Migrating CI/CD pipelines to TeamCity. Use when the user wants to migrate, convert, or switch to TeamCity from GitHub Actions (.github/workflows/) or Bamboo (bamboo-specs/*.yml), even if they only say "move our CI". Other CI systems (GitLab, Jenkins, CircleCI, Azure DevOps, Travis, Bitbucket) are not supported yet.
npx skills add https://github.com/JetBrains/teamcity-cli --skill migrate-to-teamcity
teamcity migrate # detect + convert + write .tc.yml files
teamcity migrate --dry-run --json # preview as structured JSON
teamcity pipeline validate f.tc.yml # schema check
teamcity project vcs create --url <repo-url> --auth anonymous -p ProjectId # create VCS root first
teamcity pipeline create name -p ProjectId -f f.tc.yml --vcs-root <VcsRootId>
teamcity run start PipelineId --watch
Run teamcity migrate from the repo root -- detection scans .github/workflows/ and bamboo-specs/ relative to the current directory.
runs-on, container:/services:) go before pipeline create; server-side configuration (connections, if:-derived branch filters, triggers, notifications) comes after. The checklist below orders them.--json prints {"sources": [...], "results": [...]} to stdout; each result carries outputFile, yaml, needsReview, manualSetup, and validationError.type: script for ./gradlew and ./mvnw. TC's type: gradle/type: maven runners use the agent's version, not the project's. This causes real build failures.teamcity project connection create github-app -p <project> -- its output prints the authorize, App-install, and vcs create follow-up commands. That flow opens a browser; in headless runs pass existing App credentials (--no-manifest --app-id <id> --client-id <id> --private-key-file <pem> --stdin, client secret piped to stdin) or use SSH deploy keys (teamcity project ssh upload with a [email protected]: URL). Public repos: --auth anonymous.teamcity pipeline create takes --vcs-root <id>, not a URL. Create it first with teamcity project vcs create.main. Pass --branch refs/heads/master to teamcity project vcs create if the repo uses master.Goal: get all pipeline jobs green on the TC server, not just generate valid YAML.
Copy this checklist and check off items as you complete them:
Migration progress:
- [ ] Convert: run `teamcity migrate` from the repo root
- [ ] Fix every "Needs review" item, plus "Manual setup" items needing YAML edits (matrix expansion, expression `runs-on`, container/services) -- see mappings.md and gotchas.md
- [ ] Wire up secrets in the YAML: the converter rewrites `${{ secrets.X }}` to `%X%` but does not define it -- store the value (`teamcity project token put <project> <value>`) and add `X: "credentialsJSON:<uuid>"` under the top-level `secrets:` block (see schema.md)
- [ ] Validate: `teamcity pipeline validate <file>` -- only proceed when it passes
- [ ] Create VCS root (`teamcity project vcs create`), then `teamcity pipeline create <name> -p <project> -f <file> --vcs-root <id>`
- [ ] Set up the remaining runtime "Manual setup needed" items before running: registry/cloud connections the steps reference (the first run fails without them), and any `if:`-condition items -- gate converted deploy/release steps via branch filter, execution condition, or a guard in the script so the first run cannot deploy from the wrong branch
- [ ] Run: `teamcity run start <id> --watch`; on failure read `teamcity run log <id> --failed --raw`, fix, `teamcity pipeline push`, re-run until green
- [ ] Do the trigger-only "Manual setup needed" items: triggers, notifications
- [ ] Report: what migrated and what remains manual
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take jetbrains/migrate-to-teamcity from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.