bouncer-feed-filter
AI-powered browser extension that filters unwanted posts from Twitter/X feeds using natural language rules and multiple AI backends
Best use case
bouncer-feed-filter is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
AI-powered browser extension that filters unwanted posts from Twitter/X feeds using natural language rules and multiple AI backends
Teams using bouncer-feed-filter should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bouncer-feed-filter/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bouncer-feed-filter Compares
| Feature / Agent | bouncer-feed-filter | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
AI-powered browser extension that filters unwanted posts from Twitter/X feeds using natural language rules and multiple AI backends
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Bouncer Feed Filter
> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.
Bouncer is a browser extension (Chrome/Edge/iOS) that uses AI to filter unwanted posts from Twitter/X feeds in real time. Users define filters in plain language ("crypto", "engagement bait", "rage politics"), and Bouncer classifies and hides matching posts using AI models — local (WebGPU via WebLLM) or cloud (OpenAI, Gemini, Anthropic, OpenRouter, Imbue).
## Repository Structure
```
Bouncer/ # Main extension source
src/
background/ # Service worker / background scripts
content/ # Content scripts (Twitter DOM interaction)
popup/ # Extension popup UI
adapters/ # Site adapters (Twitter/X)
models/ # AI backend integrations
utils/ # Shared utilities
icons/ # Extension icons
manifest.json # Chrome extension manifest
package.json
tsconfig.json
```
## Installation & Build
### From Source (Chrome/Edge)
```bash
git clone https://github.com/imbue-ai/bouncer.git
cd bouncer/Bouncer
npm install
npm run build
```
Load in Chrome:
1. Go to `chrome://extensions`
2. Enable **Developer mode**
3. Click **Load unpacked** → select `Bouncer/` folder
4. Navigate to `twitter.com` or `x.com`
### Development Build (watch mode)
```bash
cd Bouncer
npm run dev # watch mode with hot rebuild
```
### Production Build
```bash
npm run build # outputs to Bouncer/dist or inline
```
## AI Backend Configuration
Bouncer supports multiple providers. Configure via the extension popup Settings panel.
### Provider / Model Matrix
| Provider | Model IDs | Auth |
|----------|-----------|------|
| Local WebGPU | `Qwen3-4B`, `Qwen3.5-4B`, `Qwen3.5-4B Vision` | None |
| OpenAI | `GPT-5 Nano`, `gpt-oss-20b` | API key |
| Google Gemini | `2.5 Flash Lite`, `2.5 Flash`, `3 Flash Preview` | API key |
| Anthropic | `Claude Haiku 4.5` | API key |
| OpenRouter | `Nemotron Nano 12B VL`, `Ministral 3B` | Account token |
| Imbue | Default | None (built-in) |
API keys are stored in Chrome's `chrome.storage.local` — never hardcoded.
## Core Architecture
### 1. MutationObserver — Content Script
The content script watches the Twitter feed for new posts:
```typescript
// src/content/feedObserver.ts
const observer = new MutationObserver((mutations) => {
for (const mutation of mutations) {
for (const node of mutation.addedNodes) {
if (node instanceof HTMLElement) {
const post = extractPost(node);
if (post) classifyAndFilter(post);
}
}
}
});
observer.observe(document.body, { childList: true, subtree: true });
```
### 2. Post Extraction — Twitter Adapter
```typescript
// src/adapters/twitter.ts
export interface ExtractedPost {
id: string;
text: string;
authorHandle: string;
imageUrls: string[];
element: HTMLElement;
}
export function extractPost(element: HTMLElement): ExtractedPost | null {
const article = element.querySelector('article[data-testid="tweet"]');
if (!article) return null;
const tweetText = article.querySelector('[data-testid="tweetText"]')?.textContent ?? '';
const handle = article.querySelector('[data-testid="User-Name"] a')?.getAttribute('href') ?? '';
const images = [...article.querySelectorAll('img[src*="pbs.twimg.com/media"]')]
.map(img => (img as HTMLImageElement).src);
return {
id: article.closest('[data-testid]')?.getAttribute('data-testid') ?? crypto.randomUUID(),
text: tweetText,
authorHandle: handle.replace('/', ''),
imageUrls: images,
element: article as HTMLElement,
};
}
```
### 3. Classification Request
```typescript
// src/models/classify.ts
export interface ClassificationResult {
filtered: boolean;
matchedCategory: string | null;
reasoning: string;
}
export async function classifyPost(
post: ExtractedPost,
filters: string[],
model: ModelConfig
): Promise<ClassificationResult> {
const prompt = buildClassificationPrompt(post.text, filters, post.imageUrls);
const response = await model.provider.complete(prompt);
return parseClassificationResponse(response);
}
function buildClassificationPrompt(
text: string,
filters: string[],
imageUrls: string[]
): string {
return `You are a content filter. Given a social media post, determine if it matches any of the user's filter categories.
Filter categories: ${filters.map(f => `"${f}"`).join(', ')}
Post text:
${text}
${imageUrls.length > 0 ? `The post contains ${imageUrls.length} image(s).` : ''}
Respond with JSON:
{
"filtered": boolean,
"matchedCategory": "category name or null",
"reasoning": "brief explanation"
}`;
}
```
### 4. Hiding Filtered Posts
```typescript
// src/content/filterUI.ts
export function hidePost(element: HTMLElement, reason: string): void {
element.style.transition = 'opacity 0.3s ease-out';
element.style.opacity = '0';
setTimeout(() => {
element.style.display = 'none';
element.dataset.bouncerFiltered = 'true';
element.dataset.bouncerReason = reason;
}, 300);
}
export function showFilteredIndicator(count: number): void {
const indicator = document.getElementById('bouncer-filtered-count');
if (indicator) indicator.textContent = `${count} filtered`;
}
```
## Adding a New AI Provider
```typescript
// src/models/providers/myProvider.ts
import type { ModelProvider, CompletionRequest, CompletionResponse } from '../types';
export class MyProvider implements ModelProvider {
private apiKey: string;
private endpoint = 'https://api.myprovider.com/v1/chat/completions';
constructor(apiKey: string) {
this.apiKey = apiKey;
}
async complete(request: CompletionRequest): Promise<CompletionResponse> {
const response = await fetch(this.endpoint, {
method: 'POST',
headers: {
'Authorization': `Bearer ${this.apiKey}`,
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: request.model,
messages: [{ role: 'user', content: request.prompt }],
max_tokens: 256,
}),
});
const data = await response.json();
return {
text: data.choices[0].message.content,
usage: data.usage,
};
}
}
```
Register it in the provider registry:
```typescript
// src/models/registry.ts
import { MyProvider } from './providers/myProvider';
export function createProvider(config: StoredConfig): ModelProvider {
switch (config.provider) {
case 'my-provider':
return new MyProvider(config.apiKey);
// ... other cases
}
}
```
## Result Caching
Bouncer caches classification results so repeated posts don't trigger new inference calls:
```typescript
// src/utils/cache.ts
const CACHE_KEY = 'bouncer-post-cache';
export async function getCachedResult(postId: string): Promise<ClassificationResult | null> {
const stored = await chrome.storage.local.get(CACHE_KEY);
const cache = stored[CACHE_KEY] ?? {};
return cache[postId] ?? null;
}
export async function cacheResult(postId: string, result: ClassificationResult): Promise<void> {
const stored = await chrome.storage.local.get(CACHE_KEY);
const cache = stored[CACHE_KEY] ?? {};
cache[postId] = result;
// Limit cache size
const keys = Object.keys(cache);
if (keys.length > 1000) delete cache[keys[0]];
await chrome.storage.local.set({ [CACHE_KEY]: cache });
}
```
## Filter Management
Filters are stored and retrieved via `chrome.storage.sync`:
```typescript
// src/utils/filters.ts
export async function getFilters(): Promise<string[]> {
const result = await chrome.storage.sync.get('bouncerFilters');
return result.bouncerFilters ?? [];
}
export async function addFilter(topic: string): Promise<void> {
const filters = await getFilters();
if (!filters.includes(topic)) {
await chrome.storage.sync.set({ bouncerFilters: [...filters, topic] });
}
}
export async function removeFilter(topic: string): Promise<void> {
const filters = await getFilters();
await chrome.storage.sync.set({
bouncerFilters: filters.filter(f => f !== topic),
});
}
```
## Local WebGPU Models (WebLLM)
Local models run entirely in-browser via WebGPU — zero data sent externally:
```typescript
// src/models/providers/webllm.ts
import { CreateMLCEngine, type MLCEngine } from '@mlc-ai/web-llm';
let engine: MLCEngine | null = null;
export async function loadLocalModel(modelId: string, onProgress?: (p: number) => void): Promise<void> {
engine = await CreateMLCEngine(modelId, {
initProgressCallback: (report) => onProgress?.(report.progress),
});
}
export async function localComplete(prompt: string): Promise<string> {
if (!engine) throw new Error('Local model not loaded');
const response = await engine.chat.completions.create({
messages: [{ role: 'user', content: prompt }],
