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🇺🇸 UGC Matrix Growth Playbook — Scale user-generated content with AI + real creators. Complete SOP for UGC flywheel, creator recruitment, incentive design, multi-platform publishing. Proven at CPM $0.5, $10M ARR in 60 days with 70M impressions. Includes a full anonymized client case study. 🇨🇳 UGC矩阵号增长实操手册 — AI+真人创作者规模化用户生成内容。完整的UGC飞轮SOP、创作者招募、激励机制设计、多平台分发策略。已验证CPM $0.5，60天$1000万ARR，7000万曝光。含完整客户案例拆解（已脱敏）。 🇯🇵 UGCマトリックス成長プレイブック — AI＋リアルクリエイターでUGCをスケール。フライホイールSOP、クリエイター採用、インセンティブ設計、マルチプラットフォーム配信。CPM $0.5、60日で$10M ARR、7000万インプレッション実証済み。匿名化クライアント事例付き。 🇰🇷 UGC 매트릭스 성장 플레이북 — AI + 실제 크리에이터로 UGC 스케일링. 플라이휠 SOP, 크리에이터 모집, 인센티브 설계, 멀티플랫폼 배포. CPM $0.5, 60일 $10M ARR, 7000만 노출 검증 완료. 익명화된 클라이언트 사례 포함. Triggers: "UGC" | "UGC matrix" | "user generated content" | "creator program" | "creator economy" | "AI UGC" | "matrix accounts" | "矩阵号" | "UGC运营" | "创作者招募" | "UGC Program" | "content matrix" | "TikTok UGC" | "Instagram UGC"

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Assess AWS, Azure, and GCP environments by identifying identities, permissions, and paths to broader access or data exposure. Covers cloud storage, secrets, cross-account trust, and CI/CD, the automated systems used to build and deploy software.

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• Planning an AWS, Azure, or GCP security assessment
• Tracing privilege escalation from current permissions
• Reviewing CI/CD credentials and OIDC trust settings

Plan UGC campaigns, ads built around user-style content, from creator recruitment and production briefs to AI video variations. Organize organic testing, paid promotion, and creative refreshes for TikTok, Meta, and YouTube Shorts.

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• Recruiting creators for product videos
• Writing a short video production brief
• Planning AI-generated ad variations

Helps choose and tune Cloud Storage FUSE mounts, which expose bucket objects through file paths. It covers cache sizing, training performance, checkpoint write safety, and cost diagnosis on GKE, Compute Engine, and Cloud Run.

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• Access training data through file paths
• Choose between FUSE, native reads, and shared file systems
• Check checkpoint and concurrent write safety

Design Google Cloud infrastructure with Terraform, validate it locally, and assess the planned changes for security, cost, and reliability. The skill then imports templates into Application Design Center, deploys them, investigates failures, and checks the deployed services.

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• Designing and locally validating Terraform for GCP
• Assessing infrastructure plans before deployment
• Importing and deploying Application Design Center templates

Build a User Generated Content (UGC) strategy — getting customers to create content, review generation, UGC briefs for creators, social campaigns, contest mechanics, legal rights management, and repurposing UGC in paid ads. Use when the user says "UGC", "user generated content", "get customers to create content", "customer content", "UGC ads", "UGC creators", "organic UGC", "UGC strategy", "how do we get customers to post about us", "brand hashtag", "UGC brief", "influencer UGC", "UGC for ads", "customer photos and videos", or wants to use real customer content to build trust and reduce content production costs.

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Assess what discovered cloud credentials allow across AWS, Azure, GCP, and Kubernetes. The skill guides identity checks, permission review, and paths to higher privileges, then helps document impact, activity, and cleanup within the engagement's agreed scope.

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• Assess permissions granted by discovered cloud credentials
• Review role trust and access across cloud accounts
• Check permissions assigned to anonymous Cognito identities

AI UGC Ads workflow skill. Use this skill when the user needs When the user wants to create UGC ad campaigns, recruit UGC creators, generate AI UGC content, or scale with user-generated content. Also use when the user mentions 'UGC,' 'user-generated content,' 'creator ads,' 'Spark Ads,' 'whitelisting,' 'AI UGC,' 'Arcads,' 'Creatify,' 'creator brief,' or 'UGC testing.' This skill covers the UGC growth framework from creator recruitment through AI-powered scaling. Do NOT use for technical implementation, code review, or software architecture and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Audit Google Cloud access permissions to find overly broad roles, unnecessary service account keys, public access, and impersonation risks. Use gcloud CLI, Policy Analyzer, and IAM Recommender to document findings and plan more limited permissions.

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• Reviewing access permissions across a GCP organization
• Replacing broad Owner and Editor roles with narrower roles
• Auditing unused service accounts and user-managed keys

Prepare Bugcrowd submissions with accurate vulnerability categories, evidence-based severity requests, and explanations of program scope. Structure references between related reports, document QA testing conditions, and draft responses when a finding is downgraded or considered out of scope.

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• Choose a suitable Bugcrowd vulnerability category
• Explain a severity request using demonstrated impact
• Respond to out-of-scope decisions

Investigate cloud configuration weaknesses across AWS, GCP, and Azure, including public storage, exposed services, and leaked credentials. Find candidates in public information and JavaScript, then verify data access and permissions to establish practical impact.

