3천건 · 관련도순
Find facts, relevant examples, and prior wording in work history to support a writing draft. Use source evidence to separate completed work from plans and opinions, verify quotations, and remove sensitive details from public drafts unless disclosure is authorized.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Find information you previously saw on screen and reconstruct past work from locally recorded screenshots, recognized text, and app usage. Summarize research, measure recorded screen time, and provide references that reopen the relevant moments.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review a product experience by walking through its steps and capturing screenshots. Produces an illustrated assessment of usability, design, and accessibility risks, with findings tied to specific screens and clear limits on what was checked.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Screen supplied proteins, peptides, antibodies, or nanobodies against a target with Boltz. Prepares inputs, obtains a cost estimate for approval, downloads results, and ranks candidates by binding confidence, a score for predicted binding reliability.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build a burning-paper loading ring that keeps moving during heavy page setup. The skill covers background rendering, progress display, a smooth transition into the page, and support for reduced motion preferences.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build OpenBot administration and settings screens with shared layout components. Provides consistent page widths, row structures, control sizes, and dialog layouts, including patterns for switches, editable summaries, and read-only values.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
When the user wants to design, optimize, or evaluate App Store screenshots and preview videos. Also use when the user mentions "screenshots", "app preview", "product page design", "screenshot design", "creative assets", or "what should my screenshots show". For A/B testing screenshots, see ab-test-store-listing. For full ASO audit, see aso-audit.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Writing screenplays in the style of Ozu Yasujiro and Noda Kogo (小津安二郎・野田高梧) — distilled from six complete screenplays (晚春 Late Spring, 麦秋 Early Summer, 东京物语 Tokyo Story, 浮草 Floating Weeds, 秋日和 Late Autumn, 秋刀鱼之味 An Autumn Afternoon). Covers the "family member leaves" master plot, off-screen events / on-screen reactions, lie-driven plots, minimal-syllable confessions, go-between characters, faceless suitors, pillow-shot scene breaks, echoing objects, mirror characters, children's subplots as metaphor, displaced quarrels, the single-monologue rule, diminishing endings, Ozu's script format and stage-direction vocabulary. Use when the user wants to write a quiet family drama, a 小津风 / 侘寂 / 日常系 script, a story about marrying off a daughter, aging parents, or wants dialogue that says less and means more.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/. Use for risky or multi-step feature work that needs upfront questioning, API seams, browser-playable checkpoints, HTML visualizations, screenshot gates, staged implementation plans, recursive fog-of-war reslicing, or proactive research into reference implementations/best practices before slicing. Pairs with your project's verification harness and screenshot gates (the browser checkpoints), [refactor-clean](../refactor-clean/SKILL.md) (review the materialized spec so the plan describes one-owner architecture, not the feature bolted on), [screenshot-critique](../../visual/screenshot-critique/SKILL.md) and [compare-screenshots](../../visual/compare-screenshots/SKILL.md) (the visual gates), and a code-review pass (audit each slice before it lands).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate complete user documentation for a web app with screenshots. Browses the app via browser automation, screenshots every screen, and produces a structured user guide with step-by-step instructions, annotated screenshots, workflow diagrams, and reference tables. Supports quick (key screens), standard (all pages), thorough (every state and flow), and exhaustive (publishable documentation suite). Triggers: 'document the app', 'user guide', 'app documentation', 'screenshot docs', 'generate user docs', 'help docs', 'how-to guide', 'write the docs'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Adjudicates whether a hit generated by sanctions, PEP, or adverse-media screening is a true positive, false positive, or requires human escalation. Use whenever a user presents a screening alert, a name match against a watchlist (OFAC SDN, EU consolidated list, UK OFSI, UN list, PEP list, adverse media hit, etc.), or asks to clear a screening hit / reduce false positives / determine whether a flagged name is actually the listed party. Use even when the user describes the task casually — "is this person actually on the sanctions list", "did we get a real match", "clear this alert", "I have a hit on X" — these are all screening-adjudication tasks. Produces a deterministic determination with full audit trail (structured JSON + human-readable narrative). Designed for use by compliance analysts and screening systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate a spreadsheet of test entries — newly designated names from OFAC, OFSI, and EU sanctions lists plus deliberate variations of those names — to validate that a sanctions screening system catches fresh designations and is tuned to the right fuzziness threshold. Use this whenever the user asks for sanctions list update test data, screening regression test data, screening QA, fuzzy match calibration, or wants to verify their screening lists are current. Trigger even if the user doesn't say 'screening' explicitly — phrases like 'test my sanctions list', 'check our SDN coverage', 'is my list up to date', or 'build me a regression set from the latest designations' should also invoke this skill.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Conducts pre-DPIA threshold screening to determine whether a full Data Protection Impact Assessment is required under GDPR Article 35. Applies the EDPB WP248rev.01 nine-criteria test, national supervisory authority blacklists, and organisational risk appetite to produce a documented screening decision. Keywords: threshold screening, DPIA trigger, pre-DPIA, WP248, Article 35(1), blacklist, screening decision.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload pipelines.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Composite any two HTML screens into a locked photo plate of hands holding an open foldable phone — a 1448×1086 still with two blank 495×849 / 498×849 screen slots (left and right) — to make side-by-side meme and comparison videos: TikTok vs Reels, ChatGPT vs Claude, "me vs my friend", before/after, two apps racing. Trigger on: "foldable phone meme", "put X on the left screen and Y on the right", "two-screen comparison video", "the dual-screen phone meme", or any request that names two apps/feeds/chats to show at once. The plate, geometry, and camera are finished — the agent only authors what plays INSIDE the two screens and renders.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Lists all screens in a Stitch project. Use this after generate_screen_from_text to find the screenId of the newly generated screen, then call stitch-mcp-get-screen to retrieve it.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.