「pipelines」の検索結果

GitHub Actions CI/CD pipeline configuration for Golang projects — workflow files for test, lint, SAST, coverage and vulnerability-scan jobs, Dependabot and Renovate config files, GoReleaser release pipelines, Docker build/push, repository security settings, and AI-driven PR review. Use when setting up or improving Go project CI, writing or fixing `.github/workflows/*.yml`, adding a linter or security scanner as a pipeline job, wiring automated dependency-update bots, or adding quality gates. Covers wiring tools into a pipeline, not the analysis they perform: do NOT use for choosing or interpreting security findings (→ See `samber/cc-skills-golang@golang-security` skill) or for choosing, upgrading, or auditing dependency versions (→ See `samber/cc-skills-golang@golang-dependency-management` skill).

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Functional programming helpers for Golang using samber/lo — 500+ type-safe generic functions for slices, maps, channels, strings, math, tuples, and concurrency (Map, Filter, Reduce, GroupBy, Chunk, Flatten, Find, Uniq, etc.). Core immutable package (lo), concurrent variants (lo/parallel aka lop), in-place mutations (lo/mutable aka lom), lazy iterators (lo/it aka loi for Go 1.23+), and experimental SIMD (lo/exp/simd). Apply when using or adopting samber/lo, when the codebase imports github.com/samber/lo, or when implementing functional-style data transformations in Go. Not for streaming pipelines (→ See `samber/cc-skills-golang@golang-samber-ro` skill).

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Structured logging extensions for Golang using samber/slog-**** packages — multi-handler pipelines (slog-multi), log sampling (slog-sampling), attribute formatting (slog-formatter), HTTP middleware (slog-fiber, slog-gin, slog-chi, slog-echo), and backend routing (slog-datadog, slog-sentry, slog-loki, slog-syslog, slog-logstash, slog-graylog...). Apply when using or adopting slog, or when the codebase already imports any github.com/samber/slog-* package.

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Security best practices and vulnerability prevention for Golang — injection (SQL, command, XSS), cryptography, path traversal, SSRF and HTTP security headers, cookies, secrets management, memory safety, PII in logs, STRIDE/DREAD threat modeling, plus `gosec` SAST, race detection, and fuzz testing. Apply when writing, reviewing, or auditing Go code for security, or when touching crypto, file or network I/O, secrets, user input, or authentication. Not for non-exploitable defensive bugs such as nil panics or slice aliasing (→ See `samber/cc-skills-golang@golang-safety` skill), dependency vulnerability scanning with govulncheck (→ See `samber/cc-skills-golang@golang-dependency-management` skill), or wiring security scanners into CI pipelines (→ See `samber/cc-skills-golang@golang-continuous-integration` skill).

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Golang concurrency design — goroutine lifecycle and leak prevention, channels and `select`, channel ownership and direction, `sync.Mutex`/`RWMutex`/`sync.Map`/`sync.Once`/atomics, `errgroup`, `singleflight`, worker pools, and fan-out/fan-in pipelines. Use when writing or reviewing concurrent Go code, when choosing between channels and mutexes, when protecting a shared map or counter, or when a goroutine has no clear exit. Not for defensive coding unrelated to concurrency such as nil panics, slice aliasing, or numeric overflow (→ See `samber/cc-skills-golang@golang-safety` skill), and not for debugging a specific hung, crashing, or racing program after the fact (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

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Run Playwright tests at scale using Azure Playwright Workspaces (formerly Microsoft Playwright Testing). Use when scaling browser tests across cloud-hosted browsers, integrating with CI/CD pipelines, or publishing test results to the Azure portal.

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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".

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Compose IRQL (Incident Response Query Language) queries for Kusto cybersecurity investigations. Translates natural language hunting questions into composable IRQL pipelines using Get_*, Extract_*, and Enrich_* functions. WHEN: IRQL query, security hunt, threat hunting KQL, incident response query, compose hunting pipeline, failed logins, phishing investigation, lateral movement, process execution, file creation events.

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Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.

