「gc」の検索結果

Git Context Controller (GCC) v2 — Lean agent memory backed by real git. Stores hash + intent + optional decision notes instead of verbose markdown. Auto-bridges to aiyoucli vector memory when available. Dual mode: git-backed (lean index.yaml) or standalone (markdown fallback). Triggers on /gcc commands or natural language like 'commit this progress', 'branch to try an alternative', 'merge results', 'recover context'.

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GCP best practices: IAM, secrets, networking, security, compute, IaC, ops. Use when building, reviewing, or modifying GCP resources.

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Switch or create a gctree gc-branch.

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Clear an existing gc-branch so it can be onboarded again.

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Use when designing, validating, deploying, changing, or troubleshooting Volcengine TLS LogCollector collection on Linux hosts or Kubernetes, including Project, Topic, host group, collector rule, index, binding, heartbeat, parsing, and end-to-end log ingestion checks through volclog. 当用户需要设计、部署、 变更或排查火山引擎日志服务 LogCollector 采集（主机、Kubernetes DaemonSet/Controller、机器组、采集配置、 心跳、解析、日志未入库）时调用。

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Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.

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Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.

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AI公司首席法务官技能包。合同治理、知识产权保护、AI专项法务（算法审计/AIGC合规/数据供应链）。覆盖GDPR/CCPA跨境数据合规，新增AI伦理委员会架构、合规分级目标、数据保护双线接口、AIGC内容合规审查链。

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Build AI agents with LangChain framework. Use when building agents, tools, memory, MCP integrations, RAG pipelines, multi-agent systems, or any LLM-powered applications using LangChain or LangGraph in Python or TypeScript.

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gcloudやbqコマンド実行時に認証エラー（Reauthentication required等）を検出した場合に使用。エージェントが自動で認証コマンドを実行することを防ぎ、ユーザーに認証を依頼します。

Deploy cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach detection.

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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.

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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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Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.

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Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

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Publish a released version of the Apache SkyWalking AI Sessionizer LangChain plugin, apache-skywalking-asz-langchain, to PyPI. Says what a release manager sets up before the first upload, downloads the voted source package, verifies it, builds the source distribution and the wheel from plugins/langchain inside it, checks and installs them, and uploads them with twine. Use after a version is published.

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Use when working with Bigcommerce — bigCommerce store management including products, orders, customers, channels, shipping, inventory, storefront themes, and webhooks. Covers sales analytics, order fulfillment rates, inventory health, and multi-channel performance comparison.

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Use when working with Gcp Secret Manager — google Secret Manager secret lifecycle management, version control, rotation configuration, IAM policy auditing, and access diagnostics via gcloud CLI.

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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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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 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.

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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.

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。