「db」の検索結果

Analyze CSAT, NPS comments, reviews, and support feedback for sentiment, recurring themes, customer pain, and service improvement actions. Use for customer support and CX feedback reviews.

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Read a Prisma Next structured error envelope and route to the right recovery — code, domain, severity, why, fix, meta. Use for error, exception, my emit failed, my query won't typecheck, my query crashed, my migration won't apply, MIGRATION.HASH_MISMATCH, BUDGET.ROWS_EXCEEDED, BUDGET.TIME_EXCEEDED, RUNTIME.ABORTED, PLAN.HASH_MISMATCH, CONTRACT.MARKER_MISSING, PN-RUN-3001, PN-RUN-3002, PN-RUN-3030, PN-MIG-2001, PN-CLI-4011, PN-SCHEMA-0001, drift, capability missing, planner conflict, prisma studio, EXPLAIN, query log, db.end, db.close, script won't exit, hangs, close connection, pool.end, client is closed.

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Review and resolve PR review comments on the current branch's PR in this Flutter repository, from both AI reviewers (CodeRabbit, CodiumAI, CodePilot, Copilot, etc.) and human reviewers. Fetches comments via `gh`, splits them into AI and human groups, verifies each comment against the actual Flutter code, triages them, proposes fixes, and optionally applies fixes, replies to comments, and pushes. Use when the user asks to review PR comments, resolve PR feedback, review AI comments, review human review comments, or handle code review feedback.

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WatermelonDB reactive local database for React Native - schema, models, decorators, reactive queries, relations, writers/readers, batch operations, migrations, sync

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Code review patterns, feedback principles. Use when reviewing PRs, implementations, or making approval/rejection decisions. Covers self-correction, progress tracking, feedback principles, severity levels.

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SurrealDB multi-model database - SurrealQL queries, record links, graph relations, live queries, schema definitions, authentication, TypeScript SDK

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CockroachDB distributed SQL -- transaction retries, multi-region, online schema changes, follower reads, PostgreSQL compatibility gaps

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Detects sensitive data stored insecurely on mobile devices (Android/iOS). Trigger on: SharedPreferences, NSUserDefaults, SQLite, Room DB, DataStore, Core Data, Keychain misconfiguration, external storage, backup exposure, plaintext files, unencrypted databases, adb backup, iCloud backup, NSFileProtection, EncryptedSharedPreferences, SQLCipher, allowBackup, FLAG_SECURE, keyboard cache, sensitive logs. Covers MASVS-STORAGE-1 (local storage) and MASVS-STORAGE-2 (exposure to unauthorized actors).

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Execute and analyze malware samples within a highly controlled, instrumented sandbox environment to observe their true behavior, network communications, file system modifications, and registry changes in real-time.

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Browser and V8 exploitation playbook. Use when exploiting JavaScript engine vulnerabilities including JIT type confusion, incorrect bounds elimination, and V8 sandbox bypass to achieve renderer RCE and sandbox escape in Chrome/Chromium.

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Identify and simulate Data Poisoning attacks aimed at degrading or skewing an AI model's accuracy. This skill focuses on Adversarial Machine Learning concepts where attackers inject malicious or mislabelled data points into training or fine-tuning datasets (e.g., feedback loops) to bias the AI.

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Summarizes runtime behavior of a sample from Procmon-style operation logs — process and thread creation, file and registry writes, and network-related operations — to build a behavioral profile. Activates for requests to analyze Procmon output, summarize sample behavior, or profile process/file/registry activity from a sandbox run.

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三大闭环日常健检（v2.0）。快速读取系统状态文件（约1分钟），从三个层次输出健康仪表盘：①运营指标层（PENDING-EXPERIENCES/碎片积压/一致性检查日期/cascade/PENDING-SKILLS）②沙盘状态层（sandboxes/目录的 validated/draft/gap-found 分布）③D0覆盖率层（role-* Skills 有多少已有认知根行）。触发词：「三大闭环健康检查」「全系统健康报告」「整体系统健康」「三个闭环都健康吗」「三大闭环状态怎么样」。⚠️ 本 Skill 为快速只读健检，无 Full 模式。深度场景沙盘验证→skill-closure-verifier-meta；生成场景沙盘→scenario-sandbox-builder；单一Loop专项→对应专项Skill。

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从现有 role-* Skills 动态发现所有任务场景，为每个场景生成包含「认知根 D0 节点」和「认知反馈节点」的完整大闭环沙盘文件（status: draft），供 skill-closure-verifier-meta 做 Phase 2 验证。⚠️ 与 skill-sandbox-expander 的区别：后者从已有域的缺口扩充；本 Skill 从零发现所有场景，生成覆盖全体系的初始沙盘库。触发词：「场景沙盘生成」「为当前体系生成场景沙盘」「发现所有任务场景并建沙盘」「场景化沙盘覆盖」「从零建沙盘库」。

