「deep」の検索結果

**WORKFLOW SKILL** — Persist analysis so it survives context compression and chains between tasks. USE FOR: any task whose decisions affect future work — features, refactors, architecture changes, integrations. Produces a lightweight Task Analysis Map that externalizes intent, decisions, and chain context. Use to document decisions, create continuity, prevent context loss, bridge tasks, externalize reasoning. DO NOT USE FOR: quick intent validation (use task-intent), deep structured analysis (use contextação).

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**WORKFLOW SKILL** — Understand before implementing. USE FOR: any task where the agent might rush to code — forces understanding WHY/WHAT FOR/FOR WHOM before defining a solution, activates planning and reasoning discipline, prevents technical debt from shallow requests. Use to validate intent, plan before coding, reason before acting, challenge assumptions, prevent premature implementation. DO NOT USE FOR: deep multi-technology analysis (use contextação), persisting decisions across tasks (use task-map).

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Answer an explicitly vault-scoped question from an Obsidian wiki without changing it. Use when the user selects the vault as the evidence source: query the wiki, query quick, query deep, explain from the wiki, summarize the vault, find in wiki, search the wiki, or based on the wiki. Do not route ordinary general-knowledge questions here.

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Apply the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to assumptions. This is a reasoning review, not a deterministic vault health check or repair workflow.

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Deep cultural adaptation of an already translated blog post for one target market: local examples, CTAs, legal references, statistic sources, and formality (Sie/du, tu/vous). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets. Use when the user says "localize blog", "cultural adaptation", "adapt for Germany", "lokalisieren", "localiser", "adaptar" and a translation already exists. Not for translating (use blog-translate), auditing a folder of translations (use blog-locale-audit), or the full write-translate-localize pipeline (use blog-multilingual).

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多源深度调研与专业报告生成。适用场景广泛——只要用户的问题涉及需要深度分析的专业话题，就应使用此技能。包括但不限于：(1) 用户明确要求调研/research/综述/报告/发现；(2) 用户提出一个技术或科学话题，话题复杂度需要多源深度分析；(3) 用户要求对比多种技术方案的优劣；(4) 涉及生物医药、蛋白质、基因、药物靶点等需要专业数据库支撑的问题。核心能力：根据问题性质自动组合 arXiv 论文检索 + ToolUniverse 科学工具（UniProt/OpenTargets/PubMed/FAERS 等 1000+ 工具）+ Web 搜索，多源采集数据，交叉分析，生成专业 PDF/DOCX 研究报告。

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Summarize and critically analyze academic papers supplied as PDFs, text, arXiv links, or journal URLs. Ground claims in the inspected version and source locations; support focused questions, deeper appraisal, and cross-paper comparison.

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Convene the Council of High Intelligence — multi-persona deliberation with historical thinkers for deeper analysis of complex problems.

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Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.

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Expert guidance for deep security incident and entity investigations in Google SecOps. Use when investigating cases, analyzing entities (hosts, IPs, domains, hashes, users), extracting and searching UDM events, performing asset and user timeline analysis, and detecting lateral movement across enterprise networks. Don't use for detection rule authoring or YARA-L tuning (use secops-detection-engineering), proactive hypothesis-driven hunting (use secops-hunt), initial alert triage (use secops-triage), or basic case status updates (use secops-cases).

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Author, validate, test, and deploy YARA-L 2.0 detection rules and evaluate end-to-end detection coverage gaps in Google SecOps. Use when writing new detection rules, tuning existing rules, validating syntax, testing logic against historical telemetry, or evaluating detection coverage against threat intelligence blogs, CVE disclosures, and Threat Detection Opportunities (TDOs) using synthetic UDM events and long-running coverage analysis. Don't use for alert triage (use secops-triage), deep forensic event reconstruction on infected hosts (use secops-investigate), or case management operations (use secops-cases).

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Expert guidance for security alert triage in Google SecOps. Use when investigating and triaging security alerts, determining false positives vs. true positives, assessing entity risk, adjusting alert severity or priority, and closing or escalating alerts and cases. Don't use for deep multi-hop incident investigations across host timelines (use secops-investigate), proactive threat hunting or retroactive IoC sweeps (use secops-hunt), or authoring new detection rules (use secops-detection-engineering).

