「db」の検索結果

Decide what to PERSIST in your data structures (graph nodes, DB tables, cache layouts) based on the QUERIES you must answer, not the DEFINITIONS that justify the data. Definitions tell you logical identity; storage strategy is independent. Materializing every definitional component leads to O(N×M) blow-ups that masquerade as memory leaks. Use when: (1) designing a new graph-node type, edge type, DB table, or cache layout; (2) about to name a `subgraph` / `comprises` / `members` / `contains` collection on a parent entity; (3) memory growth proportional to (entities × per-entity-elements); (4) "memory leak" debugging that survives multiple targeted patches — the leak may be storage shape, not retention; (5) pre-implementation sketch includes "for each X, store all reached nodes"; (6) research/spec doc says "X = ⟨A, B, C⟩" and you're tempted to make it three storage edges per X.

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。

Fix silent incorrect results in RFDB Datalog queries that share a variable between two edge atoms on opposite ends (e.g. self-join patterns like `edge(M, C, "T1"), edge(C, M, "T2")`). Use when: (1) a Datalog rule returns rows that look structurally wrong (e.g. a self-loop rule reports hits but pinning M to each reported id refutes the edge existence), (2) Cypher `MATCH (m)-[:R1]->(c)-[:R2]->(m)` returns the same bogus rows (engine-level bug, not Datalog-specific), (3) results change when you bind one variable explicitly vs. leave it free. Root cause: hash- join fast path in `eval_edge_hash_join` / `eval_incoming_hash_join` unconditionally rebinds the non-key Term::Var without checking whether the variable is already bound in the current row.

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。

Design guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.

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。

Fix silent metadata issues in RFDB node storage. Covers two traps: (1) metadata flattening — nested metadata fields become top-level after serialization, so node.metadata.field is undefined but node.field works. (2) reserved keys — fields named "type", "id", "name", "file", "exported" are silently stripped from metadata by _parseNode() to prevent overwriting top-level node fields.

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。

Fix node query issues in Grafema tests when nodes have numeric IDs instead of human-readable IDs, or when `type` field is undefined. Use when: (1) queryNodes returns nodes with numeric IDs like "52710336597754872375318185843222727675" instead of semantic IDs like "net:request#__network__", (2) node.type is undefined but node.nodeType has a value, (3) metadata is a JSON string instead of parsed object, (4) tests fail with "Cannot read property 'length' of undefined" after queryNodes. Covers RFDBServerBackend vs RFDBClient distinction and async generator handling.

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。

Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.

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。

Data migration and synchronization to SurrealDB from MongoDB, PostgreSQL, MySQL, Neo4j, Kafka, and JSONL. Full and incremental CDC sync. Part of the surreal-skills collection.

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。

改修後の動作確認を、AIが自分で環境を立てて最後まで進めるための検証スキル。CLI（curl / php evo / DB）を優先し、画面操作でしか判定できないときだけ Playwright（既定はヘッドレス）に切り替える。Docker の検証環境の起動、インストール、テスト用ユーザー作成、mailpit でのメール確認、main との比較、後片付けまでを扱う。「動作確認」「自走で確認」「実環境で確かめる」「ブラウザで確認」「再現を確認」「Playwright」と依頼されたとき、または修正のあとに実機での裏付けが必要なときに使う。

DB格式Profiling数据场景化性能拆解：自动完成场景确认（框架/任务类型判定）、阶段拆解与单次执行边界拆解，并给出报告。Use when 需要对昇腾 Profiling DB 做阶段拆解、step 归因或生成性能报告, or when the user says "帮我按任务阶段拆解这个Profiling数据", "多层次性能耗时拆解", "分析这个 vllm/sglang/verl 数据，主要耗时都花费在哪里了"。

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。

分布式训练梯度尖刺 (Gradient Spike) 根因定位。基于梯度监控数据 (trend.db/CSV)、 msprobe dump 统计数据，三阶段渐进式从异常坐标定位逐层深入到算子级根因。 当用户明确指明分析梯度尖刺问题时使用此 skill。 即便用户提供以上数据，但未指明分析梯度尖刺问题时，不可使用此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。

Do work in an Upstash Box, a sandboxed cloud container driven through the remote Upstash MCP server (mcp.upstash.com), instead of on the local machine. Use when the user asks to run, build, test, clone, or edit something remotely, in a sandbox, in the cloud, or in a box, when the deliverable is a pull request, a public preview URL, or a screenshot of a running app, when the local machine cannot deliver (no GitHub login for gh, no way to expose a port, a dirty or slow local checkout), when several independent tasks should run in parallel on separate machines, a code factory that turns a list of tasks into a list of pull requests, or when the deliverable is a video, screen recording, demo or timelapse of a browser, web app, terminal program or agent run. Applies whenever the session has the box_* and blob_* MCP tools, even when Upstash is not named.