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Best practices for building and maintaining applications with PowerSync: Cloud and self-hosted setup, sync configuration, client SDK usage, backend integration (Supabase, custom Postgres, MongoDB, Azure DocumentDB, MySQL, MSSQL), schema changes, watch and reactive queries (useQuery), attachments, the upload queue, and debugging. Use this skill whenever the user mentions PowerSync, a @powersync/* package (@powersync/web, @powersync/react-native, @powersync/node), the powersync Flutter/Dart package, the powersync Rust crate, wa-sqlite, offline-first sync, local-first architecture, sync rules, sync streams, uploadData, fetchCredentials, disconnectAndClear, logout or user switching in a synced app, real-time data replication, Electric Cloud migration, Electric Shapes, an app stuck on Syncing, data not syncing, or wants to add offline-capable sync to a mobile or web app, even if they do not explicitly name PowerSync.

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Update user personas from new feedback, pilot data, or research. Use this skill when: - New pilot data or feedback reveals changed user behaviors or pain points - Annual persona review is due (1-2x per year) - New user segments are discovered that need persona documentation - Existing personas need validation against recent data

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Critique a design for usability, hierarchy, and accessibility. Use when the user says "critique this design", "review this screen", "what's wrong with this UI", "design feedback", "UX review", "how can I improve this", "review my mockup", "is this good UX", or shares a design and wants structured feedback - even if they don't explicitly say "design critique".

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Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).

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Plans, configures, and hardens platform-level Google Kubernetes Engine (GKE) cluster security. Covers cluster add-ons (Secret Manager enablement), RBAC hardening (disabling insecure bindings, audit tools), Binary Authorization, enabling Shielded Nodes, GKE Sandbox cluster enablement, GKE IAM roles, and cross-service authentication IAM patterns. Use when securing cluster control planes, hardening GKE RBAC, enabling Shielded Nodes, enabling GKE Sandbox runtime, enabling cluster-wide security add-ons, or managing GKE IAM roles. Don't use for workload-level security (Workload Identity, SecretProviderClass, PSS, NetPol, gVisor pod runtimeClassName; use gke-workload-security instead).

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Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.

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Always use this skill before claiming any work is done, fixed, passing, or complete — even if it seems obvious. Also use when receiving code review feedback from any source (human or automated), when finishing a task or major feature, before committing or creating a PR, or when about to express satisfaction with work. Covers three practices: receiving feedback with technical rigor (no performative agreement), triggering code review via /code-review command or cook/fix pipelines, and verification gates (run the command, read the output, then make the claim). Never skip — the cost of false completion claims is higher than the cost of verifying. References: code-review-reception.md, requesting-code-review.md, verification-before-completion.md.

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Use when practitioners need to understand system-managed fields (CreatedDate, LastModifiedDate, SystemModstamp, CreatedById, LastModifiedById, IsDeleted) — their update behavior, indexing, and queryability. Triggers: 'difference between SystemModstamp and LastModifiedDate', 'how to query deleted records', 'set CreatedDate during data migration', 'why does LastModifiedDate not match SystemModstamp', 'Create Audit Fields permission'. NOT for a field's value-change history — use data/field-history-tracking. NOT for Shield Field Audit Trail — use security/field-audit-trail.

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Use this skill when writing acceptance criteria for Salesforce features, structuring UAT test scripts from user stories, classifying defects found during UAT, or planning regression testing before a Salesforce release. Trigger keywords: UAT, user acceptance testing, test script, acceptance criteria, defect classification, regression testing, test plan, test case. NOT for Given/When/Then AC syntax — use admin/acceptance-criteria-given-when-then. NOT for the per-case field schema and AC-to-case decomposition — use admin/uat-test-case-design. NOT for automated Flow or Apex test coverage — use flow/flow-testing. Also covers: UAT plan record, sandbox selection for UAT, pre-UAT environment checklist, sandbox email deliverability for testers, test-user personas, negative and bulk coverage, defect triage categories, go/no-go sign-off, quick-deploy window.

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Redact PII in your own code before it reaches Agentforce prompts, models, and logs. Trigger keywords: agentforce pii, pii redaction, data masking llm, prompt pii filter, audit pii leakage. NOT for turning on the platform's own masking, zero-retention and audit-trail controls — use agentforce/einstein-trust-layer. NOT for masking PII in a refreshed sandbox — use security/sandbox-data-masking.

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。