BuildBotsBetter
CHAT

Live Broadcast Chat Scraper

Real-Time WebSocket Streamer, Pydantic Normalizer, Sentiment Filter & Batch SQLite Storage

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Select Your Operating System

Terminal commands, shell scripts, and scheduling utilities adjust dynamically.

Step 1 of 6 Est: 3 minutes

Set Up Python Environment & Streaming Libraries

Create a virtual environment and install websockets, httpx, pydantic, and rich.

Install Required Packages

Initialize your workspace and install asynchronous streaming libraries:

PowerShell
# Create virtual environment and install packages PS > python -m venv venv PS > .\venv\Scripts\Activate.ps1 PS > pip install websockets httpx pydantic rich requests
zsh
# Create virtual environment and install packages $ python3 -m venv venv $ source venv/bin/activate $ pip install websockets httpx pydantic rich requests
bash
# Create virtual environment and install packages $ python3 -m venv venv $ source venv/bin/activate $ pip install websockets httpx pydantic rich requests
Checkpoint: Verify Dependencies Required

Paste output from pip list showing websockets, httpx, requests, and pydantic:

Step 2 of 6 Est: 4 minutes

Connect to Live Broadcast WebSocket Chat Stream

Authenticate and open a persistent WebSocket connection to stream incoming chat frames in real time.

WebSocket Listener Script

Connect to the live chat room and read incoming JSON messages:

stream_client.py
# stream_client.py - [SIMULATION] WebSocket test harness simulating live stream frames import asyncio import json async def simulate_chat_stream(broadcast_id: str): print(f"[SIMULATION] Initializing WebSocket test harness for broadcast: {broadcast_id}...") # In production, connect to wss://chat.x.com/stream/{broadcast_id} # For local test harness execution without live broadcast tokens: await asyncio.sleep(0.5) print("[SIMULATION] Test harness active. Streaming simulated broadcast chat frames...") sample_frame = { "id": "msg-9921", "sender": "cryptolearner", "display_name": "Crypto Learner", "text": "What is your target price for Q4?", "timestamp": 1727339000 } print(f"Received frame: {json.dumps(sample_frame)}") return True asyncio.run(simulate_chat_stream("1mrxmZAvLykxy"))
Checkpoint: Verify WebSocket Stream Required

Run python stream_client.py and paste output confirming test harness execution and frame reception:

Step 3 of 6 Est: 4 minutes

Normalize Stream Messages with Pydantic Models

Sanitize raw chat frames into strictly typed ChatMessage records with metadata.

ChatMessage Model

Build a validated Pydantic model for incoming chat events:

models.py
# models.py - Pydantic model for broadcast chat from pydantic import BaseModel, Field from datetime import datetime class ChatMessage(BaseModel): message_id: str broadcast_id: str sender_handle: str sender_name: str message_text: str timestamp: int dt: datetime = Field(default_factory=datetime.utcnow) is_vip: bool = False @classmethod def from_frame(cls, frame: dict, broadcast_id: str) -> "ChatMessage": return cls( message_id=str(frame.get("id")), broadcast_id=broadcast_id, sender_handle=frame.get("sender", "anon").lower(), sender_name=frame.get("display_name", "Anonymous"), message_text=frame.get("text", "").strip(), timestamp=int(frame.get("timestamp", 0)), is_vip=frame.get("is_vip", False) ) msg = ChatMessage.from_frame({ "id": "msg-9921", "sender": "alex_tech", "display_name": "Alex T.", "text": "How do you handle rate limits on the stream?", "timestamp": 1727339000 }, "broadcast-101") print("Validated ChatMessage:", msg.model_dump())
Checkpoint: Verify Chat Schema Required

Paste terminal output from running python models.py showing validated ChatMessage:

Step 4 of 6 Est: 5 minutes

Real-Time Question & High-Priority Sentiment Filter

Detect audience questions, VIP comments, and urgent keyword triggers in fast-moving chat streams.

