「gc」の検索結果

Use when working with Gcp Cloud Trace — google Cloud Trace latency analysis, trace exploration, sampling configuration, and span diagnostics via gcloud CLI.

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Use when working with Gcp Idle Resources — detect unused and idle GCP resources that incur cost without providing value. Covers unattached Persistent Disks, unused external IPs, stopped VMs, idle Cloud NAT, idle load balancers, old snapshots, idle Cloud SQL instances, abandoned GKE node pools, unused VPC connectors, and orphaned Filestore instances. Includes estimated monthly waste per resource and anti-hallucination rules for safe detection.

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Use when working with Gcp Gke — google Kubernetes Engine cluster operations, node pool management, workload analysis, autopilot configuration, and upgrade planning via gcloud CLI.

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Use when working with Gcp Firestore Deep — google Cloud Firestore document and collection analysis, index management, security rules review, usage monitoring, and performance diagnostics via gcloud CLI.

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Use when working with Gcp Cloud Spanner — google Cloud Spanner instance management, query statistics analysis, hot spot detection, schema analysis, and performance diagnostics via gcloud CLI.

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Use when working with Gcp Cloud Logging — google Cloud Logging log analysis, log-based metrics, sink management, exclusion filters, and log routing configuration via gcloud CLI.

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Use when working with Gcp Cloud Armor — google Cloud Armor security policy management, WAF rule configuration, adaptive protection analysis, and edge security policy management via gcloud CLI.

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Use when working with Gcp Alloydb — google AlloyDB cluster management, instance analysis, query insights, maintenance window configuration, and performance diagnostics via gcloud CLI.

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 working with Gcp Cloud Deploy — google Cloud Deploy delivery pipeline management, release tracking, rollout status monitoring, approval workflows, and deployment diagnostics via gcloud CLI.

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Expert en infrastructure GCP (GKE, Cloud Run, BigQuery, Pub/Sub, Firestore, Cloud Functions)

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You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

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Implement GCP Organization Policy constraints to enforce security guardrails across the entire resource hierarchy, restricting risky configurations and ensuring compliance at organization, folder, and project levels.

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Implement GCP Binary Authorization to enforce Dağıt:-time security controls that ensure only trusted, attested container images are Dağıtılmış to Google Kubernetes Engine and Cloud Run.

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Implementing and auditing GCP VPC firewall rules to enforce network segmentation, restrict ingress and egress traffic, apply hierarchical firewall policies across the organization, and monitor firewall rule effectiveness using VPC Flow Logs.

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Auditing Google Cloud Platform IAM permissions to identify overly permissive bindings, primitive role usage, service account key proliferation, and cross-project access risks using gcloud CLI, Policy Analyzer, and IAM Recommender.

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AES-GCM ve ChaCha20-Poly1305 nonce yeniden kullanımı saldırısı — GF(2^128) polinom kök bulma ile Hash Key kurtarma ve MAC sahteciliği.

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Responds to security incidents in cloud environments (AWS, Azure, GCP) by performing identity-based containment, cloud-native log analysis, resource isolation, and forensic evidence acquisition adapted for ephemeral cloud infrastructure. Activates for requests involving cloud incident response, AWS security incident, Azure compromise, GCP breach, cloud forensics, or cloud identity compromise.

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。

Auditing Google Cloud Platform IAM permissions to identify overly permissive bindings, primitive role usage, service account key proliferation, and cross-project access risks using gcloud CLI, Policy Analyzer, and IAM Recommender.

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。

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

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Analyze and optimize AGC (Automatic Gain Control) parameters for WaveCap-SDR channels. Use when audio is too quiet, too loud, has pumping artifacts, or when tuning AGC attack/release/target settings for FM/AM/SSB modes.

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Detects A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigs_cis), then orients (phases) the compartment eigenvector against a GC or gene-density track so the active (A) sign is not arbitrary. Covers the eigenvector-is-a-choice problem (per-arm view_df to remove the centromere gradient; picking the eigenvector by max correlation with activity, not by eigenvalue), GC phasing with bioframe.frac_gc, resolution choice (100kb-1Mb), saddle plots and saddle_strength for compartmentalization strength, the cohesin-loss-strengthens-compartments result, subcompartments (SNIPER/Calder/dcHiC), and cross-condition compartment switching. Use when calling A/B compartments, computing E1/eigenvectors, phasing the eigenvector, building saddle plots, choosing a compartment resolution, quantifying compartment strength, or comparing compartmentalization across conditions.

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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

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 A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigs_cis), then orients (phases) the compartment eigenvector against a GC or gene-density track so the active (A) sign is not arbitrary. Covers the eigenvector-is-a-choice problem (per-arm view_df to remove the centromere gradient; picking the eigenvector by max correlation with activity, not by eigenvalue), GC phasing with bioframe.frac_gc, resolution choice (100kb-1Mb), saddle plots and saddle_strength for compartmentalization strength, the cohesin-loss-strengthens-compartments result, subcompartments (SNIPER/Calder/dcHiC), and cross-condition compartment switching. Use when calling A/B compartments, computing E1/eigenvectors, phasing the eigenvector, building saddle plots, choosing a compartment resolution, quantifying compartment strength, or comparing compartmentalization across conditions.

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。

Find and identify the CCSGameRules__sm_mapGcBanInformation global variable in CS2 binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the GC ban information map structure by decompiling CCSPlayerController_ResourceDataThink's sub-function (Linux) or searching for the "Notification about user penalty" string (Windows).

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。