「pipe flow」の検索結果

GitLab CLI for managing issues, merge requests, CI/CD pipelines, and repositories. Use when: (1) Creating, reviewing, or merging MRs, (2) Managing GitLab issues, (3) Monitoring or triggering CI/CD pipelines, (4) Working with self-hosted GitLab instances, (5) Automating GitLab workflows from the command line. Requires GITLAB_TOKEN (recommend minimal scopes). The `glab api` command enables arbitrary API calls - use read-only tokens when possible.

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Automate and script DaVinci Resolve workflows. Use when a user asks to script DaVinci Resolve via Python/Lua API, automate color grading, batch render projects, manage timelines programmatically, automate media import, build render queues, create Fusion compositions via script, automate Fairlight audio processing, manage project databases, build custom tool scripts, or integrate Resolve into production pipelines. Covers the Resolve Scripting API (Python/Lua), Fusion scripting, and workflow automation.

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Automate video editing in Blender's Video Sequence Editor with Python. Use when the user wants to add video, image, or audio strips, create transitions, apply effects, build edit timelines, batch assemble footage, estimate render times, or script any VSE workflow from the command line.

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Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.

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Generate technical architecture diagrams, pipeline flows, layer/stack diagrams, and system illustrations as standalone HTML files. Uses a dark-mode design system with embedded CSS and inline SVG. Use when the user needs architecture diagrams, system illustrations, flow charts, or technical visualizations.

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End-to-end content creation pipeline from research through editorial review to social distribution. Orchestrates a 3-stage workflow: research, draft/edit, and distribute. Supports blog posts, LinkedIn, Twitter threads, newsletters, and essays.

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Azure Machine Learning SDK v2 for Python workflow skill. Use this skill when the user needs Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Azure Machine Learning SDK v2 for Python workflow skill. Use this skill when the user needs Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Advanced Evaluation workflow skill. Use this skill when the user needs This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Advanced Evaluation workflow skill. Use this skill when the user needs This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

