Hi - I answer from the OpenSmartRoute documentation: routing, the API, plans and quotas, self-hosting. Ask away, or open a support ticket if you need a person.
Grounded in the docs - follow a source before acting on it.
This repository contains homework solutions for the [ML in Production](https://edu.kyrylai.com/courses/ml-in-production) course. Each module under `module-*` holds an independent example or exercise.
Imported from kyryl-opens-ml/ml-in-production-practice (AGENTS.md). Install upstream with npx skills add kyryl-opens-ml/ml-in-production-practice. Copyright stays with the author.
Contributor Guide
This repository contains homework solutions for the ML in Production course. Each module under module-* holds an independent example or exercise.
Development
Use Python 3.10+.
Format Python code using ruff format and check style with ruff check.
Execute tests with pytest from the repository root. Some modules also provide Makefiles (e.g. module-3/classic-example) with helper commands such as make test.
Ensure all tests pass before committing.
Pull Requests
Keep PR titles concise, e.g. [module-x] <short description>.
Summarize the changes in the body of the PR.
Use it
Copy one of these into your project. Installing also returns the manifest and these snippets.
# after Install: the listing is in your workspace's routing pool - a plan picks it for its slot
curl -s -X POST https://api.opensmartroute.ai/api/v1/route -H 'Authorization: Bearer $OSR_API_KEY' -H 'Content-Type: application/json' -d '{"text": "...", "plan": true}'
Manifest
An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.
{
"ocm": "1",
"id": "kyryl-opens-ml-ml-in-production-practice-ml-in-productio-e9cedd",
"kind": "skill",
"name": "ml-in-production-practice",
"description": "This repository contains homework solutions for the [ML in Production](https://edu.kyrylai.com/courses/ml-in-production) course. Each module under `module-*` holds an independent example or exercise.",
"publisher": "kyryl-opens-ml",
"version": "1.0.0",
"capabilities": {
"domains": [
"general"
],
"tags": [
"skill-md",
"github-agents"
],
"languages": [
"en"
]
},
"quality_prior": 0.6,
"examples": [
"This repository contains homework solutions for the [ML in Production](https://edu.kyrylai.com/courses/ml-in-production) course. Each module under `module-*` holds an independent example or exercise."
],
"primary": false,
"metadata": {
"source": {
"provider": "github-agents",
"repository": "https://github.com/kyryl-opens-ml/ml-in-production-practice",
"path": "AGENTS.md",
"ref": "e7c37c872c217a5968d6cbd0f7d0335e86431239",
"url": "https://github.com/kyryl-opens-ml/ml-in-production-practice/blob/e7c37c872c217a5968d6cbd0f7d0335e86431239/AGENTS.md",
"key": "kyryl-opens-ml/ml-in-production-practice/AGENTS.md"
}
},
"instructions": "# Contributor Guide\n\nThis repository contains homework solutions for the [ML in Production](https://edu.kyrylai.com/courses/ml-in-production) course. Each module under `module-*` holds an independent example or exercise.\n\n## Development\n- Use Python 3.10+.\n- Format Python code using `ruff format` and check style with `ruff check`.\n- Execute tests with `pytest` from the repository root. Some modules also provide Makefiles (e.g. `module-3/classic-example`) with helper commands such as `make test`.\n- Ensure all tests pass before committing.\n\n## Pull Requests\n- Keep PR titles concise, e.g. `[modu",
"cost": {
"context_tokens": 168
}
}
Fetch it by URL: GET /api/v1/registry/kyryl-opens-ml-ml-in-production-practice-ml-in-productio-e9cedd/manifest?version=1.0.0
Reviews
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