open-index/open-npm huggingface dataset:
npm package: @stan_bk/img-preload
Description: An scheme for imgs loading scheme while page rendering.
Author: Kkk
Latest version: 1.0.12

# Img Preload
A scheme for imgs loading while page is rendering.

Here is the sample: https://stan-bk.github.io/img-preload/
# U can use it to
1. handle all images loading progress which are in the first page
2. use lazyload to speed up imgs-loading and page-rendering
3. custom styles of shade when its waiting for images loading

# U can use it by

```bash
pnpm i @stan_bk/img-preload
```

```js
import ImgPreload from '@stan_bk/img-preload'

new ImgPreload({
  imaegs: [] // need an array of images that are expected to handle loading, default: document.images
  isLazy: true, // if enable lazyload
  lazySrcAttr: 'data-src', // src attribute name to access
  customShade: document.createElement('div'), // custom shade, expect a document element or function
  customColor: ['green', 'yellow 80%'], // custom color for rendering default shade(which style with linear-gradient)
  onLoad(img) {
    // handle image just loaded
  },
  onError(img) {
    // handle image just loaded with error
  },
  onFinish(loadedImages, failedImages) {
    // handle all images loaded or failed
  }
})
```
# Sth you should know
1. Img Preload only run in browser cause it need to access document
2. When lazyload option is enabled, 
- you are expect to pass img source path to attribute which named by the value of `lazySrcAttr` instead `src`
- the source path will reload with `lazySrcAttr` if it has both `lazySrcAttr` and `src` attribute
- the finish event will trigger if the images in view had been loaded
3. When customShade is provided, it will instead
of default shade, you can access the shade container and the controllers of it through customShade function
npm package: @stanchat/clawguard
Description: Real-time monitoring for AI agents - detect threats and see everything your agent does
Author: Averecion
Latest version: 2.7.0

# 🦞 Clawguard - Real-Time AI Agent Monitoring

Clawguard monitors your AI agent's activity in real-time. See everything your agent does, detect dangerous commands and prompt injection attacks.

## What Clawguard Does

**✓ Monitors:**
- Shell commands (rm, chmod, curl piped to bash, etc.)
- File writes and deletions
- Network requests
- Credential/environment access
- Prompt injection attempts

**✗ Clawguard does NOT:**
- Send data to external servers
- Modify your agent's core behavior
- Store data outside your machine

## Quick Start

Just run:
```bash
npx @stanchat/clawguard start
```

Your dashboard opens at `http://localhost:4321/clawguard`

Works on Windows, Mac, and Linux - no install needed!

### Optional: Install Globally

For frequent use, install globally:
```bash
npm install -g @stanchat/clawguard   # add sudo on Mac/Linux if needed
clawguard start
```

## How It Works

### Show Mode (First Run)
When first installed, Clawguard runs in "Show Mode" - it logs what it WOULD block but doesn't actually block anything. This lets you see how it works without disruption.

After 10 actions, you'll be prompted to enable full protection.

### Protection Levels

| Level | Description |
|-------|-------------|
| 😊 **Relaxed** | Only block the really dangerous stuff |
| ⚖️ **Balanced** | Ask before risky actions (recommended) |
| 🔒 **Strict** | Approve everything important |

## Commands

```bash
# Check status
openclaw clawguard status

# Open dashboard
openclaw clawguard dashboard

# Enable/disable protection
openclaw clawguard enable
openclaw clawguard disable

# Change protection level
openclaw clawguard level strict

# View recent activity
openclaw clawguard logs
```

## Dashboard

The Clawguard dashboard shows:
- Real-time action monitoring
- Blocked action history
- Approval requests
- Protection score

Access at: `http://127.0.0.1:4321/clawguard`

## Configuration

Config file: `~/.clawguard/config.json`
Logs: `~/.clawguard/logs/`
Custom policy: `~/.clawguard/policy.json`

## Transparency

Clawguard is:
- ✅ Open source
- ✅ Local-only (no cloud)
- ✅ No data collection
- ✅ Can be disabled anytime

## Support

- Issues: [github.com/averecion/clawguard](https://github.com/averecion/clawguard)
- Docs: [clawguard.dev](https://clawguard.dev)
- Full governance platform: [averecion.com](https://averecion.com)

---

Made with 🦞 by [Averecion](https://averecion.com)
npm package: @stancsz/typellm
Description: Strictly typed, agent-first LLM bridge for TypeScript.
Author: Antigravity
Latest version: 0.1.1
Deprecated: Package no longer supported. Contact Support at https://www.npmjs.com/support for more info.

# ⚡ TypeLLM

Strictly typed, agent-first LLM bridge for TypeScript. Designed to bridge the gap between "Conversational AI" and "Autonomous Agents."

## Features

- **🛡️ Hallucination Bridge**: Automatically maps model hallucinations like `command`, `action`, or `parameters` into standard `tool` and `args` keys.
- **🛠️ Auto-Repair**: Integrated `jsonrepair` middleware to extract and fix broken JSON blocks from conversational rambling.
- **🤖 Provider Agnostic**: One interface for OpenAI, Google, and more.
- **🏃 Client-Side First**: Lightweight ESM build perfect for CLI tools and Edge functions.

## Installation

```bash
npm install @stancsz/typellm
```

## Quick Start

```typescript
import { createTypeLLM } from '@stancsz/typellm';

const agent = createTypeLLM({
  provider: 'openai',
  model: 'gpt-4o-mini',
  apiKey: process.env.OPENAI_API_KEY
});

const response = await agent.generate(
  "You are a file organizer.",
  [{ role: 'user', content: 'Organize this folder' }]
);

console.log(response.tool); // Always "tool", never "command"
console.log(response.args); // Normalized parameters
```

## Why TypeLLM?

Most LLM SDKs are built for chat. Agents need **structure**. 

TypeLLM assumes your model is "lazy" and "talkative." It aggressively strips conversational filler and enforces a worker mindset by translating fuzzy natural language intentions into rigid JSON execution blocks.