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Use when the user asks to "script this short video", "write a TikTok / Reels / Shorts script", "给这条抖音或视频号视频写脚本", or "fix the hook — viewers drop off in the first seconds"; produces timestamped beat-sheet scripts on the retention-gate model (0-2s hook / 2-5s confirmation / 5-15s payoff / loop-or-CTA) with per-scene script lines, on-screen text for muted viewing, asset keywords, and 9:16 format-param rows per platform norm card for TikTok, Reels, Shorts, 抖音, and 视频号 — plus 2-3 hook options from named hook families and an AI-content disclosure line by default on realistic synthetic media. Spec-only: rendering, TTS, and publishing stay with the user's own tools — no pipelines, no upload automation. Not for creator video briefs — use brief-generator; long-form video SEO belongs to content-writer. 短视频脚本/抖音分镜/开头钩子/竖屏9:16

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Creates and automates custom image builds with EC2 Image Builder - Linux, Windows, and macOS AMIs, and container images to ECR. Covers the build IAM role, Amazon-managed and custom components, image recipes, infrastructure and distribution configuration (launch templates, SSM parameters, other Regions), one-off builds, recurring scheduled pipelines for golden AMI automation and OS patching, custom image workflows, and diagnosing failed builds. Applies when creating, automating, or scheduling AMI or container image builds with Image Builder, or when debugging a failed build. Not for launching instances from existing AMIs, AMI lifecycle/retirement, or general EC2 fleet management.

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Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.

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Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

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Build an Attio workflow around lists and entries while keeping record data, list-specific attributes, notes, and tasks correctly separated. Use when implementing pipelines, queues, or operational CRM processes. Trigger with "Attio lists", "Attio entries", or "Attio pipeline workflow".

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Implement Apollo.io webhook and event-driven integrations. Use when receiving Apollo notifications, syncing data on changes, or building event-driven pipelines from Apollo activity. Trigger with phrases like "apollo webhooks", "apollo events", "apollo notifications", "apollo webhook handler", "apollo triggers".

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Deploy Apollo.io integrations to production. Use when deploying Apollo integrations, configuring production environments, or setting up deployment pipelines. Trigger with phrases like "deploy apollo", "apollo production deploy", "apollo deployment pipeline", "apollo to production".

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Manage Apify datasets, key-value stores, and request queues programmatically, and orchestrate multi-Actor pipelines. Use when you need to read or write Apify datasets, export scraped data to CSV/JSON/XLSX, store config or binary artifacts in a key-value store, manage a resumable request queue, chain Actors into a scrape → transform → export pipeline, or monitor Actor run status and cost. Trigger with "apify dataset", "apify key-value store", "apify storage", "export apify data", "apify pipeline", "apify request queue".

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Configure Apollo.io CI/CD integration. Use when setting up automated testing, continuous integration, or deployment pipelines for Apollo integrations. Trigger with phrases like "apollo ci", "apollo github actions", "apollo pipeline", "apollo ci/cd", "apollo automated tests".

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Deploy Apify Actors and integrate scraping into external applications. Use when deploying an Actor to the platform, integrating Actor results into a web app (Next.js, Express), wiring webhooks, or scheduling scraping pipelines. Trigger with "deploy apify actor", "apify Vercel integration", "apify production deploy", "integrate apify results", "apify API endpoint".

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Production-grade architecture patterns for Apify-powered applications. Use when designing scraping infrastructure, building multi-Actor pipelines, or integrating Apify into a larger system architecture. Trigger with "apify architecture", "apify best practices", "apify project structure", "scraping architecture", "apify system design".

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Configure CI/CD pipelines for Apify Actor builds and deployments. Use when automating Actor deployment via GitHub Actions, running integration tests against the live Apify API, or building CI/CD for scrapers that push to the Apify platform. Trigger with "apify CI", "apify GitHub Actions", "apify automated deploy", "CI apify", "apify pipeline", "auto deploy actor".

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Production readiness checklist for Anima design-to-code pipelines. Use when deploying automated design-to-code services, preparing CI/CD Figma-to-code automation, or validating output quality before production. Trigger with: "anima production", "anima go-live", "anima prod checklist".

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Use Figma webhooks to trigger automatic Anima code generation on design changes. Use when building event-driven design-to-code pipelines, auto-generating components when Figma files change, or integrating design updates into CI. Trigger with: "anima webhook", "figma webhook", "anima auto-generate on change".

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Configure CI/CD pipelines for Anthropic Claude API integrations. Use when setting up automated testing, prompt regression tests, or CI validation for Claude-powered features. Trigger with phrases like "anthropic ci", "claude ci/cd", "test claude in pipeline", "anthropic github actions".

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