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Expert guidance for proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authoring (use secops-detection-engineering).

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Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability instead).

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Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

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Phase 1 of the SRS workflow. User gives a project topic → AI runs a deep, categorized brainstorm (5 mandatory rounds) to fully understand actors, features, scope boundary, tech constraints, and business rules before writing anything.

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Phase 1 of the SRS workflow. User gives a project topic → AI runs a deep, categorized brainstorm (5 mandatory rounds) to fully understand actors, features, scope boundary, tech constraints, and business rules before writing anything.

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Cross-tool decision matrix for field-update automation in Salesforce — Before-Save Flow vs After-Save Flow vs Apex Trigger vs the deprecated Workflow Rule + Field Update. Covers the recursion / re-entrancy rules, governor cost per pattern (Before-Save flow is governor-free for the same record), the order-of-execution slot each tool occupies, and the Workflow-Rule-to-Flow migration playbook for field-update actions. NOT for the full record-save order of execution — use apex/order-of-execution-deep-dive. NOT for building the record-triggered Flow once the tool is chosen — use flow/record-triggered-flow-patterns. Trigger keywords: WorkflowFieldUpdate, reevaluateOnChange, onCreateOrTriggeringUpdate, doesRequireRecordChangedToMeetCriteria, failedMigrationToolVersion, Migrate to Flow, workflow-meta.xml, field update deprecated, double writer, second trigger pass.

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Use when designing or maintaining the role hierarchy as a record-access mechanism. Trigger keywords: role hierarchy, manager cannot see subordinate records, grant access using hierarchies, role reparenting, portal role, how deep should the role hierarchy be, design the role hierarchy. NOT for territory models - use admin/enterprise-territory-management. NOT for rule-based grants - use admin/sharing-rules. NOT for the overall model - use admin/sharing-and-visibility.

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Use this skill when setting up, configuring, or managing Salesforce Lightning Knowledge — enabling the feature, designing record types on Knowledge__kav, configuring Data Categories for organization and visibility control, setting up publishing workflows, and layering approval processes. Trigger keywords: Lightning Knowledge setup, Knowledge article record types, Data Category visibility, Knowledge publishing workflow, Knowledge__kav configuration. NOT for converting Classic Article Types to Lightning — use admin/knowledge-classic-to-lightning. NOT for bulk-loading articles from an external help center — use data/knowledge-article-import. NOT for sizing the taxonomy itself (how many groups, how deep) — use architect/knowledge-taxonomy-design.

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Deep code review with web research. Use when double-checking code against latest docs, verifying dependency versions, or reviewing security concerns. Complements automatic quality hook with ecosystem verification.

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AI ผู้จัดการเวลา — Pomodoro, Time Block, GTD, Deep Work, Calendar Design สำหรับผู้ที่อยากควบคุมเวลาได้จริง

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Deep learning framework development with tinygrad - a minimal tensor library with autograd, JIT compilation, and multi-device support. Use when writing neural networks, training models, implementing tensor operations, working with UOps/PatternMatcher for graph transformations, or contributing to tinygrad internals. Triggers on tinygrad imports, Tensor operations, nn modules, optimizer usage, schedule/codegen work, or device backends.

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Firmware development for the 30-pin ESP32-WROOM-32 development board — the DOIT ESP32 DevKit V1 / ESP32 CH340 Type-C / NodeMCU-ESP32 30-pin form factor, 15 pins per side, CH340G or CP2102 USB-UART bridge, AMS1117 LDO, user LED on GPIO2. Use when working on a 30-pin ESP32 devkit (ESP32-D0WDQ6 / ESP32-D0WD-V3, ESP-WROOM-32 module): project setup, platformio.ini and sdkconfig for ESP-IDF or Arduino, pin mapping and which GPIOs are safe, strapping pins, ADC1 vs ADC2 with Wi-Fi, SPI speed and IO_MUX pins, I2C, deep sleep and RTC wake pins, flashing over the CH340 bridge, BOOT/EN recovery, brownouts, or debugging why something on the board does not work.

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。