인프라·DevOps 스킬

Set up Loop Engineering through a single command-line tool, choosing a workflow pattern and agent tool. Start in report-only mode, check setup health and readiness, and provide next actions and first-run instructions.

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• Introducing a daily issue and priority review workflow
• Setting up workflows for stalled pull requests or failing CI
• Checking the health of an existing Loop Engineering setup

Classify failures in CI, automated checks run during development, to distinguish code regressions, temporary infrastructure problems, security test failures, and inconsistent results. Record each failure's category, suggested next action, and attempt count in a structured report.

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• Sort failed CI checks by likely cause
• Identify small regressions that may qualify for automatic fixes
• Refer infrastructure and security failures to a human

Analyze failures in CI, the automated checks run during development. Identify the failing job and step, classify the likely cause, and suggest a small fix, continued monitoring, or human review.

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• Identifying failed jobs and steps in CI
• Distinguishing intermittent failures from code regressions
• Routing environment failures to a human

Configure MCP servers, which expose tools to AI agents, in a holaOS workspace. The skill updates workspace.yaml for remote or local servers, environment variable references, and tool permissions, then checks that the configuration follows the workspace format.

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• Adding a remote MCP server
• Updating a local MCP server configuration
• Restricting which tools an agent can use

Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.

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• Deploying a real-time model endpoint
• Scaling a sparse workload down to zero instances
• Running long inference jobs asynchronously

Check the AWS profile, region, account ID, and caller identity before starting AWS work. Read local configuration and validate credentials, then flag missing settings, expired access, or possible IAM restrictions associated with SSO authentication.

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• Check the AWS account and region before deployment
• Identify the active profile when none was specified
• Detect expired credentials or missing configuration

Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.

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• Plan AWS hosting for a language model
• Choose a serving route for an embedding model
• Plan asynchronous inference for image generation

Choose a serving container for models deployed on SageMaker. Match the model type to a compatible image, retrieve its address from the AWS catalog, and check regional availability, CPU or GPU variants, and required host settings.

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• Choose a container for an LLM deployment
• Select images for embeddings and rerankers
• Check regional image addresses and GPU compatibility

Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.

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• Prepare a Python environment for SageMaker deployment
• Separate AWS automation dependencies from system Python
• Check installed AWS package versions

Build and maintain machine learning apps on Hugging Face Spaces. Choose a suitable app framework and computing hardware, load models, deploy files, and check logs and outputs to diagnose build or runtime problems.

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• Publishing a machine learning demo
• Adapting an existing Gradio app for ZeroGPU
• Diagnosing Space build or runtime failures

Prepare SageMaker deployments and training by finding existing execution roles, checking trust settings, and reviewing required permissions. Guides role creation only when necessary and identifies permission issues that require an AWS administrator.

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• Check execution roles before deploying a SageMaker endpoint
• Find an existing role when no ARN is provided
• Investigate IAM AccessDenied errors

Build and run Omnigent’s server and Postgres together with Docker Compose on a laptop or existing Docker host. Use the supplied configuration and troubleshooting guidance to rebuild images, investigate startup problems, or adapt deployment to another platform.

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• Running Omnigent locally with Postgres
• Starting the stack on an existing VPS or EC2 host
• Investigating startup and web interface problems

Prepare a deployment checklist covering tests, approvals, database changes, release steps, and monitoring. Tailor it to the release and document rollback triggers, the conditions for reverting a change, before production deployment begins.

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• Prepare checks for an upcoming production release
• Review a release with database migrations
• Verify feature flags and CI status before deployment

Manage service incidents from initial assessment through recovery review. The skill organizes severity, impact, roles, status updates, and timelines, then drafts a blameless postmortem with root cause analysis and action items assigned to owners.

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• Assess the severity and impact of a new incident
• Draft internal and customer status updates
• Record mitigation steps and an incident timeline

Query, create, update, and delete Tencent Cloud resources through tccli, its command-line tool. The skill checks API documentation before calls, supports browser-based OAuth authorization, and helps interpret results and troubleshoot errors during cloud automation.

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• Listing CVM instances in a specific region
• Creating a pay-as-you-go cloud server
• Checking the list of COS storage buckets

Inspect and manage Tencent Cloud resources with tccli, its command line tool. Covers servers, networking, DNS, certificates, permissions, monitoring, and application deployment, with supporting scripts for login and identifying the current cloud instance.

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• Checking Lighthouse and CVM server status
• Reviewing domain expiry dates and SSL certificates
• Inspecting security groups and access permissions

Prepare and coordinate Octop Python package releases, including version updates, a Chinese changelog, and a pull request. After merging, GitHub Actions handles release tags, package and container publishing, and branch synchronization.

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• Preparing an Octop package release
• Updating version numbers across release files
• Drafting a Chinese changelog from commits

Diagnose slow systems, crashes, and resource problems on servers, PCs, virtual machines, and containers. Quick checks identify affected components, while deeper analysis investigates causes and provides optimization advice or repair plans.

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• Investigating a slow server or PC
• Finding causes of high CPU or low memory
• Checking disk space and network problems

Investigate failed GitHub Actions jobs using logs and recent runs. Receive a report explaining the actual failure trigger, recurrence, any verified breaking commit, and existing pull requests that may fix the issue.

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• Finding the actual cause of a failed job
• Checking whether failures are intermittent or recurring
• Identifying the commit that introduced a failure

Check a project's Codex infrastructure, fix missing dependencies, script permissions, and configuration errors, then repeat the check until failures are resolved. Receive a summary of repairs and any remaining optional-feature warnings.

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• Check the Codex environment after setup
• Fix dependency and script permission errors
• Repair JSON configuration syntax

Add Sentry error reporting and performance monitoring to services, scheduled jobs, and database operations. Follow examples for capturing exceptions with useful context and measuring execution time through spans, which record individual operations for investigation.

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• Adding error reporting to an API
• Recording failures and execution time for scheduled jobs
• Measuring database and asynchronous operations

Prepare and publish a Plannotator release with contributor credits, coordinated version updates, ordered builds, and tag-triggered publishing. The workflow also checks package contents, security scan results, and evidence showing how release files were built.

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• Draft release notes with contributor credits
• Update versions consistently across package files
• Check builds and package contents before release

Manage ClawHub Convex production data migrations from implementation and testing through deployment, execution, verification, and temporary code removal, with dry runs and explicit user approval before destructive changes and cleanup.

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• Backfilling fields in production data
• Removing obsolete fields or table data
• Repairing ownership and relationships

Explore and query measurements stored in Axiom MetricsDB. The skill discovers metrics and tags, checks measurement types and units, and builds MPL queries, Axiom’s metrics query language, to retrieve values and aggregate them over a chosen period.

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• Explore available metrics and tag values
• Check metrics for a specific service or host
• Aggregate values over a selected time range