「data pipeline」の検索結果

Build custom AI agents in Claude Code from a user's problem statement. This skill analyzes the user's use case, asks smart clarifying questions, researches the internet for similar agents (GitHub repos, blogs, Claude Code community patterns), and then architects and builds production-ready Claude Code agents — including subagents, skills, hooks, slash commands, MCP integrations, and CLAUDE.md configuration. Use this skill whenever the user wants to create an agent, build an automation workflow, set up a Claude Code subagent, design a multi-agent system, or says things like 'build me an agent for...', 'automate this with Claude Code', 'I want a subagent that...', 'help me create a workflow', 'set up an agent pipeline', or any variation of wanting Claude Code to do something specialized. Also trigger when the user mentions agent architecture, agent SDK, agentic workflows, or task delegation in Claude Code — even if they don't use the word 'agent' explicitly.

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。

"Implement a web crawler pipeline covering URL discovery, fetching, parsing, and storage. Use this skill when the user needs to build a site crawler, audit website structure, or collect web data systematically — even if they say 'scrape a website', 'crawl all pages', or 'site audit spider'.".

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。

Idiomatic Go concurrency - sync primitives, channel semantics, the select statement, and the standard channel patterns (cancellation/done-channel, fan-out/fan-in, pipeline, or-done, context, worker-pool). Use when writing or reviewing Go that spawns goroutines, shares state, or coordinates over channels, even if the user never says "concurrency" - e.g. data races, deadlocks, goroutine leaks, sync/context/select usage, or "make this parallel". Distilled from luk4z7/go-concurrency-guide.

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 planning a source-grounded method overview, architecture, pipeline, system/data-flow, mechanism, or agent-workflow figure for a CS or deep-learning paper. Produces an editable figure brief before image generation or manual redrawing; not for plots, tables, or general prose.

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 testing file upload endpoints for unrestricted file upload, MIME type bypass, magic byte spoofing, polyglot files, SVG XSS, XXE via Office documents, ZIP slip, and path traversal in filenames. Trigger on: multipart/form-data endpoints, avatar/document upload flows, import-from-file features, profile image, CSV/Excel import, DOCX/XLSX parsing, image resizing pipelines, archive extraction, and any endpoint that stores or serves user-supplied files. Detects extension bypass (shell.php.jpg), null byte injection, double extension, ImageMagick exploits, and content-type confusion.

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 this agent when you need to perform custom bioinformatics analyses on genes or proteins that go beyond standard database lookups. This includes situations where existing domain/family annotations from UniProt are insufficient, when you need to verify dubious annotations, or when you need to perform comparative analyses across gene sets. The agent creates reproducible analysis pipelines with proper dependency management and documentation.

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 AI website generation pipeline. Claude Opus 4.8 emits Bolt-style <boltArtifact> envelopes (multi-file, plan-first) that customize Vite+React+Tailwind templates from pre-researched business data. Pre-research via APIs, media acquisition, brand extraction, visual inspection via GPT Image 2 vision, R2 upload (per-file content-type by extension), D1 status updates. Supports all business types: SaaS, portfolio, non-profit, restaurant, salon, medical, legal, retail, tech.

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。

URQL GraphQL client patterns — the exchange pipeline, document and normalized caching, queries, mutations, subscriptions, and authentication

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。

Backend specification planning frameworks. Use when a spec touches API endpoints, database schema, middleware, or auth. Covers endpoint contracts with request/response shapes, error catalogs, auth per endpoint, schema design with constraints and indexes, migration strategy, and middleware pipeline ordering.