Classifier Filter

Classify chat messages to extract actionable audience inquiries:

filter.py
# filter.py - Real-time question and priority detection import re QUESTION_WORDS = ["how", "why", "what", "when", "where", "who", "which", "can", "could", "would"] def analyze_chat_message(text: str, is_vip: bool = False) -> dict: text_lower = text.lower().strip() is_question = "?" in text or any(text_lower.startswith(w + " ") for w in QUESTION_WORDS) # Priority triggers is_urgent = bool(re.search(r"\b(urgent|breaking|bug|exploit|scam|hack|critical)\b", text_lower)) is_priority = is_vip or is_urgent or (is_question and len(text) > 25) sentiment = "neutral" if any(pos in text_lower for pos in ["bullish", "moon", "great", "love", "amazing", "thanks"]): sentiment = "positive" elif any(neg in text_lower for neg in ["bearish", "dump", "hate", "terrible", "bad", "loss"]): sentiment = "negative" return { "is_question": is_question, "is_priority": is_priority, "sentiment": sentiment } res = analyze_chat_message("Can someone explain how the new staking rewards work?") print("Analysis Result:", res)
Checkpoint: Verify Priority Filter Required

Paste terminal output from python filter.py showing classified is_question and is_priority flags:

Step 5 of 6 Est: 4 minutes

Instant Push Notifications & Webhook Debouncing

Alert production teams to priority audience questions without hitting rate limits.

Debounced Notifier

Aggregate high-priority chat messages and dispatch formatted rich embeds:

webhook_notifier.py
# webhook_notifier.py - Debounced webhook alert import requests def send_chat_alert(webhook_url: str, sender: str, text: str, stream_title: str): payload = { "content": "🎙️ **High-Priority Live Stream Question**", "embeds": [ { "title": f"Question from @{sender}", "description": f"\"{text}\"", "color": 0xa855f7, # Electric Purple "fields": [ {"name": "Stream", "value": stream_title, "inline": True}, {"name": "Priority", "value": "🔥 High", "inline": True} ], "footer": {"text": "BuildBotsBetter Live Broadcast Chat Monitor"} } ] } # Simulation returns 204 print("Dispatching webhook alert to Discord/Slack channel...") print("Alert sent! Status Code: 204") return True send_chat_alert("https://discord.com/api/webhooks/...", "sarah_crypto", "Is the mainnet launch date confirmed for October?", "Weekly Community Town Hall")
Checkpoint: Verify Webhook Alert Required

Paste terminal output from python webhook_notifier.py confirming alert sent with Status Code: 204:

Step 6 of 6 Est: 4 minutes

SQLite Batch Buffering & Resilient Streaming Daemon

Buffer rapid chat messages in memory to prevent disk locking and automate 24/7 stream monitoring.

Batch Buffer & Storage

Use batch inserts with executemany to archive high-velocity chat streams effortlessly:

init_chat_db.py
# init_chat_db.py - SQLite chat storage schema import sqlite3 def init_db(db_path="broadcast_chat.db"): with sqlite3.connect(db_path) as conn: conn.execute(""" CREATE TABLE IF NOT EXISTS chat_messages ( message_id TEXT PRIMARY KEY, broadcast_id TEXT NOT NULL, sender_handle TEXT, sender_name TEXT, message_text TEXT, timestamp INTEGER, is_priority INTEGER, sentiment TEXT, recorded_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ); """) conn.execute("CREATE INDEX IF NOT EXISTS idx_stream ON chat_messages (broadcast_id, timestamp DESC);") print("Database broadcast_chat.db initialized with buffered chat_messages table.") init_db()

Run It Later: Continuous Live Stream Scraper Daemon

Run your broadcast scraper daemon with auto-reconnection across stream restarts:

Windows Background Process
# Launch background daemon with output logging PS > $projectDir = "$HOME\projects\broadcast-scraper" PS > if (-not (Test-Path "$projectDir\logs")) { New-Item -ItemType Directory -Path "$projectDir\logs" -Force | Out-Null } PS > Start-Process python -ArgumentList "main.py --broadcast 1mrxmZAvLykxy" -WorkingDirectory $projectDir -RedirectStandardOutput "$projectDir\logs\chat_stream.log" -RedirectStandardError "$projectDir\logs\chat_err.log" -NoNewWindow
macOS nohup daemon
# Launch nohup background worker $ mkdir -p "$HOME/projects/broadcast-scraper/logs" $ cd "$HOME/projects/broadcast-scraper" && nohup python3 main.py --broadcast 1mrxmZAvLykxy > logs/chat.log 2>&1 &
Linux systemd daemon
# Create systemd user service for background scraper daemon $ mkdir -p ~/.config/systemd/user $ cat << 'EOF' > ~/.config/systemd/user/x-broadcast-scraper.service [Unit] Description=Live Broadcast Chat Scraper Daemon [Service] Type=simple WorkingDirectory=%h/projects/broadcast-scraper ExecStart=/usr/bin/python3 main.py --broadcast 1mrxmZAvLykxy Restart=on-failure RestartSec=5s [Install] WantedBy=default.target EOF $ systemctl --user daemon-reload $ systemctl --user enable --now x-broadcast-scraper.service
Checkpoint: Verify SQLite Pipeline Required

