X Voice Match

Analyze a Twitter/X account's posting style and generate authentic posts that match their voice. Use when the user wants to create X posts that sound like them, analyze their posting patterns, or maintain consistent voice across posts. Works with Bird CLI integration.

Audits

Suspicious

ClawScanSuspicious

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install x-voice-match

X Voice Match

Analyze Twitter/X accounts to extract posting patterns and generate authentic content that matches the account owner's unique voice.

Quick Start

Step 1: Analyze the account

cd /data/workspace/skills/x-voice-match
python3 scripts/analyze_voice.py @username [--tweets 50] [--output profile.json]

Step 2: Generate posts

python3 scripts/generate_post.py --profile profile.json --topic "your topic" [--count 3]

Or use the all-in-one approach:

python3 scripts/generate_post.py --account @username --topic "AI agents taking over" --count 5

What It Analyzes

The skill extracts:

Length patterns: Tweet character counts, thread usage, one-liner vs paragraph style
Tone markers: Humor style, sarcasm, self-deprecation, edginess level
Topics: Crypto, AI, tech, memes, personal life, current events
Engagement patterns: QT vs original, reaction tweets, conversation starters
Language patterns: Specific phrases, slang, emoji usage, punctuation style
Content types: Observations, hot takes, memes, threads, questions, personal stories

Output Format

Voice Profile (JSON)

{
  "account": "@gravyxbt_",
  "analyzed_tweets": 50,
  "patterns": {
    "avg_length": 85,
    "length_distribution": {"short": 0.6, "medium": 0.3, "long": 0.1},
    "uses_threads": false,
    "humor_style": "self-deprecating, ironic",
    "topics": ["crypto", "AI agents", "memes", "current events"],
    "engagement_type": "reactive QT heavy",
    "signature_phrases": ["lmao", "fr", "based"],
    "emoji_usage": "minimal, strategic",
    "punctuation": "lowercase, casual"
  }
}

Generated Posts

Returns 1-N posts with confidence scores and reasoning.

Integration with Existing Tools

Works with Bird CLI (/data/workspace/bird.sh):

# Fetch fresh tweets for analysis
./bird.sh user-tweets @gravyxbt_ -n 50 > recent_tweets.txt
python3 scripts/analyze_voice.py --input recent_tweets.txt

Post Type Templates

See references/post-types.md for common X post frameworks:

Observations
Hot takes
Self-deprecating humor
Crypto commentary
Reaction posts
Questions

Advanced Usage

Update Voice Profile

Re-analyze periodically to capture style evolution:

python3 scripts/analyze_voice.py @username --update profile.json

Generate by Post Type

python3 scripts/generate_post.py --profile profile.json --type "hot-take" --topic "crypto"

Batch Generation

python3 scripts/generate_post.py --profile profile.json --batch topics.txt --output posts.json

Workflow

First time: Run full analysis on 30-50 tweets
Generate posts: Provide topic, get 3-5 style-matched options
User picks: Select best option or iterate with feedback
Periodic updates: Re-analyze monthly or after major voice shifts

Tips

Minimum tweets: 30 tweets for basic analysis, 50+ for accuracy
Recency matters: Recent tweets reflect current style better than old ones
Topic relevance: Generated posts work best on topics the account normally covers
Confidence scores: <70% = may not sound authentic, revise or regenerate