「pip」の検索結果

Expert TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity compliance.

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Expert knowledge for Azure DevOps development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing org/projects, repos, pipelines, work items, dashboards/analytics, or Azure DevOps Server deployments, and other Azure DevOps related development tasks. Not for Azure Boards (use azure-boards), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

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Use when the user wants to run a full ad-campaign pipeline on top of Higgsfield Marketing Studio — 'create a campaign', 'build a content plan', 'run the content pipeline', 'generate 100 UGC videos', 'plan and schedule a launch', 'make a batch of ads from my product', or 'how much did this save vs traditional production'. Covers the 5-stage orchestration (Research → Plan → Generate → Publish → Report), the UGC-first 5-format campaign mix (UGC Entertainment, Street Interview, Unboxing, Product Review, ASMR), the even-split allocation math, button-driven onboarding, the per-batch generation gate, and the publish + cost-report tail (satellite). Defers all Marketing Studio API ground-truth (presets, params, hooks, avatars) to higgsfield-marketing-studio.

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Bo tai lieu ban hang cong voi ky luat giu pipeline cho SME VN, gop mot skill vi cung mot nguoi lam ca hai viec — the persona nguoi mua (co ca nguoi can duong), one-pager, outline bo slide tu van, thu vien xu ly phan doi kieu VN viet o hai dang (bang tra nhanh khi dang goi va ban day du de dao tao), kich ban demo co moc thoi gian, bao gia, giai doan pipeline co tieu chi RA cong nguoi chiu trach nhiem cong SLA, quy tac toc do phan hoi inbox Zalo, va phuong an khong co CRM bang Google Sheet cong nhan Zalo. Kich hoat khi user nhac 'tai lieu ban hang', 'sales deck', 'xu ly phan doi', 'kich ban tu van', 'pipeline ban hang', 'quan ly khach tiem nang', 'bao gia cho khach', 'khach noi de suy nghi them'. Khong dung cho — tim va tao lead moi thi dung skill 33-b2b-lead-gen; thiet ke goi ban va muc gia thi dung skill 31-offer-design va skill 17-pricing-strategy.

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Use when the user is launching or running a DROPSHIPPING business end to end — the full 12-phase pipeline: product research with winning criteria, Minea and PiPiAds, supplier sourcing on AliExpress, CJ, Spocket, and Zendrop, Shopify store and app setup, the 10-ads-per-week creative pipeline, interest and lookalike targeting, 3-5x markup and BE-ROAS math, customer service, and scaling. Trigger on 'start dropshipping', 'winning product research', 'Shopify store setup', 'AliExpress supplier', 'scale my dropshipping store', 'my store gets traffic and no sales'. Not for — only the ad copy, see `05-ad-copy-global`; only the product page, see `12-landing-page-brief-global`; only the account audit, see `21-ads-audit-global`; only pricing, see `17-pricing-strategy-global`.

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 the user needs B2B pipeline work — ICP and account lists, prospecting, cold email and LinkedIn outbound sequences, lead scoring, MQL to SQL handoff, sales decks, objection handling, demo scripts, CRM pipeline hygiene, and RevOps reporting. Trigger on 'find leads', 'prospect list', 'cold email sequence', 'outbound campaign', 'lead scoring model', 'our pipeline is empty', 'sales deck for enterprise buyers'. Also use when the user has a strong product and no route to the companies that need it. Not for — consumer lifecycle email, see `14-email-marketing-global`; the offer being sold, see `31-offer-design-global`; positioning against rivals, see `58-positioning-global`; inbound content, see `36-content-brief-global`.

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。

生成用于构建和发布 .NET 6 MAUI iOS 项目的 Azure Pipelines YAML 配置，包含 DotNetCoreCLI 任务配置、RuntimeIdentifier 设置及 IPA 文件重命名功能。

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Build custom Angular attribute directives, HostDirectives, and pure pipes. Use for Angular directive or pipe implementation, including custom attribute directives; defer React/CSS middleware and unrelated framework work.