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。

Work with the @upstash/box TypeScript/JavaScript SDK for sandboxed cloud containers with AI agents, shell, filesystem, git, cron schedules, snapshots, and a headless browser. Use when building with Upstash Box, creating a sandbox or isolated environment to run untrusted or agent-generated code, running AI coding agents in containers, giving an agent a cloud dev environment with a shell and repository, browser automation from a box, scheduling recurring jobs inside a box, saving and restoring snapshots, or orchestrating parallel boxes.

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。

Work with the upstash-box Python SDK for sandboxed cloud containers with AI agents, shell, filesystem, git, cron schedules, snapshots, and a headless browser. Use when building with Upstash Box in Python, creating a sandbox or isolated environment to run untrusted or agent-generated code, running AI coding agents in containers, giving an agent a cloud dev environment with a shell and repository, browser automation from a box, scheduling recurring jobs inside a box, saving and restoring snapshots, or orchestrating parallel boxes.

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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

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 receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.

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 receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

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。

Investigates phishing email incidents from initial user report through header analysis, URL/attachment detonation, impacted user identification, and containment actions using SOC tools like Splunk, Microsoft Defender, and sandbox analysis platforms. Use when a reported phishing email requires full incident investigation to determine scope and impact.

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。

Run Phase 4 (feedback routing) after an adversarial review FAIL. Routes adversary findings to the earliest affected phase, checks iteration limits, and escalates to human if limits are exceeded.

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。

AbuseIPDB API reference. IP reputation and abuse report lookups.

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 you need authoritative classification, threat-name, MITRE ATT&CK mapping, dynamic-analysis or sandbox results on a file hash, or when you need network threat intelligence for a URL/domain/IP from ReversingLabs Spectra Analyze (A1000). Returns verdict, risk score, AV detection ratio, threat name, behavioural tags, and pivot candidates (parent containers, extracted files, related samples by family). Commonly invoked by /hash-investigation and /malware-analysis. Other agents/skills can chain this for deeper malware enrichment beyond VirusTotal.

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。

Assess, adapt, and apply specified discussions, feedback, findings, or references to existing project work. Preserve coverage, corrections, and selected intent; judge fit and carry adopted effects through the authorized result. Includes technical review feedback; incidental sources or understanding material without a target-work adoption question need no separate incorporation workflow.

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。

Investigate Azure incidents and service degradations with a read-only workflow using Azure CLI, Resource Health, Activity Log, metrics, resource logs, and official Microsoft docs. Use when troubleshooting Azure outages, Resource Health alerts, Azure Firewall or DNS failures, ExpressRoute or BGP instability, VM availability problems, AKS incidents, App Service failures, database connectivity issues, Entra ID sign-in problems, load balancer issues, platform-vs-workload cutover, or drafting a troubleshooting report. Triggers on 'Azure トラブルシューティング', '障害調査', '切り分け', 'Resource Health', '復旧確認', 'Azure Firewall', 'DNS', 'ExpressRoute', 'VPN Gateway', 'BGP', 'AKS', 'App Service', 'SQL', 'PostgreSQL', 'Cosmos DB', 'Entra ID', 'incident', 'degraded', 'unavailable'.

"Apply Bayesian averaging to rank items by combining observed ratings with prior expectations. Use this skill when the user needs to rank items with varying review counts, build a 'top rated' list that handles low-sample items fairly, or implement IMDB-style weighted rating — even if they say 'weighted average rating', 'IMDB formula', or 'ranking with prior'.".

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。

Controls a local browser from a sandboxed remote machine. Use when the agent is running in a sandbox (no GUI) and needs to navigate websites, interact with web pages, fill forms, take screenshots, or expose local dev servers via tunnels.

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。