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• Checking public access to S3, GCS, and Azure Blob storage
• Reviewing exposed serverless and managed services
• Assessing CloudWatch RUM guest role permissions

Use this skill when the user is doing hands-on DOCA AES-GCM work on a BlueField DPU or ConnectX NIC — configuring `doca_aes_gcm_task_encrypt` / `_task_decrypt`, querying `doca_aes_gcm_cap_*` for per-key-type (only `DOCA_AES_GCM_KEY_128` / `_256` — AES-192 not supported) and per-task support, sizing plaintext against the max-buf cap, setting source / destination mmap permissions, validating with a NIST GCMVS or RFC 5288 vector, or debugging DOCA_ERROR_* including the security-critical tag-verification-failed outcome on decrypt. Trigger even when the user does not explicitly mention "DOCA AES-GCM" or "AEAD" — typical implicit phrasings: "decrypt completion IO_FAILED", "auth tag isn't verifying", "NOT_PERMITTED on my encrypt buffer", "is AES-192-GCM on this BlueField" (no), or "encrypted record came back tampered". Refuse and route elsewhere for non-GCM AES modes (CBC / CTR / XTS — CPU OpenSSL), key management (KMS / HSM / rotation), SHA (doca-sha), or general AEAD background.

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Assess what exposed credentials can access in AWS, GCP, and Azure. Map permissions, test paths to higher privileges, and document demonstrated impact using benign test objects within an explicitly authorized assessment scope.

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• Assessing the impact of exposed cloud credentials
• Reviewing paths to higher cloud privileges
• Checking storage access with benign test objects

Google Cloud Platform (GCP) の Application Default Credentials 認証を実行するスキル。 「GCP認証して」「Google Cloud認証」「gcloud login」等のリクエストで発動。 BigQuery や Cloud Storage 等の GCP サービス利用前の認証手順をガイド。

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Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

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Review images and their visible captions, then connect them with figure and figcaption markup. Check caption placement and alternative text so screen readers can present the relationship between an image and its description.

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• Reviewing HTML for images with captions
• Checking captions and alternative text for duplication
• Improving screen reader access to image descriptions

Design AI applications with LangChain 1.x and LangGraph, libraries for coordinating language models and workflows. Build agents that use external tools, manage conversation history and saved state, process documents, and support tracing and testing.

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• Build an AI agent that calls external tools
• Manage conversation history and saved execution state
• Create document processing and retrieval workflows

Respond to security incidents across AWS, Azure, and GCP by investigating audit logs, containing compromised identities, isolating resources, and preserving evidence. Organize recovery and follow-up security measures for credential leaks, unauthorized permission changes, and attacks spanning cloud services.

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• Responding to leaked cloud credentials
• Investigating unauthorized permission or resource changes
• Preserving audit logs and disk snapshots

Design Google Cloud infrastructure around application needs, scale, and budget. Get service recommendations, cost estimates, infrastructure configuration files, and deployment guidance, with support for automated delivery and security configuration reviews.

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• Designing a mobile backend on Cloud Run
• Planning a SaaS architecture on GKE
• Building an event analytics pipeline with BigQuery

Guides building chatbots and tool-using agents with LangChain. Covers conversation history, model provider integration, and RAG, which retrieves relevant documents to inform answers, plus tracing to inspect application behavior.

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• Build a chatbot that remembers conversation history
• Create document question answering with source references
• Build an agent that uses custom tools

Migrate workloads from Google Cloud Platform to AWS — plus AI and agentic workloads from any provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, migrate App Engine to Elastic Beanstalk, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform, app code, or billing exports, then clarify, design, estimate costs, generate artifacts, and collect feedback. Clarify must finish before Design, Estimate, or Generate. Do not use for: Azure migrations (see azure-to-aws), on-premises migrations, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, or GCP-to-GCP refactoring.

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降低中文学术写作（本/专科毕业论文）AIGC检测率的专项 skill。 基于真实论文改写实验（AIGC率从 >50% 降至 11%）归纳的规律，并参考 Wikipedia「Signs of AI writing」项目的系统性模式分类。 检测并修复中文AI写作的典型模式；默认保量润色（避免为降AIGC大幅删短）； 含中英术语与缩写规范、会话记忆库（避免重复释义、滚动段落摘要防前后不一致）、 LaTeX 源文件保护规则。支持：降低AI率、人工润色、降低AIGC、 humanize、去AI味、学术写作润色、论文降重、AIGC检测、AI痕迹消除、学术文本 人工化处理、AIGC检测报告。

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使用 gcloud CLI 操作 Google Cloud Platform。当用户提到 GCP、Google Cloud、gcloud、Google Cloud SDK、GCE、GKE、BigQuery、Cloud Storage、Cloud Run、Cloud Functions、VPC、计算实例、VM、存储桶、部署到 GCP、GCP 项目、GCP 账单、IAM 权限等时触发此技能。即使用户只说"我的云服务器"、"查看服务器状态"、"云存储"等模糊表述，如果上下文暗示 Google Cloud，也应使用此技能。

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Set up decoy credentials, storage, and secrets across AWS, Azure, and GCP to detect suspicious access. Connect audit logs to security alerts, verify detection, and document permissions, resource inventories, and maintenance plans.

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• Detecting use of leaked cloud credentials
• Monitoring suspicious access to decoy storage and secrets
• Extending on-premises deception coverage to cloud environments