'db' 검색 결과

Manage isolated working environments in OpenShell using its command-line tool. Register gateways, create and connect to sandboxes, configure credential providers, and reuse workload templates with shared images, environment settings, and computing resources.

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• Create and connect to a sandbox
• Configure and refresh provider credentials
• Inspect and import provider profiles

Run Python code and Shell commands in an isolated Cube Sandbox environment. The skill covers file access, host directory mounting, network restrictions, and pausing and resuming execution while preserving the sandbox's memory state.

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• Running untrusted code in an isolated environment
• Reading and writing sandbox files
• Restricting outbound network access

Build, test, and format CubeSandbox components using make targets and a Docker builder. Also covers the web dashboard’s development workflow, API type synchronization, and troubleshooting for missing services, outdated build tools, and environment requirements.

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• Build all components or a specific CubeSandbox component
• Run component tests or a single targeted test
• Format Go and Rust code and lint the web interface

Query EpiGraphDB for trait classifications, related literature, genetic evidence, and gene–drug relationships. Get concise summaries with limited result counts, or save the complete response when you need the underlying data.

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• Find ontology terms for a disease or trait
• Look up drugs associated with a gene
• Summarize literature and genetic evidence

Use this skill when you develop, design, build, deploy, debug, migrate, or troubleshoot CloudBase (腾讯云开发, 云开发, TCB, 微信云开发) projects — Web, 微信小程序, 小程序, uni-app, mobile (iOS, Android, Flutter, React Native). Covers UI (页面, 界面, 表单, dashboard, prototype, 原型); auth (登录, 注册, OAuth, publishable key); databases (NoSQL 文档数据库, MySQL 关系型数据库, PostgreSQL/CloudBase PG, app.rdb(), queryPgDatabase/managePgDatabase, CRUD, security rules); 云函数/cloud functions (serverless, scf_bootstrap); CloudRun (云托管, Dockerfile); 云存储; built-in AI (内置大模型, AI 对话, streaming, 流式输出, 图片生成, generateText, streamText, createModel, generateImage, TokenHub, Hunyuan, DeepSeek, GLM, Kimi, Token Credits 资源包, 小程序成长计划); third-party/custom model onboarding (第三方大模型接入, 大模型调用, LLM API); AI agent (智能体, AG-UI, LangGraph); ops troubleshooting (巡检, 诊断, 日志); spec workflow (需求文档, 技术方案, requirements, tasks.md). Do NOT use for non-CloudBase projects, pure frontend without CloudBase, or self-hosted backends without CloudBase.

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SKL_plc_db_sync（旧 ai-plc-db-sync）- AI-PLCのローカルDBとNotion DBをpull、push、sync、statusで双方向同期する。

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ScalarDB Analytics設計エージェント - ScalarDB Analyticsを使用した分析基盤の設計。Apache Sparkベースの分析クエリ、マルチDB横断分析を策定。/design-scalardb-analytics [対象パス] で呼び出し。

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Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

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ScalarDB Analytics設計エージェント - ScalarDB Analyticsを使用した分析基盤の設計。Apache Sparkベースの分析クエリ、マルチDB横断分析を策定。/design-scalardb-analytics [対象パス] で呼び出し。

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Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.

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• Build agent memory that reuses past experiences
• Record and assess sequences of agent actions
• Consolidate similar experiences into reusable patterns

Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.

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• Consolidating separate agent memory systems
• Migrating SQLite and Markdown data to AgentDB
• Finding stored information with similar meaning

Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.

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• Build agent memory that reuses past experiences
• Record and assess sequences of agent actions
• Consolidate similar experiences into reusable patterns

Plan and implement a unified AgentDB memory backend for agents. Migrate SQLite and Markdown data, add HNSW indexing for similarity search, and support shared memory and SONA learning patterns while preserving compatibility with existing systems.

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• Consolidating separate agent memory systems
• Migrating SQLite and Markdown data to AgentDB
• Finding stored information with similar meaning

Store and retrieve knowledge across agent sessions with AgentDB. Organize memories into tiers, search learned patterns, combine related memories into context, and record causal links to support later work.

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• Reuse knowledge from previous agent sessions
• Store and search learned patterns
• Combine related memories into context

Debug programs with LLDB while keeping session state across commands. Set breakpoints, inspect variables and memory, and examine core dumps, with JSON results for agents and a debug adapter interface for editor clients.

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• Inspect variables where a program stops
• Investigate a crash using a core dump
• Debug a running process

Analyze Windows identity and host evidence in CTF sandboxes. Connect authentication tickets, credentials, mailbox rules, and host activity to explain which services accept access and how movement between hosts occurs.

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• Tracing authentication tickets and tokens
• Investigating mailbox rules alongside identity events
• Building a timeline of Windows host evidence

Analyze Android apps in a CTF sandbox using static inspection and Frida hooks, which observe or alter running code. Trace request signing, local storage, and Java-to-native boundaries to reproduce a request accepted by the server.

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• Tracing request-signing inputs and outputs
• Inspecting Java-to-native calls with runtime hooks
• Examining app settings, databases, and token storage

Build data quality checks with Great Expectations, dbt tests, and data contracts, which define shared expectations between teams. Check missing values, duplicates, and freshness, then integrate validation into data pipelines and CI/CD.

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• Check data for missing values and duplicates
• Build a dbt test suite
• Define shared data contracts between teams

Observe malware in an isolated Cuckoo Sandbox environment and review its processes, network traffic, and system changes. Use the findings to trace infection stages, identify persistence mechanisms, and extract indicators of compromise for behavioral reports.

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• Investigating suspicious files after static analysis
• Tracing multi-stage infections and persistence mechanisms
• Extracting indicators of compromise from observed behavior

Create a feedback form in Google Forms, retrieve its respondent link, and send that link through Gmail. The workflow connects form creation with an email invitation asking recipients to share feedback.

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• Create a form for event feedback
• Retrieve the form's respondent link
• Email participants a feedback request

Install and configure the memory-tencentdb plugin for OpenClaw to retain and retrieve conversation information. Verify the setup through logs and searches, adjust memory settings, and troubleshoot missing recall or vector search results.

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• Enable long-term memory in OpenClaw
• Adjust recall and retention settings
• Investigate saved memories that cannot be retrieved

Plan and configure security across Google Kubernetes Engine clusters. Review access permissions, enable Secret Manager integration, and configure protections such as Shielded Nodes and Binary Authorization to strengthen the platform supporting your applications.

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• Review cluster access permissions
• Enable Secret Manager integration
• Configure Shielded Nodes and GKE Sandbox

Add business knowledge missing from a Wren project's database definitions. Compare source documents with existing context, then add explanations, rules, SQL examples, and proposed metrics through guided questions or an automatic mode with an audit report.

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• Documenting status codes, units, and NULL meanings
• Adding business knowledge from handbooks and glossaries
• Defining aggregation metrics such as ARR or DAU

Monitor model training through WandB metrics to detect invalid numbers, worsening loss, and declining evaluation results. Ambiguous trends receive a Codex review, while stopped runs retain key metrics and the reason for stopping.

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• Check loss and evaluation trends during model training
• Detect NaN values and diverging loss
• Review flat or noisy training metrics