOT USE FOR: 單純修一個 runtime bug、單一 prefab 微調、純美術資產委託。

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參考圖解析 SKILL — 從截圖、Figma 連結或參考 PNG 中，分解出 proof draft（visualZones + componentIntents + spacingRecipe + contentSlots + unresolved notes）。USE FOR: 拿到新畫面設計稿或截圖時，作為 UI production pipeline 第 1 步。DO NOT USE FOR: 直接生成 normalized recipe 或 screen package（需先完成此步再進 family / MCQ 收斂）。

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BROWSER VFX ACCEPTANCE SKILL — Use built-in browser/headless browser to automatically verify Cocos VFX block effects and composed VFX recipes in BattleScene preview (`http://localhost:7456`, `previewMode=true`, `previewTarget=5`). USE FOR: VFX health audit, per-block smoke validation, combo recipe regression checks before merging `vfx-block-registry.ts` / `vfx-effects.json`. DO NOT USE FOR: Cocos Editor window screenshot capture, compile-error debugging, or pure log-only diagnosis without browser preview.

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Agent context budget guard and artifact compression workflow. USE FOR: image-heavy QA, compare board review, screenshot batches, large keep/todo/task manifests, long notes handoff, and md/json diff summarization. Trigger this before reading or forwarding heavy artifacts when token growth is a risk.

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DEBUGGING SKILL — Read and analyze Cocos Creator project.log for runtime errors, warnings, and stack traces. USE FOR: any bug report, crash, UI glitch, TypeError, null reference, missing asset, CompositePanel mount fail, ChildPanel route fail, or screen spec wiring error. Load this skill FIRST before any runtime debugging session. DO NOT USE FOR: compile errors (use get_errors tool instead).

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UI 資產品質驗證 SKILL — 執行 validate-visual-assets.js、validate-ui-specs.js 與必要的 snapshot regression，對 skin family 資產、screen package、proof draft / family-map 進行全面驗證，並回寫 generated-review。USE FOR: 任何 UI 資產生成後、在 ui-preview-judge 前必須先通過的 QA 關卡。DO NOT USE FOR: debug runtime 崩潰（用 cocos-log-reader）、Layout 佈局調整（用 ui-vibe-pipeline）。

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HTML -> UCUF (Cocos Creator UI) conversion skill. Use for turning a complete HTML source package with ui-design-tokens.json and colors_and_type.css into Cocos usable layout/skin/screen JSON, then validating with Plan5 final fidelity gates: stale-rule audit, rule guard, visual fidelity risk, runtime interaction smoke, Cocos Editor final gate, and 95% regression matrix.

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Review a built frontend against its chosen direction, catch drift back to generic AI defaults, enforce state coverage, ethical patterns, UX heuristics, and polish, and check contrast with APCA (not the legacy WCAG ratio). Produces specific, prioritized fixes — never vibes-based feedback. Works with pasted screenshots or Playwright MCP automated capture. Load when the user asks to review a UI, audit a design, check if a frontend looks generic or vibecoded, evaluate visual quality or polish, says "review this UI", "is this design good", "audit my frontend", "does this feel like [product]", "design QA", or when frontend-design routes here. Sub-skill of frontend-design.

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Verify a code change end-to-end in the current worktree: identify what changed, run the relevant tests, get the app running, drive a browser over the affected areas, and report a verdict with screenshots. Generic across project types (Laravel/Herd, Node/bun, frontend, CLI, library). Use when the user wants to verify/QA/smoke-test a change, check that a PR or branch works, validate uncommitted work, or says "/flow", "verify this change", "does this PR work", "test and click through this". Optional target: PR number/URL, branch/ref, or worktree path; default is the current working changes.

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Implement several variations of a design directly inside the running app, on the real page/route, with a live switcher to toggle between them in the browser, then recommend one and fold the winner into the real code. Variants are real implementations judged against real data, layout, and density (not an isolated mockup). Use this skill whenever the user wants to try design options live in the app, prototype changes directly on a real page, add a variant switcher, compare layouts in-context, asks to "build a few versions of this page", "try variants on the real screen", "switch between designs in the app", "implement this with a toggle", or says "real-prototype this". For a safe standalone .html copy that never touches the app, use the prototype skill instead.

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Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

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Track Apple devices and AirTags via FindMy.app on macOS using AppleScript and screen capture.

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Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

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Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.

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Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.

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Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

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Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).

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Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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