1.5만건 · 관련도순
Implement fog of war, an effect that conceals unseen areas, in Three.js action games. It combines soft visual boundaries with obstacle-aware visibility rules for targeting and combat, plus performance tuning and testing guidance.
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Fetch official documentation from Anthropic and save local snapshots through an MCP tool. Initialize both baseline and current copies, then report the section count and storage path so later refreshes can be compared against the baseline.
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Rename the current thread or session in Cloudroom with a short title describing its main activity. When explicitly requested, the skill chooses two to five lowercase words, updates the title through room-cli, and returns the new name.
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Find locally stored Codex conversations from a thread link, ID, or session file path. Review requests, answers, and executed commands, build a timeline of past work, search relevant details, and export a Markdown transcript.
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Set up and inspect periodic prompts that continue a specific Codex thread after idle checks pass. It supports App-native automation and a headless fallback, with controls to inspect status and enable or disable scheduled prompts.
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Install and configure Anthropic Claude SDK authentication for Python and TypeScript. Use when setting up a new Claude API integration, configuring API keys, or initializing the Anthropic SDK in your project. Trigger with phrases like "install anthropic", "setup claude api", "anthropic auth", "configure anthropic API key", "claude sdk setup".
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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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Use when you need to apply Java concurrency best practices — including thread safety fundamentals, ExecutorService thread pool management, concurrent design patterns like Producer-Consumer, asynchronous programming with CompletableFuture, immutability and safe publication, deadlock avoidance, virtual threads, structured concurrency, scoped values, backpressure, cancellation discipline, and observability for concurrent systems. This should trigger for requests such as Review Java code for concurrency; Review Java code for thread safety; Fix race conditions in Java concurrency code; Choose ExecutorService or virtual threads in Java; Improve synchronization and shared mutable state handling; Apply structured concurrency for related Java subtasks. Part of Plinth Toolkit
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Analyze a codebase and produce a structured threat model at .turbo/threat-model.md covering assets, trust boundaries, attack surfaces with existing mitigations, attacker stories, and calibrated severity. Use when the user asks to "create a threat model", "threat model", "threat model this codebase", "security analysis", "analyze the attack surface", "what are the threats", or "identify security risks".
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Configures privacy settings for enterprise HR systems including SAP SuccessFactors, Workday, and BambooHR. Covers role-based access controls, automated data retention enforcement, cross-border transfer configurations, audit logging, data subject rights facilitation, and field-level security. Keywords: HR system, SAP SuccessFactors, Workday, BambooHR, RBAC, retention automation, cross-border transfer, privacy configuration.
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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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Read your Threads (Meta) audience and niche from real data. Scan a niche query or hashtag for the top posts right now with likes, replies, reposts, and quotes, and pull a profile (yours or a competitor) for follower count, bio, verified status, and recent posts with engagement. Powered by Apify, no login. Triggers on "analyze Threads", "what is working on Threads", "top posts for", "competitor Threads profile", "niche scan", "follower count". Not for writing posts (use threads-post-writer).
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Cultivate edible and medicinal mushrooms from spawn through fruiting. Covers substrate preparation, inoculation methods, incubation conditions, fruiting chamber management, harvest timing, and successive flushes for oyster, shiitake, lion's mane, and other commonly cultivated species. Use when growing edible mushrooms without the risks of wild foraging, when a reliable supply of fresh culinary or medicinal mushrooms is needed, or when exploring mycelial ecology through hands-on cultivation practice.
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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.
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Chrome拡張機能の開発からChrome Web Store申請準備までをサポート。「拡張機能を開発」「Chrome拡張作成」などで使用。
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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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OpenClaw에 전달한 자연어 요청을 Claude Code 같은 외부 코딩 에이전트로 연결합니다. ACP나 acpx로 작업을 시작하거나 이어가며 대화 맥락을 유지합니다.
Node.js 실행을 일시 중지하고 변수와 실행 경로를 조사합니다. 비동기 작업 중단, 간헐적 테스트 실패, 메모리 증가와 CPU 부하의 원인을 찾는 데 사용합니다.
앱 개발을 계획·구현·평가 에이전트로 나누고, 실행 중인 앱의 화면과 기능을 검증하며 설정한 품질 기준이나 반복 횟수 상한까지 수정을 이어 갑니다.
공개 전 웹 페이지를 브라우저로 조작해 폼과 인증, 세 가지 화면 너비의 표시 차이, 속도 지표와 접근성을 점검하고 공개 가능 여부와 수정 사항을 정리합니다.
로컬 앱과 외부 브라우저의 화면을 조작합니다. 에이전트에게 클릭, 문자 입력, 메뉴와 대화 상자 조작, 스크린샷 촬영을 맡길 수 있습니다.
Check and fix hreflang tags, which identify language and regional versions of web pages for search engines. Review rendered HTML and HTTP responses to find missing reciprocal links, incorrect language codes, and fallback settings.
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