appify

name	appify
description	Build and deploy an inference.sh app from a conversation — scaffold, implement, test, deploy, and configure pricing. Use when the user says 'appify', 'make an app', 'deploy this as an app', or when a working API integration, model wrapper, or processing pipeline should become a reusable cloud app.
allowed-tools	Bash(belt *), Read, Write, Edit, Glob, Grep, Agent

Appify

Turn an API integration, model wrapper, or processing pipeline from this conversation into a deployed inference.sh app. Apps run in the cloud, have typed input/output schemas, and are callable via belt app run or the API.

When to use

• A working API integration should become a reusable cloud app
• The user says "appify", "make an app", "deploy this"
• A script or notebook should become a callable service
• A model needs to be wrapped with pre/post processing

Process

0. Analyze the conversation first [MANDATORY]

Before asking anything, review what happened in this conversation. Look for:

• API calls that worked (HTTP clients, SDK usage, model inference)
• Data processing pipelines (transform input → call API → format output)
• Working scripts that could be packaged as cloud functions

If you find app candidates, present them:

"Based on this session, here's what could become an app:

1. — , takes , returns
2. — , takes , returns Which one should we build? Or describe something different."

Only ask "what kind of app?" if the conversation has no relevant context.

1. Scaffold

belt app init <app-name>              # python (default)
belt app init <app-name> --lang node  # node.js

This creates the directory with inf.yml, inference.py/inference.js, and schema files. Never create these by hand.

Exception: when adding to an existing provider directory with shared helpers and symlinks (e.g. api/pruna/ with helper.py), create files manually since belt app init doesn't support symlinked shared modules.

2. Configure inf.yml

Key fields:

name: my-app
description: what it does
version: 0.0.1
category: image|video|audio|text|3d|search|utility
python: "3.11"  # or node: "20"

3. Implement inference.py / inference.js

The scaffold generates RunInput, RunOutput, and App classes. Edit these to match your app's purpose.

Python pattern:

from inferencesh import BaseApp, BaseAppSetup, File, OutputMeta
from pydantic import BaseModel, Field
from typing import Optional

class RunInput(BaseModel):
    text: str = Field(description="text to process")
    max_length: int = Field(default=100, description="max output length")

class RunOutput(BaseModel):
    result: str = Field(description="processed result")

class App(BaseApp):
    async def setup(self, config: BaseAppSetup):
        # one-time init (load models, create clients)
        pass

    async def run(self, input_data: RunInput) -> RunOutput:
        # the actual work
        return RunOutput(result="...")

If you need to chain multiple apps together, use a flow instead (/flowify). Apps don't call other apps — that's what flows are for.

For upstream API costs, use RawMeta(cost=cents) in output_meta:

from inferencesh import RawMeta

class RunOutput(BaseModel):
    result: str
    output_meta: dict = None

# in run():
return RunOutput(
    result=result,
    output_meta=RawMeta(cost=cost_in_cents)
)

4. Test locally

belt app test --save-example          # generate sample input
belt app test --input input.json      # test with sample
belt app test --input '{"prompt": "test"}'  # inline test
belt app test --debug                 # debug mode

Fix any errors before deploying.

5. Deploy

belt app deploy                       # deploy to cloud
belt app deploy --dry-run             # validate without deploying
belt app deploy --stage               # deploy as staged version

6. Test in cloud

belt app run <namespace/app-name> --input '{"prompt": "test"}'
belt app run <namespace/app-name> --input input.json --save output.png

7. Configure pricing (if publishing to store)

Use the pricing skill:

belt skill use infsh/configure-pricing-for-inference-sh-apps

Or use the pricing agent directly:

belt agent run infsh/pricing-agent "configure pricing" --context version_id=<vid> --context app_id=<aid>

Troubleshooting

Problem	Cause	Fix
belt app test fails with import error	missing dependency	add to requirements.txt or package.json
deploy succeeds but app fails at runtime	dependency not in requirements.txt	check belt task logs <task-id>
"no inf.yml found"	wrong directory	cd into the app directory first
upstream API returns non-200 success	some APIs return 201/202	check status_code not in (200, 201) not != 200

Rules

• Always belt app init to scaffold — don't create files by hand (except shared-helper provider directories)
• Always test locally before deploying — belt app test catches most issues
• Use RawMeta(cost=cents) for upstream API costs — the platform needs this for pricing
• Deploy then test in cloud — local and cloud can differ (dependencies, GPU, env vars)
• Use the pricing skill for pricing — don't write CEL expressions by hand