云、CI/CD、监控、部署（6835 个）
Analyze Apify spending from Actor run history and tune memory, crawl duration, and proxy traffic. Produce cost comparisons and monthly reports, with an optional budget guard that stops runs once spending exceeds a set limit.
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Prepare Apollo.io integrations for production on Vercel, GCP Cloud Run, or Kubernetes. Provides deployment configurations, secure API key handling, connectivity health checks, and validation scripts to check readiness before release.
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Harden an Apify setup by protecting API tokens and proxy credentials, separating secrets by environment, verifying webhooks, and keeping sensitive Actor data private. It also outlines token rotation and response steps for an exposed credential.
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Set up GitHub Actions for Apollo.io integrations, including linting, type checks, mock based unit tests, and live API checks on pushes to the main branch. Add a health check and scan source files for hardcoded API keys.
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Improve Apify Actor runs by comparing baseline metrics and adjusting crawler choice, concurrency, memory, and proxies. Get workload-specific recommendations for speed, reliability, and cost, along with before-and-after measurements.
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Prepare an Apify Actor, a program running on the platform, for production. Check configuration and builds, then set up scheduled runs, event notifications, health monitoring, rollback, and a spending limit that stops over-budget runs.
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Respond to Apollo.io API incidents with severity classification, diagnostic scripts, and recovery procedures. Use temporary call blocking and cached responses to handle failures, then document the timeline, customer impact, and corrective actions in a post-incident review.
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Plan capacity and load tests for services using Claude API. Estimate request and token demand, measure latency and costs, and evaluate queues, shared rate limiting, and scaling within the organization's API limits.
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Analyze request handling, data pipelines, and queues to find system capacity limits. Review processing rates and resource usage, identify bottlenecks, and compare scaling options to produce recommendations for capacity planning.
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Prepare Claude API services for deployment using Docker, Cloud Run, or Kubernetes. Configure runtime secrets, health checks, staging tests, gradual releases, and rollback procedures, then document deployment evidence without exposing credentials or request content.
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Set up organization, Workspace, and role-based access rules for Anthropic teams. The skill helps map people and services to limited permissions, constrain model and usage access, and plan key rotation, revocation, and sandbox checks.
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Assess current infrastructure use across CPU, memory, storage, network, and request rates, then use historical trends to estimate future needs. The skill identifies possible bottlenecks and outlines vertical or horizontal scaling options with cost estimates for capacity planning.
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Analyze application logs to find slow requests, recurring errors, and unusual resource usage. Group related issues into reports with response times, error frequencies, and recommendations for performance improvements and clearer logging.
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Set up GitHub Actions to turn approved Figma components into code with Anima. Schedule generation, validate the output, and create pull requests so maintainers can review design changes before merging them.
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Design Alchemy traffic controls from observed account usage and workload patterns. This skill helps set shared admission limits, bounded retries, and load tests so teams can handle rate limits, protect critical requests, and define clear degraded behavior.
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Collect evidence for reproducible Algolia search or indexing failures, including client versions, reproduction steps, and redacted request details. Package the findings with a file manifest and secret scan results for review before a support handoff.
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Bring performance metrics from applications, databases, caches, and infrastructure into a central monitoring setup. The skill helps choose a collection tool, define consistent metric names, configure sources, and create dashboards and alerts for shared visibility.
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Review a specific Alchemy-backed release using evidence about supported features, security, data handling, capacity, cost, monitoring, and rollback. It produces a gate-by-gate record of blockers, approvals, waivers, and a go or no-go decision.
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Assemble a reproducible Alchemy incident bundle with sanitized environment details, request IDs, timing, status evidence, and reproduction results. It helps teams prepare support evidence while screening out credentials and sensitive customer data before sharing.
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Prepare an Alchemy service for deployment by checking environment credentials, blockchain connections, and a limited trial of live traffic. Produce deployment evidence, promotion decisions, and rollback records for review before expanding traffic.
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Diagnose Algolia request pressure using actual response details, client behavior, and queue data. Set bounded retries and concurrency, reduce competition between interactive search and batch indexing, and document verification results without assuming a universal quota.
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Investigate an Algolia-backed search problem by separating availability, access, freshness, speed, relevance, and event collection symptoms. Use application, provider, and data pipeline evidence to choose reversible containment, verify recovery with representative queries, and record the incident handoff.
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Plan observability for an application's Algolia search journey. Define measurements for availability, latency, index freshness, relevance, and event health, then map them to dashboards, alerts, traces, and service objectives using product baselines.
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Analyze Alchemy usage and charges by application and workload, forecast spending against the current account contract, and evaluate reversible cost reductions. Produce documented findings, budget alerts, and checks on service quality before adopting changes.
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