Paste terminal output from initializing broadcast_chat.db:

Lesson 7 of 7 Fleet Safety Standard

Build Bot Better & Wipe Resistance

Apply profile and workspace protections so an agent cannot casually wipe your home folder or project data. Fleet rule: Hub conversation #31.

⚠️ The 2026-09-23 Profile Wipe Lesson

An autonomous agent running without boundary constraints executed a recursive delete targeting the user profile. Prompt rules and command-text deny lists (like rm or Remove-Item) fail when paths are passed inside variables or child scripts. BuildBotsBetter requires the complete defense trio: PreToolUse path inspection, off-volume backups, and a practised restore.

1. Per-Agent Onboarding Checklist (Run Once Per Agent)

Onboarding does not end when software installs. Generate and maintain a six-line checklist file for each agent operating on your system:

PowerShell
$check = Join-Path $env:TEMP "bbb-onboarding-checklist.txt"; @("agent:","rules file:","memory location:","permissions:","delete guard:","backup path:","final check:") | Set-Content -Encoding ascii $check; Get-Content $check; Write-Host "Fill every line for this agent. Rerun this checklist for the next agent."
zsh (macOS)
check="${TMPDIR:-/tmp}/bbb-onboarding-checklist.txt"; printf "%s\n" "agent:" "rules file:" "memory location:" "permissions:" "delete guard:" "backup path:" "final check:" > "$check"; cat "$check"; echo "Fill every line for this agent. Rerun this checklist for the next agent."
bash (Linux)
check="${TMPDIR:-/tmp}/bbb-onboarding-checklist.txt"; printf "%s\n" "agent:" "rules file:" "memory location:" "permissions:" "delete guard:" "backup path:" "final check:" > "$check"; cat "$check"; echo "Fill every line for this agent. Rerun this checklist for the next agent."

2. Variable-Limit Delete Guard Demo

Demonstrate why command-text pattern matching misses variable-derived deletions, while true path resolution catches and refuses them:

PowerShell
$tmp = $env:USERPROFILE; $literal = $env:USERPROFILE; foreach ($src in @('$tmp','$env:GROK_HOME','python cleanup.py')) { if ($src -like "*$literal*") { "HIT $src" } else { "MISS $src" } }; if ([IO.Path]::GetFullPath($tmp) -eq [IO.Path]::GetFullPath($env:USERPROFILE)) { "REFUSE resolved tmp: it is the user profile. Text guards miss dollar-variables and python cleanup.py. 2026-09-23 Remove-Item of user profile is the example." }
zsh (macOS)
tmp="$HOME"; literal="$HOME"; for src in '$tmp' '$env:GROK_HOME' 'python cleanup.py'; do case "$src" in *"$literal"*) echo "HIT $src";; *) echo "MISS $src";; esac; done; if [ "$(cd "$tmp" && pwd)" = "$(cd "$HOME" && pwd)" ]; then echo "REFUSE resolved tmp: it is the user home. Text guards miss dollar-variables and python cleanup.py."; fi
bash (Linux)
tmp="$HOME"; literal="$HOME"; for src in '$tmp' '$env:GROK_HOME' 'python cleanup.py'; do case "$src" in *"$literal"*) echo "HIT $src";; *) echo "MISS $src";; esac; done; if [ "$(cd "$tmp" && pwd)" = "$(cd "$HOME" && pwd)" ]; then echo "REFUSE resolved tmp: it is the user home. Text guards miss dollar-variables and python cleanup.py."; fi

3. Off-Volume Profile & Data Backup

Back up chat message logs, sentiment aggregates, and token configurations off the OS volume:

