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Design agents that operate desktop or browser software through screenshots, mouse actions, and keyboard input. Compare ways to locate interface elements, diagnose coordinate errors, handle changing screens, and evaluate task completion, operating speed, and safety boundaries.
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Plan initialization, state saving, restoration, and cleanup around .NET MAUI app lifecycle events. Understand how Window events map to each platform, distinguish lost focus from backgrounding, and account for multiple windows.
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Check specific documentation pages against source code, one factual claim at a time. Receive verdicts showing supported, partly supported, contradicted, or unsupported statements, with code file and line references and suggested corrections.
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[omh] Deep dive before a decision: deep research engine - grounding for specs and decisions: study open-source reference implementations with pinned refs, gather live web evidence with citation discipline, verify contested claims, and distill a decision-grounding dossier that planning consumes; for a decision brief use research-brief, for upstream guidance use web-research. Use when the user says: research plan, literature review, research literature, review recent papers, deep research, deep-research, exhaustive research, saturation research.
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Establish what is actually true and current for the surface you are about to change — not just search. Grounding = locate the authoritative source and reconcile sources that disagree (code vs doc, migration vs schema, memory vs current code, live vs archived), not take the first hit. Use before any grep/find/explore of the codebase or docs, when deciding which of several definitions is the real one, or when a doc or memory conflicts with the code. Covers the source-of-truth hierarchy, reconciliation discipline, scoped ripgrep, and the docs/tags.yml + docsearch.py index.
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.NET MAUI app lifecycle guidance — the four app states, cross-platform Window lifecycle events (Created, Activated, Deactivated, Stopped, Resumed, Destroying), platform-specific lifecycle mapping, backgrounding and resume behavior, and state-preservation patterns. USE FOR: "app lifecycle", "window lifecycle events", "save state on background", "resume app", "OnStopped", "OnResumed", "backgrounding", "deactivated event", "ConfigureLifecycleEvents", "platform lifecycle hooks". DO NOT USE FOR: navigation events (use maui-shell-navigation), dependency injection setup (use maui-dependency-injection), platform API invocation (use conditional compilation and partial classes).
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Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.
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Grounding-theory primitives for common ground and repair. Use when deciding whether to ask a clarifying question, confirming a handoff was understood, or fixing multi-turn drift.
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Use when Agentforce run costs are climbing, you need to forecast scale, or you want to reduce tokens per conversation without hurting quality. Covers topic (now subagent) design impact on cost, prompt/template reuse, grounding size discipline, caching, and model-tier selection. Triggers: 'agentforce cost', 'tokens per conversation too high', 'reduce agentforce runs spend', 'forecast agentforce scale cost', 'einstein trust layer tokens'. NOT for capping spend per user with a budget gate and fallback — use agentforce/agent-rate-limit-strategy. NOT for org-wide model-tier and BYOLLM platform strategy — use architect/ai-platform-architecture.
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Router for the 53 SfSkills `agentforce` skill packages. Agentforce and Einstein: agents, topics, actions, prompt templates, grounding, guardrails, evaluation and production readiness. Use when the request mentions Agentforce, agent topic, agent action, prompt builder, Einstein, Trust Layer, grounding, RAG, guardrails, agent evaluation, prompt injection. Finds and opens the exact skill package to read; it does not contain the guidance itself.
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Forces Claude to extract exact quotes from files before making claims about them. Prevents hallucinations by grounding every statement in actual text from the codebase. Use when working with large files or unfamiliar code.
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Shared corpus-grounding engine — BM25 + structured filters + decision rules over CSV corpora via a domain manifest. Use when a skill needs grounded pre-action option-space constraints.
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Técnicas de estabilización: Anclaje (grounding), lugar seguro, técnica de contenedor y ejercicios de respiración. Técnicas inmediatas para malestar agudo y ataques de pánico.
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稳定化技术：着陆技术（Grounding）、安全岛/安全场所（Safe Place）、封存/保险箱技术（Containment）及呼吸练习。用于应对急性心理困扰和惊恐发作的即时干预技术。
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Техники стабилизации: Заземление (grounding), безопасное место, техника контейнирования и дыхательные упражнения. Экстренные техники при остром стрессе и панических атаках.
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Stabilization techniques: Grounding, safe place, containment, and breathing exercises. Immediate techniques for acute distress and panic attacks.
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Erstellt oder überarbeitet einen Skill FÜR DIESES ÖKOSYSTEM: delegiert die generische Interview-/Entwurfs-/Eval-Schleife an das offizielle skill-creator-Plugin (Anthropic, Apache-2.0), setzt aber zusätzlich die ellmos-Anforderungen durch, die das offizielle Plugin nicht kennt — Haus-Frontmatter (9 Felder), Kategorie/Sichtbarkeits-Regeln, anbieter-/nutzer-/ellmos-neutrale aber ellmos-sensitive Erkennung via grounding-seed, mitgelieferte config.json (inkl. Lernvertrag) + connections.json, Anbieter-/Vendor-Bewusstsein (State-of-the-Art-Alternativen inkl. eigener ellmos-Komponenten vorschlagen), einen Selbstaktualisierungs-Abschnitt UND einen wiederverwendbaren Lern-/Abdeckungs-Dateisatz (user-usecases.json, usecase-gaps.json, known-providers-and-abilities.json, gap-closing-analysis.json — an skill-creators evals.json und A2A/AgentSkill angelehnt, kein Parallelstandard). IMMER nutzen, wenn ein NEUER Skill für dieses System entstehen oder ein bestehender auf ellmos-Konformität nachgerüstet werden soll — auch bei "erstelle einen Skill", "bau mir einen Skill für X", "skill-creator nutzen", "neuen Skill anlegen", "diesen Skill ellmos-konform machen", "Lernvertrag/Verhaltenscodex für einen Skill anlegen", "Usecase-Abdeckung eines Skills prüfen". Für reines Skill-FINDEN/ROUTEN stattdessen skill-finder, für Landschafts-Audit skill-explorer.
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将收集好的素材逐步写成文章，提供多个展开方向供用户选择。先确认读者已有的知识，再按理解顺序解释概念，每次只写入选中的内容，并保留用户修改。
以文章、笔记、演示和文字记录为材料，保留作者语气，制作适合X、LinkedIn等平台的帖子、视频脚本及新闻简报，并按各媒体的形式调整结构。
使用SAM通过点或矩形框指定图像中的对象，生成用于抠图的区域掩码。指导自动生成、边界修正和质量筛选，也涵盖训练数据准备及模型部署步骤。
Prepare for one company's upcoming earnings announcement with a compact HTML report. It combines the latest earnings call, competitor comparisons, valuation measures, and recent news, with links to supporting sources and calculation details.
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Organize scattered concept notes into a map of recurring ideas. Merge duplicates, rank concepts into four tiers, trace how stronger ideas developed across sources, and assess which notes to keep, rewrite, merge, or remove.
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Design AI agent analytics workflows across distributed tables and documents. Combine Knowledge Catalog for metadata, meaning information about data and its usage rules, with managed Spark processing to produce architecture diagrams, deployment guidance, and validation plans.
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Organize paper reviews into individual concerns and draft responses within venue limits. Track evidence, unanswered issues, and approved commitments, then produce reviewer replies and a revision checklist that can be updated during follow-up rounds.
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