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Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

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。

Audit/regression checks for the evidence-first survey pipeline: citation health, per-section coverage, placeholder leakage, and template repetition.

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Re-ingest-correctness SOP for production RAG. Activate when a calling agent builds, reviews, or debugs an ingestion pipeline that runs more than once over a changing corpus — scheduled re-index, incremental updates, CI re-ingest, or a "retrieval has duplicates / shows deleted docs" bug. Encodes the rule — **ingestion must be idempotent: a document's content hash decides insert/update/skip, so re-running over unchanged docs is a no-op** — plus the docstore + doc-hash upsert machinery (LlamaIndex `IngestionPipeline` + `DocstoreStrategy`), the delete-propagation problem, and cross-framework equivalents (LangChain `index()` + `RecordManager`, manual hash table). ENHANCE overlay over [[llamaindex]]: the IngestionPipeline exists in the base skill but the re-ingest-correctness contract is not surfaced.

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。

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

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Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL, including Auto CDC from event streams or periodic complete snapshots. Invoke BEFORE starting implementation.

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Guides compliance with country-specific data localization requirements across key jurisdictions including Russia (242-FZ), China (PIPL Art. 40, CAC measures), India (DPDP Act), Turkey, Vietnam, and Indonesia. Covers localization assessment, architecture design, and exemption procedures. Keywords: data localization, data residency, PIPL, 242-FZ, cross-border restrictions.

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Guides management of cross-border data transfers under Asia-Pacific regulatory frameworks including APEC CBPR, ASEAN Model Contractual Clauses, Japan APPI supplementary rules, South Korea PIPA provisions, and Thailand/Singapore PDPA mechanisms. Keywords: APEC CBPR, ASEAN MCCs, APPI, PIPA, PDPA, APAC transfers.

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Generate GitHub Actions CI/CD pipeline configurations for automated building and testing of library and package projects. Use when creating or updating CI workflows for npm packages, Python packages, Go modules, Rust crates, or other library projects that need automated build and test pipelines. Includes templates for common package ecosystems with best practices for dependency caching, matrix testing, and artifact publishing.

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Plan, implement, or review ml pipeline work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests ml pipeline work.

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End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design-tool-selection. For QC thresholds, use protein-design-qc.

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。

End-to-end guidance for protein design pipelines. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design. For QC thresholds, use protein-qc.

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。

Validate data quality in CSV, JSON, and database exports by checking for missing values, type mismatches, duplicates, outliers, and schema violations. Use when building ETL pipelines, auditing data imports, checking data freshness, or ensuring data contracts between teams. Trigger words: data quality, validation, null values, duplicates, schema check, data contract, ETL, pipeline, data drift.

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ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.x.

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。

Coordinate multiple AI agents working together on complex tasks — routing, handoffs, consensus, memory sharing, and quality gates. Use when tasks involve building multi-agent systems, coordinating specialist agents in a pipeline, implementing agent-to-agent communication, designing swarm architectures, setting up agent orchestration frameworks, or building autonomous agent teams with supervision and quality control. Covers hierarchical, mesh, and pipeline topologies.

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Data pipeline specialist for ETL design, data quality, CDC patterns, and batch/stream processingUse when "data pipeline, etl, cdc, data quality, batch processing, stream processing, data transformation, data warehouse, data lake, data validation, data-engineering, etl, cdc, batch, streaming, data-quality, dbt, airflow, dagster, data-pipeline, ml-memory" mentioned.

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。

Expert in integrating Claude Code with CI/CD pipelines. Covers headless mode for non-interactive execution, GitHub Actions and GitLab CI/CD integration, automated code review, issue triage, and PR workflows. Essential for teams wanting AI-powered automation in their development pipelines. Use when "claude code CI/CD, headless mode, GitHub Actions claude, GitLab CI claude, automated code review, PR automation, issue triage, claude-code, cicd, automation, github-actions, gitlab, headless, pipeline, devops" mentioned.

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。