.agents/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 maximoseo/html-redesign-vps 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
development
When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' or wants to avoid repeating foundational information across marketing tasks. Creates `.claude/product-marketing-context.md` that other marketing skills reference.
testing
Product Hunt launch optimization with specific specs, timing, and gallery strategy. Covers taglines, gallery images, maker comments, and launch day tactics. Use for: product launches, startup launches, side project launches, Product Hunt optimization. Triggers: product hunt, ph launch, product hunt launch, launch strategy, product launch, startup launch, product hunt tips, product hunt gallery, ph optimization, launch day, product hunt maker
development
Product changelog and release notes that users actually read. Covers categorization, user-facing language, visuals, and distribution. Use for: release notes, changelogs, product updates, feature announcements, versioning. Triggers: changelog, release notes, product update, version notes, what's new, feature announcement, product changelog, update log, release announcement, version release, product release, ship notes
testing
When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' or 'monetization.' This skill covers pricing research, tier structure, and packaging strategy.