加密链上交易 (加密 / 链上交易 (Crypto & On-chain Trading) — 以加密资产为标的、强调链上活动的主动交易认知操作系统：从「看叙事/选赛道 → 找标的 → 链上尽调与风控 → 选场所(DEX/CEX/链上合约) → 建仓与仓位管理 → 监控链上流向与情绪 → 退出与复盘」的完整决策链。覆盖 (a) 第一性张力 — **链上 on-chain (DEX 现货/AMM、钱包追踪 wallet tracking、资金流向 flow、MEV、memecoin、链上衍生品 Hyperliquid/GMX、自托管 self-custody, 'not your keys not your coins') ⇄ 链下 CEX (中心化交易所现货/合约 Binance/OKX/Bybit、订单簿、托管、出入金法币)**; **合约/杠杆/衍生品 (perps 永续、资金费率 funding、清算 liquidation、爆仓、做多做空、高频/择时) ⇄ 现货/价值/长持 (spot、HODL、定投 DCA、基本面/估值、'time in market > timing the market')**; **价值/基本面派 (协议收入、TVL、代币经济学 tokenomics、真实使用、估值模型) ⇄ meme/叙事/情绪派 (memecoin、narrative rotation 叙事轮动、attention is the asset、纯博弈/PvP、Ponzinomics)**; **链上数据/量化/客观 (on-chain analytics, Nansen/Arkham/Dune、聪明钱 smart money、链上指标、系统化/机械化) ⇄ 直觉/盘感/叙事阅读 (narrative、KOL alpha、社群情绪、tape reading)**; **自托管/去中心化/抗审查 (DeFi、自托管钱包、抗审查、'be your own bank') ⇄ 便利/合规/中心化 (CEX、合规出入金、KYC、托管、ETF)**; **主动交易/择时/高换手 (trading、波段、合约、撸毛/空投 airdrop farming、套利 arbitrage) ⇄ 被动配置/低换手 (配置 BTC/ETH、ETF、长期叙事 beta)**; (b) 核心工作流 / pipeline (最标准、最易蒸高质量 + CLI 化) — 通用链: 叙事/赛道判断 (宏观周期/减半/流动性/板块轮动) → 标的发现 (链上扫链/聪明钱跟单/新币/叙事映射) → 尽调 due diligence (代币经济学/解锁 unlock/团队/合约审计/honeypot 检测/流动性与持仓分布) → 风控前置 (仓位 sizing/最大回撤/止损/相关性) → 场所与执行 (CEX vs DEX、滑点/MEV/gas、限价 vs 市价、TWAP) → 持仓监控 (链上流向/巨鲸/资金费率/解锁日历/情绪指标) → 退出与复盘 (止盈分批/再平衡/记录交易日志); 子流派 SOP: 链上 degen 打新与 memecoin 流程、合约交易者的资金费率与清算地图、空投/撸毛 farming、套利与 MEV、被动配置与定投; (c) 工具栈 — 链上分析 (Nansen 聪明钱/Arkham 实体标注/Dune 自建仪表盘/Dexscreener & DEX Screener 新币/GMGN & Photon & BullX memecoin 终端/Etherscan & Solscan 区块浏览器/DeBank 钱包总览/Token Terminal 协议基本面/DefiLlama TVL/Glassnode & CryptoQuant 链上指标)、交易场所 (CEX: Binance/OKX/Bybit/Coinbase; 链上现货 DEX: Uniswap/Jupiter/Raydium/Aerodrome; 链上衍生品: Hyperliquid/GMX/dYdX)、钱包与安全 (MetaMask/Rabby/Phantom 热钱包、Ledger/Trezor 硬件冷钱包、Revoke.cash 授权管理、honeypot/合约扫描)、行情与图表 (TradingView、CoinGecko/CoinMarketCap)、信息与聚合 (Twitter/X、Telegram、DeFiLlama、RootData/ICO 日历、解锁日历 Token Unlocks)、自动化 (交易机器人、链上 bot/sniper、Telegram trading bot); (d) 知识正典 — 注意加密交易 canon 偏少且新: 通用交易/风控经典 (Edwin Lefèvre 'Reminiscences of a Stock Operator'、Van Tharp 'Trade Your Way to Financial Freedom' 仓位管理、Mark Douglas 'Trading in the Zone' 交易心理、Michael Mauboussin、Nassim Taleb 'Fooled by Randomness/Antifragile')、加密原生 (Bitcoin whitepaper 中本聪、'The Bitcoin Standard' Saifedean、Vitalik 博客与 Ethereum whitepaper、'DeFi and the Future of Finance'、Lyn Alden 宏观、a16z crypto 文章、Messari/Delphi/Bankless 研报)、链上分析方法 (Glassnode/CryptoQuant 教程、Nansen/Arkham/Dune 文档、Dune Analytics SQL)、交易方法论散见于 podcast/Substack/Twitter thread 而非书; (e) figures/流派 — 价值/基本面派 (Vitalik Buterin 以太坊、Hasu/Su Zhu(争议)、Arthur Hayes BitMEX 创始人/宏观+衍生品、Raoul Pal、Lyn Alden 宏观、Cobie/Jordan Fish UpOnly podcast、DeFi 研究 Bankless Ryan Adams/David Hoffman); 链上分析派 (Nansen Alex Svanevik、Arkham、Adam Cochran、Ansem(blknoiz06) 链上交易、Murad memecoin supercycle、链上侦探 ZachXBT 反诈); 合约/衍生品派 (Arthur Hayes、GCR(GiganticRebirth) 传奇合约、Pentoshi、CryptoCred 技术分析、Tyler Durden); meme/叙事派 (Ansem、Murad、Cobie、各 Solana memecoin KOL); 宏观/周期派 (Raoul Pal、Lyn Alden、PlanB stock-to-flow(争议)、Benjamin Cowen 数据/周期); 量化/系统化 (各匿名量化、Hyperliquid 生态); 中文圈 (链上分析/合约 KOL、币圈大 V、播客如 '无人之境'/'Web3 佛学院'、研究机构如 Foresight/PANews/律动 BlockBeats); 反诈/安全 (ZachXBT、SlowMist 慢雾、go+ 安全); (f) 行业话术/黑话 — 现货 spot / 合约 perps 永续 / 杠杆 leverage / 多空 long-short / 爆仓 liquidation 清算 / 资金费率 funding rate / 开多开空 / 止损 SL 止盈 TP / 仓位 position sizing / 满仓梭哈 ape in / 梭 / FOMO / FUD / 钻石手 diamond hands / 纸手 paper hands / 韭菜 / 镰刀 / 庄 / 出货 / 拉盘砸盘 / 插针 wick / 针 / 上车下车 / 埋伏 / 潜伏 / 链上 on-chain / 钱包 wallet / 地址 / 聪明钱 smart money / 巨鲸 whale / 跟单 copy-trade / 老鼠仓 / 貔貅盘 honeypot / 貔貅 / rug 跑路 rug-pull / 归零 / 土狗 (memecoin/小币) / 金狗 (暴涨百倍) / meme 币 / 叙事 narrative / 赛道 / 板块轮动 / 撸毛 撸空投 airdrop / 女巫 sybil / 交互 / 解锁 unlock / 砸盘解锁 / 代币经济学 tomomics / TVL / APY APR / 无常损失 IL / 滑点 slippage / MEV / 三明治 sandwich / gas / 抢跑 frontrun / 貔貅检测 / 授权 approve / 撤销授权 revoke / 私钥 助记词 seed phrase / 冷钱包 热钱包 / 自托管 / 减半 halving / 牛熊 / 山寨季 altseason / 比特币市占率 BTC.D / 山寨 alt / 主流币 / DeFi 暑期 / GM / WAGMI / NGMI / 归零归零 / PvP / Ponzi 庞氏; (g) 争议/批

