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Draft a project status report against stated milestones using recent Screenpipe activity and verified outcomes. Separate work performed from delivered and accepted results, summarize blockers and next actions, and cite dated sources with verification limits.
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Prepare screenpipe component releases by updating versions and dependency lock files, running required build checks, and triggering GitHub Actions. Verify build results and release artifacts, then hand public app publication over to a person.
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Document visual work with real screenshots captured before a change, during key interactions, and after completion. Inspect each image and share captioned files throughout the task so users can review progress and redirect the work early.
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Operate a physical Android phone connected to OpenMausBot through authorized USB debugging. Open apps, read screens, tap controls, enter text, and swipe, checking the resulting screen after each action to support app testing and phone tasks.
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Inspect Kiln’s interface in a real browser, capture screenshots, and run or debug end-to-end tests that check complete user flows. A sandbox with sample project data provides populated screens for checking UI changes.
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Turn screenshot-based UI feedback into code fixes. Identify layout, styling, component, and state issues, trace their causes, agree on the scope of changes, and verify visual consistency across screen sizes and interaction states.
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Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.
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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.
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Screen and evaluate A-share investment opportunities. Adapts the original deal-screening skill for Chinese market data sources, A-share screening criteria, and domestic deal origination. Triggers on "A股标的筛选", "标的筛选", "screening China", "deal screening A-share", "筛选投资标的", or "screen for [criteria]".
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App Store and Google Play screenshot creation with exact platform specs. Covers iOS/Android dimensions, gallery ordering, device mockups, and preview videos. Use for: app store optimization, ASO, app screenshots, app preview, play store listing. Triggers: app store screenshots, aso, app store optimization, play store screenshots, app preview, app listing, ios screenshots, android screenshots, app store images, app mockup, device mockup, app gallery, store listing
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iOS/Android シミュレーターのスクリーンショットから macOS ウィンドウのタイトルバー （"iPhone 16 Pro / iOS 18.6" 等のシミュレーター chrome）を自動検出して除去するスキル。 alpha チャンネルを利用してバーの高さを自動検出するため、1x/2x/3x どの解像度でも動作する。 Use this skill when the user wants to crop simulator screenshots, remove the simulator title bar, clean up Xcode simulator captures, or prepare app screenshots for App Store. Triggers: "シミュレーターのスクショからバーを除去", "simulator chrome crop", "タイトルバー除去", "スクショのクロップ", "simulator screenshot crop", "シミュレーターのヘッダー消して", "スクショ切り抜き", "crop simulator".
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Stock Screener — pre-built screens (growth, value, momentum, dividend, earnings beats) plus custom natural language criteria. Returns top 10-20 matches ranked by Screen Fit Score (0-100). Triggered by "trade screen growth/value/momentum/dividend/earnings" or "trade screen custom <criteria>".
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Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.
中文简介正在准备中。当前显示原文说明。
Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.
中文简介正在准备中。当前显示原文说明。
Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.
中文简介正在准备中。当前显示原文说明。
Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.
中文简介正在准备中。当前显示原文说明。
Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.
中文简介正在准备中。当前显示原文说明。
Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.
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将界面布局和信息顺序绘制成黑白SVG线框图，配上编号注释；多页面设计可附带流程图，也可整理成支持目录浏览和放大的静态审阅页面。
从符合题材的构图和字体选择入手，规划、实现并检查公开网页，通过真实浏览器测量及三种屏幕宽度的截图评审，反复调整页面外观。
Captures screenshots on registered computers and manages saved captures. Supports screen recording on macOS and timed screenshot sequences on HarmonyOS, checking available capabilities first and returning the finalized file path when recording stops.
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Screen incoming investment opportunities against a fund’s criteria using company sale memoranda, short deal summaries, or broker materials. Extract financials and risks, assess fit, and produce a one-page memo with a recommendation and first-call questions.
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Design and improve in-app upgrade prompts, paid feature screens, and trial expiration notices. Plan when they appear, what they say, how users reach payment, and which metrics to track while keeping dismissal options clear.
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Build AI agents that inspect screenshots, choose actions, and operate a mouse and keyboard. Covers the observation and action loop, desktop control examples, and isolated environments that restrict access to files, credentials, and networks.
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