「tsp」の検索結果

Anbieterneutrales Onboarding-Protokoll für einen neuen, neu aufgesetzten oder ausgetauschten Arbeitsplatzrechner. Es richtet Betriebssystemvoraussetzungen, Agentenlaufzeiten, gemeinsame Regelflächen, portable Skills, verifizierte Konfiguration und Nachweise nach der Installation ein, ohne Zugangsdaten, private Prompts oder host-spezifische Konfiguration in ein Repository zu kopieren.

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。

Erweitert automation-self-care um Letter Hooks, Preflight-Bootloader, Dokument-Traversierungsregeln und selbstheilende Prompt-Kontext- Anreicherung für KI-Agenten und CLIs ohne native, ereignisgetriebene JSON-Lifecycle-Hooks (wie Antigravity / Gemini CLI). Nutzen, wenn ein Agent Preflight-Regeln injizieren, vor Arbeitsbeginn Memory/Gardener durchsuchen, Verzeichnis-Dokument-Lesestrategien erzwingen (CLAUDE.md / AGENTS.md) oder Sidecar-Tasks dynamisch an Skills und Sicherheitsprotokolle routen soll.

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。

Providerneutrale Operator- und Teamleader-Rolle für komplexe, länger laufende oder planbasierte Vorhaben. Verwenden bei `$operator`, `/operator`, "übernimm als Operator" oder "arbeite den Plan weiter ab", um den nächsten substanziellen Planschritt zu wählen, unabhängige Arbeitspakete kontrolliert zu delegieren und Ergebnisse evidenzbasiert abzunehmen und zu integrieren.

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。

Verhaltensaktivierung bei Depression: Den Teufelskreis durchbrechen, Aktivitätsprotokoll, Wochenplanung und wertebasierte Aktivitäten.

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。

Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code int...

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。

Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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。

Use when working with Codescene — codeScene behavioral code analysis including code health scoring, hotspot detection, change coupling analysis, and PR integration for code quality gates. Use when configuring CodeScene for codebase visualization, technical debt prioritization, and automated code review feedback.

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。

SonarQube code quality and security analysis. Use when working with code quality metrics, security hotspots, quality gates, or issue tracking in SonarQube Cloud or Server.

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。

Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists or non-security ownership questions.

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。

Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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。

Implement App Intents for Siri, Shortcuts, Spotlight, widgets, Control Center, and Apple Intelligence on iOS. Covers AppIntent actions, AppEntity and EntityQuery models, AppShortcutsProvider phrases, IndexedEntity Spotlight indexing, WidgetConfigurationIntent, SnippetIntent, and assistant schemas. Use when exposing app actions or entities to system surfaces.

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。

Analyze Swift and mixed-language compile hotspots using build timing summaries and Swift frontend diagnostics, then produce a recommend-first source-level optimization plan. Use when a developer reports slow compilation, type-checking warnings, expensive clean-build compile phases, long CompileSwiftSources tasks, warn-long-function-bodies output, or wants to speed up Swift type checking.

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。

Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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。

Plot per-gene mutation distributions on a protein-domain map (lollipop / needle plots) showing mutation position, recurrence count, and variant classification with maftools, g3-lollipop, trackViewer, and ProteinPaint. Use when visualizing recurrent mutation hotspots on a single gene's protein, marking domain boundaries from UniProt/Pfam, comparing missense vs truncating distributions, or contrasting two cohorts on the same lollipop.

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。

Restructure existing code while preserving observable behavior. Use for targeted technical debt or hotspot work with characterization tests.

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。

Use to create, update or verify the Lean 4 executable specification under lean/ from the exploration documents (event-timeline, hotspots, ubiquitous-language, ux-review, model-review) — initial generation, reflecting a session's results, fixing lake build, adding a command or a screen, checking a proposed feature against the model. Formalization questions go to model-review.md as MQ, never to the expert directly.

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。

Use to run or continue domain exploration with the product owner as domain expert — event storming interviews, hotspots (HS), ubiquitous language, UX review of the domain, checking or resolving open MQ/UX items, or a status summary of documents/ddd. Also use when the user starts explaining their business, purpose or workflow. Never edit documents/ddd directly; go through this skill.

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。

Plot per-gene mutation distributions on a protein-domain map (lollipop / needle plots) showing mutation position, recurrence count, and variant classification with maftools, g3-lollipop, trackViewer, and ProteinPaint. Use when visualizing recurrent mutation hotspots on a single gene's protein, marking domain boundaries from UniProt/Pfam, comparing missense vs truncating distributions, or contrasting two cohorts on the same lollipop.

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。

Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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。

Handoff text-spacing extractor SKILL — 從 canonical HTML/JSX/TSX inline style 盡量抽出字間距與空間細節（letterSpacing, gap, padding, margin, fontSize, lineHeight 等），並回補到 Design System/design_handoff/source/ui-design-tokens.json 的 handoffTextSpacingExtracted 區段。USE FOR: UI 對齊前先把參考圖 / handoff source 的 spacing recipe 轉成可重用 token；DO NOT USE FOR: 只有 runtime 截圖、沒有 source 設定，或純 CSS module / computed style 無法直接讀取的場景。

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。

Quickly understand an unfamiliar codebase or project by mapping its architecture, identifying key components and data flows, and surfacing complexity hotspots. Load when the user asks to understand a repo, explain how something works, map the architecture, onboard to a codebase, or explore how components connect. Also triggers on "walk me through this codebase", "how does this project work", "explain the architecture", "what does this repo do", "show me the structure", "onboard me", or any request to build a mental model of a codebase before making changes.

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。

Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.

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。

Plot per-gene mutation distributions on a protein-domain map (lollipop / needle plots) showing mutation position, recurrence count, and variant classification with maftools, g3-lollipop, trackViewer, and ProteinPaint. Use when visualizing recurrent mutation hotspots on a single gene's protein, marking domain boundaries from UniProt/Pfam, comparing missense vs truncating distributions, or contrasting two cohorts on the same lollipop.

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。

See, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.

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。