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Runs a multi-step procedure as a Python DAG, so ordering, branching and retries are enforced by the runner rather than described in prose a model can generate past. Use for "run these steps in order and retry the flaky one until the check passes", "build a pipeline that fetches, validates, then skips the upload when nothing changed", "make sure these steps cannot be skipped", "resume from where it broke instead of redoing the expensive early stages", "run these independent calls at once and merge the results", or any procedure of 3+ steps with branches, input contracts, or side effects that must not block the critical path. Primitives are depends_on, when=, validate=, retry_until=, detached= and journal_path=. Not for a single sequential call, for steps needing reasoning between them that no predicate captures, or for async and distributed work. To audit whether one verification check can actually go red, use gating. To fan work out across many subagents, use a dynamic workflow.

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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Design CI/CD pipelines for GitHub Actions, GitLab CI, and CircleCI with matrix builds, test sharding, caching, Docker layer caching, OIDC auth, deployment strategies (rolling, blue-green, canary), auto-rollback, self-hosted runners, and environment protection with manual approvals. Use when user asks to set up CI/CD, write a pipeline, configure GitHub Actions/GitLab CI/CircleCI, automate deployments, or set up build/test/deploy workflows. Do NOT use for Dockerfile authoring (use docker), K8s manifests (use kubernetes), or Terraform config (use terraform).

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Use when a repository needs CI/CD - author GitHub Actions workflows so TaskTrooper's pipeline auto-detects your validate/build/test jobs and stage/preprod/prod deploys, and so a dispatch delivery profile can dispatch the production deploy

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CI/CD pipeline patterns for GitHub Actions, PR automation, and deployment workflows. Use when setting up CI, fixing broken pipelines, automating PR checks, or configuring deployment.

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Designs and troubleshoots NUKE-based CI/CD pipelines for .NET services. Use when refactoring target graphs, splitting test flows, publishing reports, or diagnosing slow pipelines.

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Designing GitHub Actions workflows in depth: trigger strategy, security hardening, performance optimization, PR automation, and Reusable Workflow design.

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Instructions for managing CI pipelines, GitHub Actions workflows, Airflow DAGs, and automation scripts in the insurance analytics project.

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Create a Datagrok Compute2 workflow (pipeline configuration with steps, links, and actions)

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Category-theoretic pipes with GF(3) flow balance checking. Validates data transformation pipelines using triadic trit algebra.

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Configures CI/CD pipelines for automated Python package publishing to PyPI or GitLab Package Registry. Use when creating GitHub Actions or GitLab CI release workflows, setting up trusted publishing or API token-based PyPI authentication, configuring version management with git tags and hatch-vcs, writing pyproject.toml publishing metadata, testing packages against TestPyPI, or documenting the release process for a Python project.

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Use this skill when answering business analytics, RevOps, GTM metric, pipeline, revenue, funnel, customer, or warehouse questions with Deepline. Triggers on phrases like 'query Snowflake', 'analyze pipeline', 'total ACV', 'break down by quarter', 'use the semantic layer', 'run a semantic query', or any use of snowflake_get_semantic_layer / snowflake_run_semantic_query. Skip prospecting, enrichment, contact finding, outbound, or personalization workflows; use deepline-gtm for those.

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Use when the user has written (or wants to write) a `ModularPipelineBlocks` subclass in a local Python file and needs to package it into a Hub-uploadable directory. Covers the workflow from a single `block.py` file to a published custom-block repo that consumers can load via `ModularPipeline.from_pretrained(<repo>, trust_remote_code=True)`.

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