BentoML
SkillAI & models"Use BentoML to author model-serving Services, build and
Available today. Use it from your connected AI after setup.
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Then ask your AI: use the BentoML skill
What this skill tells your AI
The instructions your AI receives, as published by vectorspacelab/arex-skill in skills/repositories/repo-skills/bentoml/SKILL.md and read by ahel’s review.
Use this repo skill for BentoML, a Python framework for building, serving, packaging, and deploying AI/model inference APIs. This root skill is a router: read the focused sub-skill that matches the user’s task, then use its bundled references and scripts.
First Checks
- Install:
pip install -U bentoml; optional extras includebentoml[grpc],bentoml[io],bentoml[tracing-otlp],bentoml[monitor-otlp], and framework-specific packages needed by the user’s model. - Import check:
python -c "import bentoml; print(bentoml.__version__)". - CLI check:
bentoml --helpandbentoml env. - Provenance check: read
references/repo-provenance.mdbefore deciding whether this skill is stale for a current checkout. - Troubleshooting: read
references/troubleshooting.mdfor cross-cutting install/import, CLI, environment, optional dependency, and cloud safety issues.
Route The Task
| User task | Use this sub-skill | Typical triggers |
|---|---|---|
Write or debug service.py, APIs, tasks, batching, lifecycle hooks, ASGI/Gradio/WebSocket/streaming, IO specs, runtime image hints | sub-skills/service-authoring/SKILL.md | @bentoml.service, @bentoml.api, @bentoml.task, bentoml.importing, service import target, API schema |
Build a Bento, author bentofile.yaml, include/exclude files, configure build dependencies/images, local Bento store, Docker/containerize | sub-skills/packaging-and-containerization/SKILL.md | bentoml build, bentofile.yaml, bentoml.build, containerize, .bentoignore, build context |
| Run a local server or call endpoints using Python clients, curl/OpenAPI, HTTP/gRPC, streaming, WebSocket, server flags | sub-skills/serving-and-clients/SKILL.md | bentoml serve, bentoml.serve, SyncHTTPClient, AsyncHTTPClient, port, host, reload, gRPC |
| Save/load/list/import/export models and choose framework helpers or optional dependencies | sub-skills/model-management/SKILL.md | bentoml.models, model tags, Model Store, sklearn/pytorch/transformers/mlflow/xgboost helpers, missing model |
| Use CLI command families or BentoCloud login/deploy/deployment/secret/api-token/codespace/push/pull workflows | sub-skills/cli-and-cloud/SKILL.md | bentoml deploy, bentoml deployment, bentoml cloud, secrets, tokens, context, cluster, BentoCloud |
| Configure logging, metrics, tracing, monitoring, resources, scaling, gateways, testing, config files, production diagnostics | sub-skills/observability-and-operations/SKILL.md | metrics missing, tracing, config YAML, GPU resources, workers, autoscaling, gateways, production readiness |
Common End-To-End Flow
- Author a Service with
sub-skills/service-authoring/SKILL.md; validate the import target before serving. - Serve locally and call endpoints with
sub-skills/serving-and-clients/SKILL.md; keep server/client debugging separate from service authoring. - Package with
sub-skills/packaging-and-containerization/SKILL.md; validatebentofile.yamlorbentoml.build(...)options before expensive builds. - Use
sub-skills/model-management/SKILL.mdwhen the workflow needs saved model tags, framework helpers, or model inclusion in a Bento. - Deploy or manage cloud resources with
sub-skills/cli-and-cloud/SKILL.md; treat credentialed and destructive operations as explicit user-run or user-approved actions. - Add telemetry, resources, scaling, config, and production validation with
sub-skills/observability-and-operations/SKILL.md.
Safe Bundled Helpers
sub-skills/service-authoring/scripts/create_minimal_service.pygenerates a starter service file.sub-skills/service-authoring/scripts/validate_service_target.pyimports and inspects a service target without serving it.sub-skills/packaging-and-containerization/scripts/validate_bentofile.pystatically checks abentofile.yaml.sub-skills/serving-and-clients/scripts/serve_command_builder.pyconstructs dry-runbentoml servecommands.sub-skills/model-management/scripts/check_framework_extra.pychecks optional framework imports.sub-skills/cli-and-cloud/scripts/inspect_bentoml_cli.pyrenders safe CLI help without contacting BentoCloud.sub-skills/observability-and-operations/scripts/check_observability_config.pychecks local config shapes without external collectors.
Side-Effect Boundaries
- Safe by default: import checks, CLI help, static YAML/config validation, command construction, and read-only local store inspection.
- Potentially mutating:
bentoml build, model save/delete/import/export, local store changes, config writes, and server startup. - External or credentialed:
bentoml deploy,cloud,deployment,secret,api-token,push,pull, Docker/container builds, OTLP/exporter endpoints, and cloud scaling changes. - Expensive or environment-specific: framework model downloads, GPU workloads, large image builds, long-running servers, and integration tests requiring Docker/cloud credentials.
Version Notes
This skill was generated from BentoML source commit 73c4dbead99be6515fa25fcd91e348ac30f5c22e on branch main and installed package version 0.0.0.post1+g73c4dbead. If the current checkout, package version, CLI flags, or public SDK signatures differ, run a DisCo refresh before relying on detailed guidance.
Signals
- GitHub stars
- 266
- Forks
- 21
- Last commit
- Sep 2026
ahel review
K1binfo
installs-packagesK1binfo
installs-packages (in references/troubleshooting.md)
Automated review, not a security audit. Ruleset v1+k2.
Advanced
- Catalog kind
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- Gateway key
bentoml-vectorspacelab- Source
- github.com/vectorspacelab/arex-skill