max_tokens: 256,
});
return response.choices[0].message.content ?? '';
}
```
## Chrome Extension Manifest Key Points
```json
{
"manifest_version": 3,
"permissions": ["storage", "activeTab", "scripting"],
"host_permissions": ["https://twitter.com/*", "https://x.com/*"],
"background": { "service_worker": "background.js" },
"content_scripts": [{
"matches": ["https://twitter.com/*", "https://x.com/*"],
"js": ["content.js"],
"run_at": "document_idle"
}]
}
```
## Troubleshooting
| Problem | Fix |
|---------|-----|
| Extension not loading | Ensure `npm run build` completed without errors; reload unpacked extension |
| Posts not being filtered | Check that filters are saved in popup; open DevTools on x.com and check console for errors |
| API key errors | Verify key is stored via Settings panel, not hardcoded; check provider dashboard for quota |
| Local model not loading | Browser must support WebGPU (`chrome://flags/#enable-unsafe-webgpu`); first load downloads model (~2-4GB) |
| Filtered count not updating | MutationObserver may have detached; reload the page |
| TypeScript errors on build | Run `npm install` to ensure all types are present; check `tsconfig.json` target is `ES2020+` |
## Common Patterns
**Check if a post should be processed (before API call):**
```typescript
async function classifyAndFilter(post: ExtractedPost): Promise<void> {
// Skip if already processed
if (post.element.dataset.bouncerProcessed) return;
post.element.dataset.bouncerProcessed = 'true';
// Check cache first
const cached = await getCachedResult(post.id);
if (cached) {
if (cached.filtered) hidePost(post.element, cached.reasoning);
return;
}
const filters = await getFilters();
if (filters.length === 0) return;
const config = await getModelConfig();
const result = await classifyPost(post, filters, config);
await cacheResult(post.id, result);
if (result.filtered) hidePost(post.element, result.reasoning);
}
```
**Storing API key securely (popup UI):**
```typescript
// Never log or expose the key — store only via chrome.storage.local
async function saveApiKey(provider: string, key: string): Promise<void> {
await chrome.storage.local.set({ [`${provider}_api_key`]: key });
}
async function getApiKey(provider: string): Promise<string> {
const result = await chrome.storage.local.get(`${provider}_api_key`);
return result[`${provider}_api_key`] ?? '';
}
```Related Skills
openai-privacy-filter
OpenAI Privacy Filter — bidirectional token-classification model for PII detection and masking in text
```markdown
---
zeroboot-vm-sandbox
Sub-millisecond VM sandboxes for AI agents using copy-on-write KVM forking via Zeroboot
yourvpndead-vpn-detection
Android app that detects VPN/proxy servers (VLESS/xray/sing-box) via local SOCKS5 vulnerability, exposing exit IPs and server configs without root
xata-postgres-platform
Expert skill for Xata open-source cloud-native Postgres platform with copy-on-write branching, scale-to-zero, and Kubernetes deployment
x-mentor-skill-nuwa
AI-powered X (Twitter) content strategy skill that distills methodologies from 6 top creators + open-source algorithm data into actionable writing, growth, and monetization guidance.
wx-favorites-report
End-to-end pipeline to extract, decrypt, and visualize WeChat Mac favorites from encrypted SQLite DB into an interactive HTML report.
wterm-web-terminal
Web terminal emulator with Zig/WASM core, DOM rendering, and React/vanilla JS bindings
worldmonitor-intelligence-dashboard
Real-time global intelligence dashboard with AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking
witr-process-inspector
CLI and TUI tool that explains why processes, services, and ports are running by tracing causality chains across supervisors, containers, and shells.
wildworld-dataset
WildWorld large-scale action-conditioned world modeling dataset with 108M+ frames from a photorealistic ARPG game, featuring per-frame annotations, 450+ actions, and explicit state information for generative world modeling research.
whatcable-macos-usb-inspector
macOS menu bar app that identifies USB-C cable capabilities and charging diagnostics using IOKit