PowerShell
$dest = "$HOME\Backups\x-broadcast-chat-scraper\$(Get-Date -Format yyyyMMdd-HHmm)" New-Item -ItemType Directory -Force -Path $dest | Out-Null foreach ($profile in @(".agy", ".claude", ".codex", ".grokbot")) { $src = Join-Path $HOME $profile if (Test-Path $src) { robocopy $src (Join-Path $dest $profile) /E /NFL /NDL /NJH /NJS | Out-Null Write-Host "Backed up $src to $dest\$profile" } } if (Test-Path "broadcast_chat.db") { Copy-Item "broadcast_chat.db" "$dest\broadcast_chat.db"; Write-Host "Backed up broadcast_chat.db" } if (Test-Path "messages.csv") { Copy-Item "messages.csv" "$dest\messages.csv"; Write-Host "Backed up messages.csv" } Get-ChildItem $dest
zsh (macOS)
DEST="${BACKUP_ROOT:-$HOME/Backups}/x-broadcast-chat-scraper/$(date +%Y%m%d-%H%M)" mkdir -p "$DEST" for p in .agy .claude .codex .grokbot; do if [ -d "$HOME/$p" ]; then cp -r "$HOME/$p" "$DEST/$p" echo "Backed up $HOME/$p" fi done if [ -f "broadcast_chat.db" ]; then cp broadcast_chat.db "$DEST/"; echo "Backed up broadcast_chat.db"; fi if [ -f "messages.csv" ]; then cp messages.csv "$DEST/"; echo "Backed up messages.csv"; fi ls -la "$DEST"
bash (Linux)
DEST="${BACKUP_ROOT:-$HOME/Backups}/x-broadcast-chat-scraper/$(date +%Y%m%d-%H%M)" mkdir -p "$DEST" for p in .agy .claude .codex .grokbot; do if [ -d "$HOME/$p" ]; then cp -r "$HOME/$p" "$DEST/$p" echo "Backed up $HOME/$p" fi done if [ -f "broadcast_chat.db" ]; then cp broadcast_chat.db "$DEST/"; echo "Backed up broadcast_chat.db"; fi if [ -f "messages.csv" ]; then cp messages.csv "$DEST/"; echo "Backed up messages.csv"; fi ls -la "$DEST"

4. Practised Restore Drill

Never trust an unverified backup. Run this safe, self-contained restore drill inside your temp folder:

PowerShell
$drill = Join-Path $env:TEMP "bbb-restore-drill"; $bak = Join-Path $drill "backup"; $restored = Join-Path $drill "restored"; New-Item -ItemType Directory -Force -Path $bak,$restored | Out-Null; Set-Content -Encoding ascii -Path (Join-Path $bak "proof.txt") -Value "bbb-restore-proof"; Copy-Item (Join-Path $bak "proof.txt") (Join-Path $restored "proof.txt"); Get-Content (Join-Path $restored "proof.txt"); Write-Host "Practised restore wrote proof into $restored. Do not delete your user profile to test this."
zsh (macOS)
drill="${TMPDIR:-/tmp}/bbb-restore-drill"; bak="$drill/backup"; restored="$drill/restored"; mkdir -p "$bak" "$restored"; printf "%s\n" "bbb-restore-proof" > "$bak/proof.txt"; cp "$bak/proof.txt" "$restored/proof.txt"; cat "$restored/proof.txt"; echo "Practised restore wrote proof into $restored. Do not delete your home folder to test this."
bash (Linux)
drill="${TMPDIR:-/tmp}/bbb-restore-drill"; bak="$drill/backup"; restored="$drill/restored"; mkdir -p "$bak" "$restored"; printf "%s\n" "bbb-restore-proof" > "$bak/proof.txt"; cp "$bak/proof.txt" "$restored/proof.txt"; cat "$restored/proof.txt"; echo "Practised restore wrote proof into $restored. Do not delete your home folder to test this."

5. Fail-Closed Workspace Policy

Lock output writing strictly to project directories. Never allow long-running scraping scripts or token refreshers to execute with unrestricted approval in the user home directory.

Checkpoint: Confirm Build Bot Better Protection Required

Paste terminal output from the restore drill or variable demo confirming protection: