anything-analyzer-cdp
Electron desktop app that captures web traffic via Chrome DevTools Protocol and uses AI to generate protocol analysis reports
Best use case
anything-analyzer-cdp is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Electron desktop app that captures web traffic via Chrome DevTools Protocol and uses AI to generate protocol analysis reports
Teams using anything-analyzer-cdp 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/anything-analyzer-cdp/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How anything-analyzer-cdp Compares
| Feature / Agent | anything-analyzer-cdp | 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?
Electron desktop app that captures web traffic via Chrome DevTools Protocol and uses AI to generate protocol analysis reports
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
# Anything Analyzer CDP Skill
> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.
Anything Analyzer is an Electron desktop application that embeds a browser, captures all network traffic via Chrome DevTools Protocol (CDP), injects JS hooks, snapshots storage, and feeds the data to an AI (OpenAI/Anthropic/custom) to generate protocol analysis reports — useful for documenting registration flows, 2API reverse engineering, and general browser protocol analysis.
## Installation & Setup
```bash
git clone https://github.com/MouseWW/anything-analyzer.git
cd anything-analyzer
pnpm install
pnpm dev # development mode
pnpm build # production build
```
**Windows native module build requirement:**
```bash
# Install Visual Studio Build Tools first, then:
pnpm install
# If better-sqlite3 fails:
pnpm rebuild
```
**Package as installer:**
```bash
pnpm run build && npx electron-builder --win
```
## Core Architecture
```
src/
├── main/ # Electron main process
│ ├── ai/ # AI analysis pipeline
│ │ ├── ai-analyzer.ts # orchestrator
│ │ ├── data-assembler.ts# data preparation
│ │ ├── prompt-builder.ts# prompt generation
│ │ └── scene-detector.ts# rule-based scene classification
│ ├── capture/ # Capture engine
│ │ ├── capture-engine.ts# data sink → SQLite + renderer
│ │ ├── js-injector.ts # hook script injection
│ │ └── storage-collector.ts # periodic storage snapshots
│ ├── cdp/
│ │ └── cdp-manager.ts # CDP manager
│ ├── db/ # SQLite via better-sqlite3
│ ├── session/
│ │ └── session-manager.ts # session lifecycle
│ ├── tab-manager.ts # Multi-tab WebContentsView
│ ├── window.ts # Main window layout
│ └── ipc.ts # IPC handlers
├── preload/ # Context bridge + hook script
├── renderer/ # React 19 + Ant Design 5 UI
└── shared/types.ts # Shared TypeScript types
```
## Key Concepts
### Sessions
A **Session** scopes all captured data. Each session has a name, target URL, and contains all requests, JS hook events, and storage snapshots captured during that session.
### Capture Engine
The capture engine:
1. Attaches CDP to `WebContentsView` tabs
2. Enables `Fetch.enable` for request interception
3. Injects JS hooks via `Page.addScriptToEvaluateOnNewDocument`
4. Collects storage snapshots periodically
### AI Analysis Pipeline
1. **Scene detection** — rule-based classification (registration, OAuth, API auth, etc.)
2. **Data assembly** — selects relevant requests, deduplicates, truncates large bodies
3. **Prompt building** — constructs structured prompt with scene context
4. **LLM call** — streams response back to renderer
## Configuration
### LLM Provider Setup (Settings UI)
Configure via the Settings panel (bottom-left gear icon):
```typescript
// Config shape (stored in SQLite settings table)
interface LLMConfig {
provider: 'openai' | 'anthropic' | 'custom';
apiKey: string; // from env or user input
model: string; // e.g. 'gpt-4o', 'claude-sonnet-4-20250514'
baseUrl?: string; // for custom OpenAI-compatible endpoints
}
```
**OpenAI:**
- API Key: `$OPENAI_API_KEY`
- Model: `gpt-4o` or `gpt-4o-mini`
**Anthropic:**
- API Key: `$ANTHROPIC_API_KEY`
- Model: `claude-sonnet-4-20250514`
**Custom (OpenAI-compatible):**
- Base URL: e.g. `https://api.deepseek.com/v1`
- API Key: your provider key
- Model: provider-specific model name
## IPC API (Main ↔ Renderer)
### Session Management
```typescript
// Create a session
const session = await window.electron.ipcRenderer.invoke('session:create', {
name: 'My Analysis Session',
url: 'https://example.com'
})
// List sessions
const sessions = await window.electron.ipcRenderer.invoke('session:list')
// Delete session
await window.electron.ipcRenderer.invoke('session:delete', sessionId)
```
### Capture Control
```typescript
// Start capturing for current tab
await window.electron.ipcRenderer.invoke('capture:start', { sessionId, tabId })
// Stop capturing
await window.electron.ipcRenderer.invoke('capture:stop', { sessionId, tabId })
// Get captured requests
const requests = await window.electron.ipcRenderer.invoke('capture:getRequests', sessionId)
```
### AI Analysis
```typescript
// Trigger AI analysis (streams back via IPC events)
await window.electron.ipcRenderer.invoke('analyze:start', { sessionId })
// Listen for streaming chunks
window.electron.ipcRenderer.on('analyze:chunk', (_, chunk: string) => {
setReport(prev => prev + chunk)
})
// Listen for completion
window.electron.ipcRenderer.on('analyze:done', () => {
setAnalyzing(false)
})
```
## Real Code Examples
### Extend the Scene Detector
```typescript
// src/main/ai/scene-detector.ts
import { CapturedRequest } from '../../shared/types'
export type Scene =
| 'registration'
| 'oauth'
| 'api-auth'
| 'websocket'
| 'general'
export function detectScene(requests: CapturedRequest[]): Scene {
const urls = requests.map(r => r.url.toLowerCase())
const bodies = requests.map(r => r.requestBody?.toLowerCase() ?? '')
// OAuth detection
if (urls.some(u => u.includes('oauth') || u.includes('authorize') || u.includes('callback'))) {
return 'oauth'
}
// Registration detection
if (
bodies.some(b => b.includes('password') && (b.includes('email') || b.includes('username'))) &&
urls.some(u => u.includes('register') || u.includes('signup') || u.includes('sign-up'))
) {
return 'registration'
}
// WebSocket upgrade detection
if (requests.some(r => r.isWebSocket)) {
return 'websocket'
}
// Auth token patterns
if (urls.some(u => u.includes('/auth') || u.includes('/token') || u.includes('/login'))) {
return 'api-auth'
}
return 'general'
}
```
### Custom Prompt Builder
```typescript
// src/main/ai/prompt-builder.ts
import { Scene } from './scene-detector'
import { AssembledData } from './data-assembler'
export function buildPrompt(scene: Scene, data: AssembledData): string {
const sceneInstructions: Record<Scene, string> = {
registration: `Analyze this registration flow. Extract:
1. Required fields and validation rules
2. Password requirements
3. Captcha/bot protection mechanisms
4. Email verification flow
5. Reproducible curl commands for each step`,
oauth: `Analyze this OAuth flow. Extract:
1. OAuth provider and grant type
2. Authorization URL with all parameters
3. Token exchange endpoint and parameters
4. Token refresh mechanism
5. Scopes requested`,
'api-auth': `Analyze this authentication protocol. Extract:
1. Auth endpoint and method
2. Request payload schema
3. Response token format (JWT/session/etc)
4. Token usage in subsequent requests (header name, format)
5. Expiry and refresh strategy`,
websocket: `Analyze this WebSocket protocol. Extract:
1. Upgrade request headers
2. Initial handshake messages
3. Message format (JSON/binary/custom)
4. Heartbeat/ping-pong mechanism
5. Event types and schemas`,
general: `Analyze this web protocol. Extract:
1. Core API endpoints and their purposes
2. Authentication mechanism
3. Request/response schemas
4. Error handling patterns
5. Rate limiting signals`,
}
return `You are a protocol reverse engineer. ${sceneInstructions[scene]}
## Captured Data
### Network Requests (${data.requests.length} total)
${data.requests.map(r => `
**${r.method} ${r.url}**
Status: ${r.statusCode}
Request Headers: ${JSON.stringify(r.requestHeaders, null, 2)}
Request Body: ${r.requestBody ?? '(empty)'}
Response Headers: ${JSON.stringify(r.responseHeaders, null, 2)}
Response Body: ${r.responseBody ?? '(empty)'}
`).join('\n---\n')}
### JS Hook Events
${JSON.stringify(data.hookEvents, null, 2)}
### Storage Snapshots
${JSON.stringify(data.storageSnapshots, null, 2)}
Generate a comprehensive protocol analysis report in Markdown.`
}
```
### Adding a Custom JS Hook
```typescript
// src/main/capture/js-injector.ts
export function buildHookScript(): string {
return `
(function() {
// Hook fetch
const _fetch = window.fetch.bind(window)
window.fetch = async function(...args) {
const [input, init] = args
const url = input instanceof Request ? input.url : String(input)
// Pre-request hook
window.__cdpHook?.({ type: 'fetch:request', url, init: JSON.stringify(init) })
const response = await _fetch(...args)
const clone = response.clone()
// Post-response hook (non-blocking)
clone.text().then(body => {
window.__cdpHook?.({ type: 'fetch:response', url, status: response.status, body })
}).catch(() => {})
return response
}
// Hook XHR
const _open = XMLHttpRequest.prototype.open
const _send = XMLHttpRequest.prototype.send
XMLHttpRequest.prototype.open = function(method, url, ...rest) {
this.__hookData = { method, url }
return _open.apply(this, [method, url, ...rest])
}
XMLHttpRequest.prototype.send = function(body) {
this.addEventListener('load', function() {
window.__cdpHook?.({
type: 'xhr:complete',
method: this.__hookData?.method,
url: this.__hookData?.url,
requestBody: body,
status: this.status,
responseBody: this.responseText
})
})
return _send.apply(this, [body])
}
// Hook crypto.subtle for key detection
if (window.crypto?.subtle) {
const _sign = crypto.subtle.sign.bind(crypto.subtle)
crypto.subtle.sign = async function(algorithm, key, data) {
window.__cdpHook?.({ type: 'crypto:sign', algorithm: JSON.stringify(algorithm) })
return _sign(algorithm, key, data)
}
}
// Hook document.cookie
const cookieDesc = Object.getOwnPropertyDescriptor(Document.prototype, 'cookie')
Object.defineProperty(document, 'cookie', {
get: function() { return cookieDesc.get.call(this) },
set: function(val) {
window.__cdpHook?.({ type: 'cookie:set', value: val })
return cookieDesc.set.call(this, val)
}
})
})()
`
}
```
### Database Schema Access
```typescript
// src/main/db/ — SQLite via better-sqlite3
import Database from 'better-sqlite3'
import path from 'path'
import { app } from 'electron'
const DB_PATH = path.join(app.getPath('userData'), 'analyzer.db')
export function getDb(): Database.Database {
const db = new Database(DB_PATH)
db.pragma('journal_mode = WAL')
return db
}
// Typical schema
export function initSchema(db: Database.Database) {
db.exec(`
CREATE TABLE IF NOT EXISTS sessions (
id TEXT PRIMARY KEY,
name TEXT NOT NULL,
url TEXT NOT NULL,
created_at INTEGER NOT NULL
);
CREATE TABLE IF NOT EXISTS requests (
id TEXT PRIMARY KEY,
session_id TEXT NOT NULL,
url TEXT NOT NULL,
method TEXT NOT NULL,
status_code INTEGER,
request_headers TEXT,
request_body TEXT,
response_headers TEXT,
response_body TEXT,
is_sse INTEGER DEFAULT 0,
is_websocket INTEGER DEFAULT 0,
timestamp INTEGER NOT NULL,
FOREIGN KEY (session_id) REFERENCES sessions(id)
);
CREATE TABLE IF NOT EXISTS hook_events (
id TEXT PRIMARY KEY,
session_id TEXT NOT NULL,
type TEXT NOT NULL,
data TEXT NOT NULL,
timestamp INTEGER NOT NULL
);
CREATE TABLE IF NOT EXISTS storage_snapshots (
id TEXT PRIMARY KEY,
session_id TEXT NOT NULL,
cookies TEXT,
local_storage TEXT,
session_storage TEXT,
timestamp INTEGER NOT NULL
);
CREATE TABLE IF NOT EXISTS settings (
key TEXT PRIMARY KEY,
value TEXT NOT NULL
);
`)
}
```
### Shared Types Reference
```typescript
// src/shared/types.ts
export interface Session {
id: string
name: string
url: string
createdAt: number
}
export interface CapturedRequest {
id: string
sessionId: string
url: string
method: string
statusCode?: number
requestHeaders?: Record<string, string>
requestBody?: string
responseHeaders?: Record<string, string>
responseBody?: string
isSSE: boolean
isWebSocket: boolean
timestamp: number
}
export interface HookEvent {
id: string
sessionId: string
type: 'fetch:request' | 'fetch:response' | 'xhr:complete' | 'crypto:sign' | 'cookie:set'
data: Record<string, unknown>
timestamp: number
}
export interface StorageSnapshot {
id: string
sessionId: string
cookies: string
localStorage: Record<string, string>
sessionStorage: Record<string, string>
timestamp: number
}
export interface LLMConfig {
provider: 'openai' | 'anthropic' | 'custom'
apiKey: string
model: string
baseUrl?: string
}
```
## Common Patterns
### Pattern: Capture a Full Registration Flow
1. Click **New Session** → enter name + target URL (e.g. `https://example.com/register`)
2. Click **Start Capture**
3. In the embedded browser, complete the full registration flow
4. Click **Stop Capture**
5. Click **Analyze** → AI generates a report with extracted fields, validation rules, and curl commands
### Pattern: OAuth Flow Analysis
1. Create session with the OAuth entry URL
2. Start capture
3. Authorize the OAuth flow including the redirect callback
4. Stop capture — the analyzer auto-detects OAuth and focuses prompt on token exchange
### Pattern: Adding a New LLM Provider
```typescript
// src/main/ai/ai-analyzer.ts
import Anthropic from '@anthropic-ai/sdk'
import OpenAI from 'openai'
export async function* callLLM(
config: LLMConfig,
prompt: string
): AsyncGenerator<string> {
if (config.provider === 'anthropic') {
const client = new Anthropic({ apiKey: config.apiKey })
const stream = await client.messages.stream({
model: config.model,
max_tokens: 8192,
messages: [{ role: 'user', content: prompt }]
})
for await (const chunk of stream) {
if (chunk.type === 'content_block_delta' && chunk.delta.type === 'text_delta') {
yield chunk.delta.text
}
}
} else {
// OpenAI or custom compatible
const client = new OpenAI({
apiKey: config.apiKey,
baseURL: config.baseUrl // undefined = default OpenAI
})
const stream = await client.chat.completions.create({
model: config.model,
messages: [{ role: 'user', content: prompt }],
stream: true
})
for await (const chunk of stream) {
yield chunk.choices[0]?.delta?.content ?? ''
}
}
}
```
### Pattern: Filter Requests Before Analysis
```typescript
// Useful for large sessions — filter to only auth-related requests
function filterRelevantRequests(requests: CapturedRequest[]): CapturedRequest[] {
const AUTH_PATTERNS = [
/\/auth/, /\/login/, /\/register/, /\/signup/, /\/token/,
/\/oauth/, /\/session/, /\/verify/, /\/captcha/
]
return requests.filter(r => {
// Always include if has auth header
if (r.requestHeaders?.['authorization'] || r.requestHeaders?.['x-auth-token']) {
return true
}
// Include if URL matches auth patterns
if (AUTH_PATTERNS.some(p => p.test(r.url))) return true
// Include if response sets cookies
if (r.responseHeaders?.['set-cookie']) return true
// Exclude static assets
if (/\.(js|css|png|jpg|gif|svg|woff|ico)(\?|$)/.test(r.url)) return false
return false
})
}
```
## Troubleshooting
### `better-sqlite3` build fails on Windows
```bash
npm install --global windows-build-tools
# or install Visual Studio Build Tools 2022 manually
pnpm rebuild
```
### `better-sqlite3` wrong Electron version
```bash
# Rebuild for current Electron version
./node_modules/.bin/electron-rebuild -f -w better-sqlite3
# or
npx @electron/rebuild -f -w better-sqlite3
```
### CDP not attaching to tab
- Ensure `WebContentsView` is fully loaded before calling `cdpManager.attach()`
- Check `webContents.getURL()` isn't `about:blank` before enabling Fetch
- For popups/OAuth windows, listen for `new-window` or `setWindowOpenHandler` and capture the new `WebContents`
### AI response truncated
- Increase `max_tokens` in the LLM call (default 8192, increase to 16384)
- Reduce request body size in `data-assembler.ts` — truncate large response bodies to first 2000 chars
### Requests missing response bodies
- CDP `Fetch.getResponseBody` must be called before `Fetch.continueRequest`
- Binary/gzip responses need base64 decoding: check `base64Encoded` field in CDP response
- Some streaming responses (SSE) can't have body captured synchronously — mark as SSE and capture chunks via `Network.eventSourceMessageReceived`
### HTTPS interception not working
- CDP Fetch interception works on all HTTPS by default in Electron's WebContentsView
- If a site uses certificate pinning, it may reject interception — look for `ERR_CERT_*` in request errors
### App window blank on startup
```bash
# Check renderer build
pnpm dev
# Look for Vite errors in terminal — usually missing env vars or import errors
```
## Development Tips
- **Hot reload**: `pnpm dev` uses electron-vite with HMR for renderer and restart for main
- **Devtools**: In dev mode, DevTools auto-opens for renderer; use `Ctrl+Shift+I` for embedded browser webview devtools
- **SQLite inspection**: Use [DB Browser for SQLite](https://sqlitebrowser.org/) on `%APPDATA%/anything-analyzer/analyzer.db` (Windows) or `~/Library/Application Support/anything-analyzer/analyzer.db` (macOS)
- **IPC debugging**: Add `console.log` in `ipc.ts` handlers — logs appear in Electron main process terminal
- **CDP raw events**: Enable `cdp.on('*', console.log)` in `cdp-manager.ts` during development to see all CDP eventsRelated Skills
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