truefoundry-helm
Routing note: For ambiguous user intents, use the shared clarification templates in references/intent-clarification.md.
Helm Chart Deployment
Deploy any Helm chart to TrueFoundry -- databases, caches, message queues, vector databases, monitoring tools, or any other OCI-compatible Helm chart. TrueFoundry supports any chart as long as the cluster can pull it from the registry.
Two paths:
- CLI (
tfy apply) -- Write a YAML manifest and apply it. Works everywhere. - REST API (fallback) -- When CLI unavailable, use
tfy-api.sh.
When to Use
- User explicitly wants a Helm chart deployment for a database (PostgreSQL, MySQL, MongoDB, etc.)
- User wants to install a cache (Redis, Memcached)
- User wants to deploy a message queue (RabbitMQ, Kafka, NATS)
- User says "install helm chart", "deploy via helm"
More from truefoundry/tfy-deploy-skills
truefoundry-jobs
Deploys and monitors TrueFoundry batch jobs, scheduled cron jobs, and one-time tasks. Uses YAML manifests with `tfy apply`. Use when deploying jobs, scheduling cron tasks, checking job run status, or viewing execution history. For listing job applications, use `applications` skill.
11truefoundry-ssh-server
Deploys remote development environments with SSH access on TrueFoundry. Use when setting up VS Code Remote workspaces, cloud development machines, GPU dev boxes, or remote coding environments with SSH access.
11truefoundry-docs
Fetches TrueFoundry documentation, API reference, and deployment guides. Use when the user needs platform docs or how-to guidance.
10truefoundry-logs
Views, downloads, and searches application and job logs from TrueFoundry. Supports time-range filtering, pod filtering, and error search.
10truefoundry-secrets
Manages TrueFoundry secret groups and secrets. Handles listing, creating, updating, and deleting secret groups and individual key-value secrets.
10truefoundry-workflows
Builds and deploys data processing and ML training pipelines using TrueFoundry Workflows (built on Flyte). Use when creating DAGs, orchestrating multi-step tasks, scheduling ETL pipelines, or running ML training workflows.
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