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。

Native MongoDB driver (the mongodb npm package) - MongoClient lifecycle, typed collections, CRUD result shapes, cursors, aggregation pipelines, index design, transactions

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。

Upstash serverless Redis -- REST-based client, auto-serialization, pipelines, rate limiting, QStash, edge compatibility, global replication

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。

Curate Gaia bot crawler branches into real-skill catalog entries and clean up processed bot/* branches. Use when the user says: "process bot branches", "curate the crawl output", "clean up bot proposals", "run bot curation", "merge the crawler results", "review bot/* branches", "ingest crawler data", or explicitly types /gaia-bot-curate. Also triggers when the user asks to review pending `bot/*` branches, turn crawler JSON proposals into registry nodes, decide which crawled skills to accept vs reject, or batch-add skills from an automated crawl run. This skill handles the full pipeline: fetch remote bot branches, triage proposals, run `gaia dev` mutations, validate, open a PR, and delete consumed bot branches.

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 human adjudication / hand-labeling sheets from LLM-pipeline data without evidence truncation. Use when: (1) preparing a CSV/Excel sheet for a human to rule on cases an LLM classifier or rater panel judged, (2) a labeler reports "there is no information to label from" or cells look empty in Excel, (3) excerpt columns cluster at one exact length (e.g. all 1,500 chars — a hard truncation cap). Covers: full rating-basis recovery, Excel 32,767-char cell cap, multi-line CSV mangling, ruling dropdowns, companion text files.

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。

Account-based marketing for B2B SaaS. Use this skill to build a target account list, tier it 1:1 / 1:few / 1:many, size it against sales capacity, turn intent and engagement signals into plays, write the multi-channel orchestration contract each tier runs on, and measure the program in account coverage, penetration and pipeline instead of MQLs. Also triggers on: ABM, account-based marketing, target account list, TAL, named accounts, account tiering, 1:1 ABM, one-to-few, programmatic ABM, account selection, ideal customer profile fit, intent data, buying signals, 6sense, Bombora, Demandbase, account penetration, account coverage, sales and marketing alignment on accounts, enterprise account program.

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。

Create, run, and maintain API test collections using Bruno (OpenCollection YAML format and legacy Bru format). Use when the user wants to: (1) create a Bruno API test collection from scratch or from OpenAPI/Swagger specs, (2) write API request files with tests and assertions, (3) run API tests using bru CLI, (4) generate test reports (HTML, JUnit, JSON), (5) set up CI/CD pipelines (GitHub Actions) for automated API testing, (6) debug or fix failing Bruno API tests, (7) add environment configurations for API testing, (8) chain API requests with data extraction, or (9) work with any .yml/.bru Bruno collection files. Triggers on mentions of 'Bruno', 'bru CLI', 'API testing collection', 'OpenCollection', or requests to automate API testing with file-based collections.

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。

Building robust document ingestion — chunking, metadata, and multi-format pipelines for search and RAG.

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。

Manage CRM systems — data architecture, pipeline design, automation, user adoption, and reporting.

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。

Design annotation pipelines that produce reliable training labels — guidelines, agreement measurement, and quality control.

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 bioinformatics analysis — sequence QC, alignment, and omics pipelines for research data.

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。

Configure a complete iOS App Store publishing pipeline — sets up Fastlane with code signing (match), App Store Connect API key, TestFlight and release lanes, screenshot automation, metadata templates, review compliance checklist, and phased rollout. Use for any iOS or Flutter app ready to submit to the App Store.

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。

Evaluate survey data pipelines and research methodology. Analyzes response bias detection (speeders, straight-liners, bots), statistical significance testing (t-test, chi-square, ANOVA with Bonferroni correction), sentiment and theme extraction from open-ended responses.

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 pharmaceutical regulatory compliance -- inspection readiness, CAPA system effectiveness, change control pipeline, data integrity (ALCOA+), and validation lifecycle tracking..

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 manufacturing predictive maintenance systems for OPC-UA/MQTT sensor data pipelines, time-series storage retention, ML model lifecycle (training-serving skew, drift detection, retraining triggers), MTBF/MTTF/MTTR reliability calculations, Weibull survival analysis.

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 laboratory automation systems -- LIMS architecture, instrument connectivity (SiLA 2, OPC-UA, serial drivers), sample tracking and chain of custody, protocol workflow engines, data acquisition pipelines, and regulatory compliance (21 CFR Part 11 electronic records/signatures.

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。