인프라·DevOps 스킬

Deploy n8n on a Linux server using Docker Compose and Caddy for automatic HTTPS. Choose a single instance or queue workers, configure secrets and Python task runners, and follow procedures for updates, backups, and recovery.

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• Deploy n8n on a personal VPS with HTTPS
• Set up queue workers for parallel workflow execution
• Update, back up, or restore an n8n instance

Troubleshoot Feishu plugin authorization and permission problems using an FAQ and a diagnostic command. Check account configuration, API connectivity, app permissions, and user authorization, then review a report with issues and suggested fixes.

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• Investigate errors that persist after repeated authorization
• Review app permissions and user authorization together
• Check account configuration and API connectivity

Guides LoopX project setup and repair, including goal configuration, shared registry synchronization, stale dashboard status, and command-line routing issues. It distinguishes project lifecycle maintenance from ordinary task execution and directs each operation to its relevant procedure.

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• Connecting a project and configuring its goal
• Repairing stale dashboard status
• Diagnosing registry and history routing mismatches

Prepare and publish DeepChat releases through its project-specific workflow. Update versions and bilingual changelogs, run release checks, manage release branches and tags, and resume unfinished releases before publishing after release PR approval.

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• Prepare a version update and bilingual changelog
• Check which release steps remain
• Resume an unfinished release after metadata changes

Check whether an Apache Hudi metadata table is synchronized, lists stored files consistently, and compacts on schedule. Interpret a read-only health report to identify record index sizing problems and recommend specific next steps.

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• Checking metadata synchronization
• Verifying metadata file listings
• Investigating delayed metadata compaction

Investigate failed Apache Hudi jobs using stacktraces, configuration, and logs. Identify known failure patterns, explain the failing phase and available evidence, and provide ordered mitigation steps once the necessary details are confirmed.

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• Investigating failed Hudi writes
• Tracing compaction failures
• Diagnosing memory or concurrency errors

Check whether Apache Hudi table maintenance is keeping pace with incoming data. Runs a read-only utility, interprets its JSON report, and explains unhealthy or skipped checks with concrete next steps.

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• Checking Hudi table maintenance health
• Investigating delayed compaction or cleaning
• Finding why the active timeline keeps growing

Check whether an Ouroboros update is available and upgrade after the user chooses to proceed. The native updater preserves the installation manager, environment, and optional dependencies, then provides restart guidance for the host integrations.

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• Checking for an available Ouroboros update
• Upgrading while preserving the installation configuration
• Refreshing both Claude Code and Codex integrations

Manage Ouroboros settings through a browser interface or chat commands. Choose agents and models globally or for individual stages, change the LLM backend, review saved settings, and restore the configuration from before the last change.

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• Changing the default agent and model
• Assigning different agents to individual stages
• Editing settings on a remote host

Stop stuck Ouroboros executions or background jobs by ID, or cancel all running and paused executions in the current project. It reports whether cancellation is complete or has been requested, with guidance for checking the final status.

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• Stopping a stuck execution
• Cancelling a background job by ID
• Cancelling all active executions in a project

Deploy an application to an environment that has already been configured. Use it when the destination is established and the next task is to put the application into that environment.

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• Deploying an application to a configured environment
• Putting an application into its established destination
• Requesting deployment after environment configuration

Diagnose command failures before attributing them to sandbox restrictions, which limit access to files and networks. Check execution methods, command matching, and network behavior, then prepare an evidence-based report for the person responsible for settings.

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• Investigating permission errors such as EPERM
• Separating network restrictions from hostname failures
• Checking why an excluded command still fails

Check release readiness by reviewing tests, type checks, linting, builds, security findings, documentation, and environment settings. Get a report separating deployment blockers from warnings, plus action items and checks for post-deployment health and rollback preparation.

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• Checking readiness before a production deployment
• Reviewing staging environment settings
• Adding release checks to CI/CD

Take an existing pull request through automated check results, user approval, merging, deployment verification, and a production health check. Report outcomes and timings, with investigation or rollback options when deployment problems appear.

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• Merge a pull request after CI and test checks
• Verify deployment after a merge
• Check production status, response time, and page content

Check CI, the automated checks run on code changes, for the current branch alongside recent commits. Supports GitLab CI and GitHub Actions, with optional pull request or merge request numbers and links when command-line tools are unavailable.

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• Checking CI progress after a push
• Reviewing checks for a specific pull request
• Viewing a GitLab merge request

Push the current Git branch and get a link to its GitLab CI or GitHub Actions runs. The workflow checks for uncommitted changes and blocks direct pushes to main, master, and production before proceeding.

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• Pushing a committed feature branch
• Finding the GitLab CI tracking link after a push
• Finding GitHub Actions runs for the current branch

Prepare a branch for a pull request by running local tests, checking types for Node projects, pushing commits, and showing the CI pipeline link. Failed checks stop the workflow before the push.

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• Running tests before opening a pull request
• Checking Node types before pushing commits
• Getting the CI link after a push

Monitor a live application after deployment and compare it with a saved baseline, a record of its earlier state. Check page responses, loading times, and new errors, flag persistent changes, and produce a per-page health report.

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• Recording a healthy baseline before deployment
• Monitoring production immediately after deployment
• Checking key pages for slower responses or failures

Register a deployed agent with Gemini, choose the appropriate registration method, and troubleshoot setup problems. Also covers managing agents and registering MCP servers, which expose tools to agents, in Google Cloud Agent Registry.

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• Registering a deployed agent with Gemini
• Automating registration in a release pipeline
• Listing and updating registered agents

Set up monitoring for deployed agents to inspect execution paths, latency, and model interactions. It explains Cloud Trace, prompt and response logging, BigQuery analytics, and external monitoring integrations, with checks for missing data and unwanted content capture.

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• Investigating agent latency and execution errors
• Configuring prompt and response logging
• Analyzing agent events in BigQuery

Plan and carry out agent deployments on Google Cloud using agents-cli. The guide covers deployment targets, permissions, secrets, automated build and deployment pipelines, verification, and recovery procedures for agents built with supported frameworks.

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• Comparing Agent Runtime, Cloud Run, and GKE
• Setting up automated build and deployment pipelines
• Configuring secrets and service account permissions

Inspect production AI agents in FailproofAI Cloud using the fp CLI. Review errors, run histories, quality scores, and usage, then manage issues, audits, user access, and scoped API keys for the team's deployment.

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• Investigating failed agent runs
• Checking for drops in quality scores
• Running audits and triaging findings

Guides Kaku’s macOS release process from prerequisite checks through building, signing, Apple notarization, and GitHub publication. It covers stable releases, Homebrew tap updates, rolling Nightly previews, and resuming failed stages using existing release scripts.

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• Publish a stable Kaku release
• Distribute a rolling Nightly preview
• Check release prerequisites and credentials

Prepare a software release by analyzing code changes, writing English and Chinese release notes, updating versions with changeset, and creating commits and a release pull request for review before automated publishing.

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• Preparing a patch release after bug fixes
• Writing English and Chinese release notes
• Creating a release pull request for a protected main branch