Skill: gravyxbt/x-voice-match (version 1.0.0)

--- name: x-voice-match description: 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. --- # 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** ```bash cd /data/workspace/skills/x-voice-match python3 scripts/analyze_voice.py @username [--tweets 50] [--output profile.json] ``` **Step 2: Generate posts** ```bash python3 scripts/generate_post.py --profile profile.json --topic "your topic" [--count 3] ``` Or use the all-in-one approach: ```bash 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) ```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`): ```bash # 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](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: ```bash python3 scripts/analyze_voice.py @username --update profile.json ``` ### Generate by Post Type ```bash python3 scripts/generate_post.py --profile profile.json --type "hot-take" --topic "crypto" ``` ### Batch Generation ```bash python3 scripts/generate_post.py --profile profile.json --batch topics.txt --output posts.json ``` ## Workflow 1. **First time**: Run full analysis on 30-50 tweets 2. **Generate posts**: Provide topic, get 3-5 style-matched options 3. **User picks**: Select best option or iterate with feedback 4. **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


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)