「vin」の検索結果

Use Exasol Cloud Storage Extension workflows for moving structured object-storage files through extension UDFs. Covers setup prerequisites, the fixed `CLOUD_STORAGE_EXTENSION.IMPORT_PATH` and `CLOUD_STORAGE_EXTENSION.EXPORT_PATH` entrypoints used with `FROM SCRIPT` and `INTO SCRIPT`, Parquet, Avro, ORC, and Delta files on S3, Google Cloud Storage, Azure Blob Storage, Azure Data Lake, HDFS, and Alluxio, connection-object credential patterns, and routing away from native IMPORT or EXPORT when the extension path is the right fit.

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。

Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.

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。

Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean. Use when the user asks to simplify, declutter, reduce noise, remove elements, or make a UI cleaner and more focused.

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 convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.

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。

Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.

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。

Strip designs to their essence by removing unnecessary complexity. Great design is simple, powerful, and clean. Use when the user asks to simplify, declutter, reduce noise, remove elements, or make a UI cleaner and more focused.

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 convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.

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。

Tones down visually aggressive or overstimulating designs, reducing intensity while preserving quality. Use when the user mentions too bold, too loud, overwhelming, aggressive, garish, or wants a calmer, more refined aesthetic.

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。

Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.

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 an autonomous triage-resolve-review-merge loop to auto-pilot the GitHub issue backlog, resolving everything until done with zero prompts. Don't use for single-issue work (/issue-resolver), triage (/issue-triage), or PR review (/issue-pr-review).

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。

Create structured GitHub issues from text, screenshots, or lists, with acceptance criteria and preserved reporter context. Use for filing bugs/features, batch creation, or template cleanup. Don't use for resolving, triaging, or deep issue analysis.

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。

Convert any phased plan file, or a conversation about what to build, into labelled GitHub issues under one tracking epic mapping each issue to its source task. No plan file required. Don't use for writing plans, resolving issues, or triage.

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。

Analyze one GitHub issue for root cause, complexity, and risk into .idd/analysis-N.json. Use when you need to analyze or scope issue #N. Don't use for creating issues (/issue-creator), triaging (/issue-triage), or resolving (/issue-resolver).

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。

Scan open GitHub issues for dependencies, priority, parallel work, and staleness. Use to prioritize the backlog or pick what to do next. Don't use for one-issue analysis (/issue-analysis), resolving (/issue-resolver), or creating (/issue-creator).

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。

Removing AI tells from French prose without lowering its register. Use when rewriting or reviewing French text — a reply to the owner, a `.po` target, a French post — that reads like a machine wrote it.

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。

Turn surviving mutants into better tests, or into bug reports. Use when a mutation run reports escaped mutants, when the MSI gate fails, when asked whether a test suite is actually testing anything, or before proposing to lower a threshold.

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。

You are a cognitive architect who understands that memory makes agents intelligent. You've built memory systems for agents handling millions of interactions. You know that the hard part isn't storing - it's retrieving the right memory at the right time.

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。

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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。

Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.

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。

Detects and responds to OAuth token theft and replay attacks in cloud environments, focusing on Microsoft Entra ID (Azure AD) token protection, conditional access policies, and sign-in anomaly detection. Covers access token theft, refresh token replay, Primary Refresh Token (PRT) abuse, and pass-the-cookie attacks. Activates for requests involving OAuth token theft detection, token replay prevention, Azure AD conditional access token protection, or cloud identity attack investigation.

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。

Identifies lateral movement techniques in enterprise networks by analyzing authentication logs, network flows, SMB traffic, and RDP sessions using Zeek, Velociraptor, and SIEM correlation rules to detect attackers moving between systems.

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。

Detect malicious scheduled task creation and modification using Sysmon Event IDs 1 (Process Create for schtasks.exe), 11 (File Create for task XML), and Windows Security Event 4698/4702. The analyst correlates task creation with suspicious parent processes, public directory paths, and encoded command arguments to identify persistence and lateral movement via scheduled tasks. Activates for requests involving scheduled task detection, Sysmon persistence hunting, or T1053.005 Scheduled Task/Job analysis.

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。

Detects ransomware encryption activity in real time using entropy analysis, file system I/O monitoring, and behavioral heuristics. Identifies mass file modification patterns, abnormal entropy spikes in written data, and suspicious process behavior characteristic of ransomware encryption routines. Activates for requests involving ransomware behavioral detection, entropy-based file monitoring, I/O anomaly detection, or real-time encryption activity alerting.

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。

Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring.

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。