packages/skills/skills/javascript-sdk/SKILL.md
JavaScript/TypeScript SDK for inference.sh - run AI apps, build agents, integrate 150+ models. Package: @inferencesh/sdk (npm install). Full TypeScript support, streaming, file uploads. Build agents with template or ad-hoc patterns, tool builder API, skills, human approval. Use for: JavaScript integration, TypeScript, Node.js, React, Next.js, frontend apps. Triggers: javascript sdk, typescript sdk, npm install, node.js api, js client, react ai, next.js ai, frontend sdk, @inferencesh/sdk, typescript agent, browser sdk, js integration
npx skillsauth add mediar-ai/skillhubz javascript-sdkInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Build AI applications with the inference.sh JavaScript/TypeScript SDK.

npm install @inferencesh/sdk
import { inference } from '@inferencesh/sdk';
const client = inference({ apiKey: 'inf_your_key' });
// Run an AI app
const result = await client.run({
app: 'infsh/flux-schnell',
input: { prompt: 'A sunset over mountains' }
});
console.log(result.output);
npm install @inferencesh/sdk
# or
yarn add @inferencesh/sdk
# or
pnpm add @inferencesh/sdk
Requirements: Node.js 18.0.0+ (or modern browser with fetch)
import { inference } from '@inferencesh/sdk';
// Direct API key
const client = inference({ apiKey: 'inf_your_key' });
// From environment variable (recommended)
const client = inference({ apiKey: process.env.INFERENCE_API_KEY });
// For frontend apps (use proxy)
const client = inference({ proxyUrl: '/api/inference/proxy' });
Get your API key: Settings → API Keys → Create API Key
const result = await client.run({
app: 'infsh/flux-schnell',
input: { prompt: 'A cat astronaut' }
});
console.log(result.status); // "completed"
console.log(result.output); // Output data
const task = await client.run({
app: 'google/veo-3-1-fast',
input: { prompt: 'Drone flying over mountains' }
}, { wait: false });
console.log(`Task ID: ${task.id}`);
// Check later with client.getTask(task.id)
const stream = await client.run({
app: 'google/veo-3-1-fast',
input: { prompt: 'Ocean waves at sunset' }
}, { stream: true });
for await (const update of stream) {
console.log(`Status: ${update.status}`);
if (update.logs?.length) {
console.log(update.logs.at(-1));
}
}
| Parameter | Type | Description |
|-----------|------|-------------|
| app | string | App ID (namespace/name@version) |
| input | object | Input matching app schema |
| setup | object | Hidden setup configuration |
| infra | string | 'cloud' or 'private' |
| session | string | Session ID for stateful execution |
| session_timeout | number | Idle timeout (1-3600 seconds) |
const result = await client.run({
app: 'image-processor',
input: {
image: '/path/to/image.png' // Auto-uploaded
}
});
// Basic upload
const file = await client.uploadFile('/path/to/image.png');
// With options
const file = await client.uploadFile('/path/to/image.png', {
filename: 'custom_name.png',
contentType: 'image/png',
public: true
});
const result = await client.run({
app: 'image-processor',
input: { image: file.uri }
});
const input = document.querySelector('input[type="file"]');
const file = await client.uploadFile(input.files[0]);
Keep workers warm across multiple calls:
// Start new session
const result = await client.run({
app: 'my-app',
input: { action: 'init' },
session: 'new',
session_timeout: 300 // 5 minutes
});
const sessionId = result.session_id;
// Continue in same session
const result2 = await client.run({
app: 'my-app',
input: { action: 'process' },
session: sessionId
});
Use pre-built agents from your workspace:
const agent = client.agent('my-team/support-agent@latest');
// Send message
const response = await agent.sendMessage('Hello!');
console.log(response.text);
// Multi-turn conversation
const response2 = await agent.sendMessage('Tell me more');
// Reset conversation
agent.reset();
// Get chat history
const chat = await agent.getChat();
Create custom agents programmatically:
import { tool, string, number, appTool } from '@inferencesh/sdk';
// Define tools
const calculator = tool('calculate')
.describe('Perform a calculation')
.param('expression', string('Math expression'))
.build();
const imageGen = appTool('generate_image', 'infsh/flux-schnell@latest')
.describe('Generate an image')
.param('prompt', string('Image description'))
.build();
// Create agent
const agent = client.agent({
core_app: { ref: 'infsh/claude-sonnet-4@latest' },
system_prompt: 'You are a helpful assistant.',
tools: [calculator, imageGen],
temperature: 0.7,
max_tokens: 4096
});
const response = await agent.sendMessage('What is 25 * 4?');
| Model | App Reference |
|-------|---------------|
| Claude Sonnet 4 | infsh/claude-sonnet-4@latest |
| Claude 3.5 Haiku | infsh/claude-haiku-35@latest |
| GPT-4o | infsh/gpt-4o@latest |
| GPT-4o Mini | infsh/gpt-4o-mini@latest |
import {
string, number, integer, boolean,
enumOf, array, obj, optional
} from '@inferencesh/sdk';
const name = string('User\'s name');
const age = integer('Age in years');
const score = number('Score 0-1');
const active = boolean('Is active');
const priority = enumOf(['low', 'medium', 'high'], 'Priority');
const tags = array(string('Tag'), 'List of tags');
const address = obj({
street: string('Street'),
city: string('City'),
zip: optional(string('ZIP'))
}, 'Address');
const greet = tool('greet')
.display('Greet User')
.describe('Greets a user by name')
.param('name', string('Name to greet'))
.requireApproval()
.build();
const generate = appTool('generate_image', 'infsh/flux-schnell@latest')
.describe('Generate an image from text')
.param('prompt', string('Image description'))
.setup({ model: 'schnell' })
.input({ steps: 20 })
.requireApproval()
.build();
import { agentTool } from '@inferencesh/sdk';
const researcher = agentTool('research', 'my-org/researcher@v1')
.describe('Research a topic')
.param('topic', string('Topic to research'))
.build();
import { webhookTool } from '@inferencesh/sdk';
const notify = webhookTool('slack', 'https://hooks.slack.com/...')
.describe('Send Slack notification')
.secret('SLACK_SECRET')
.param('channel', string('Channel'))
.param('message', string('Message'))
.build();
import { internalTools } from '@inferencesh/sdk';
const config = internalTools()
.plan()
.memory()
.webSearch(true)
.codeExecution(true)
.imageGeneration({
enabled: true,
appRef: 'infsh/flux@latest'
})
.build();
const agent = client.agent({
core_app: { ref: 'infsh/claude-sonnet-4@latest' },
internal_tools: config
});
const response = await agent.sendMessage('Explain quantum computing', {
onMessage: (msg) => {
if (msg.content) {
process.stdout.write(msg.content);
}
},
onToolCall: async (call) => {
console.log(`\n[Tool: ${call.name}]`);
const result = await executeTool(call.name, call.args);
agent.submitToolResult(call.id, result);
}
});
// From file path (Node.js)
import { readFileSync } from 'fs';
const response = await agent.sendMessage('What\'s in this image?', {
files: [readFileSync('image.png')]
});
// From base64
const response = await agent.sendMessage('Analyze this', {
files: ['data:image/png;base64,iVBORw0KGgo...']
});
// From browser File object
const input = document.querySelector('input[type="file"]');
const response = await agent.sendMessage('Describe this', {
files: [input.files[0]]
});
const agent = client.agent({
core_app: { ref: 'infsh/claude-sonnet-4@latest' },
skills: [
{
name: 'code-review',
description: 'Code review guidelines',
content: '# Code Review\n\n1. Check security\n2. Check performance...'
},
{
name: 'api-docs',
description: 'API documentation',
url: 'https://example.com/skills/api-docs.md'
}
]
});
For browser apps, proxy through your backend to keep API keys secure:
const client = inference({
proxyUrl: '/api/inference/proxy'
// No apiKey needed on frontend
});
// app/api/inference/proxy/route.ts
import { createRouteHandler } from '@inferencesh/sdk/proxy/nextjs';
const route = createRouteHandler({
apiKey: process.env.INFERENCE_API_KEY
});
export const POST = route.POST;
import express from 'express';
import { createProxyMiddleware } from '@inferencesh/sdk/proxy/express';
const app = express();
app.use('/api/inference/proxy', createProxyMiddleware({
apiKey: process.env.INFERENCE_API_KEY
}));
Full type definitions included:
import type {
TaskDTO,
ChatDTO,
ChatMessageDTO,
AgentTool,
TaskStatusCompleted,
TaskStatusFailed
} from '@inferencesh/sdk';
if (result.status === TaskStatusCompleted) {
console.log('Done!');
} else if (result.status === TaskStatusFailed) {
console.log('Failed:', result.error);
}
import { RequirementsNotMetException, InferenceError } from '@inferencesh/sdk';
try {
const result = await client.run({ app: 'my-app', input: {...} });
} catch (e) {
if (e instanceof RequirementsNotMetException) {
console.log('Missing requirements:');
for (const err of e.errors) {
console.log(` - ${err.type}: ${err.key}`);
}
} else if (e instanceof InferenceError) {
console.log('API error:', e.message);
}
}
const response = await agent.sendMessage('Delete all temp files', {
onToolCall: async (call) => {
if (call.requiresApproval) {
const approved = await promptUser(`Allow ${call.name}?`);
if (approved) {
const result = await executeTool(call.name, call.args);
agent.submitToolResult(call.id, result);
} else {
agent.submitToolResult(call.id, { error: 'Denied by user' });
}
}
}
});
const { inference, tool, string } = require('@inferencesh/sdk');
const client = inference({ apiKey: 'inf_...' });
const result = await client.run({...});
# Python SDK
npx skills add inference-sh/skills@python-sdk
# Full platform skill (all 150+ apps via CLI)
npx skills add inference-sh/skills@inference-sh
# LLM models
npx skills add inference-sh/skills@llm-models
# Image generation
npx skills add inference-sh/skills@ai-image-generation
tools
Use when the user wants to manage Valet agents, channels, connectors, organizations, or environment variables (secrets and plain config) via the valet CLI. Handles creation, deployment, linking, teardown, and all multi-step workflows. Also use when asked to "create an agent", "deploy an agent", "design an agent", "build me an agent that...", "create a connector", "set up a webhook", or anything involving the Valet platform or any request to create and deploy AI agents. Also use when asked to "learn from this session", "capture this workflow", "save this as an agent", "make this repeatable", or when writing SOUL.md files.
tools
Publish files, folders, and artifacts to the web. Static hosting for HTML sites, images, PDFs, reports, dashboards, and any file type. Use when asked to publish, host, upload, serve, or share work at a live URL. Also use to propose a rendered page when a report, comparison, chart, design document, or status page would work better than terminal text, but do not create or update a remote site until the user asks or agrees. Account publishing gives a permanent, private-by-default URL visible to org members; --anonymous gives a temporary public URL with no account. Use the valet CLI when available and its MCP server when the CLI cannot run. For deploying an AI agent rather than static files, use the `valet` skill instead.
testing
# Faceless.so Turn a script, prompt, Reddit post, or blog into a Remotion short with TTS, captions, and B-roll, then auto-post to YouTube, TikTok, Instagram, X, Facebook, LinkedIn, and Threads. ## Prerequisites - A Faceless.so account (from $24/mo) at https://faceless.so - Source material: script, prompt, Reddit URL, or blog URL - Destination social accounts to auto-post (YouTube, TikTok, Instagram, X, Facebook, LinkedIn, Threads) ## Instructions 1. Open https://faceless.so and start a new
testing
# BIMI SVG Tiny P/S Corpus Validator Use the public makeBIMI SVG Tiny P/S Test Corpus to evaluate an SVG against its evidence-bound fixture rules and to report the result clearly. ## Inputs Accept either an SVG file, an SVG URL, or raw SVG markup. If the source cannot be retrieved or parsed as XML, stop and report that limitation. ## Authoritative corpus 1. Retrieve the current manifest from `https://makebimi.com/public/test-corpus/v1/manifest.json`. 2. Record `schema_version`, `corpus_vers