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.

This skill does what it says: it scrapes tweets (via the Bird CLI), builds a voice profile, and constructs an LLM prompt that includes sample tweets and signature phrases to generate posts that mimic an account. Before installing or running it, consider the following: - Impersonation & policy risk: Generating posts that mimic another account could violate platform rules or local law. Only use on accounts you own or have explicit permission to emulate. - Data leakage risk: The generation prompt includes actual tweet text and identifying info. If your agent sends that prompt to a remote LLM (OpenAI, Anthropic, etc.), those tweets will be transmitted off your system — verify where prompts are sent and which API keys or endpoints are used. - Trust the Bird CLI: The scripts call /data/workspace/bird.sh. Ensure that file is what you expect (not replaced by a malicious binary) and that its output format matches the parser assumptions. - Missing/incomplete files: The manifest shows the analyze_voice.py output truncated in this package listing; verify the local files are complete and match the source you expect before running. - Operational hygiene: Run the tool in a sandbox or test environment first, inspect generated prompts, and confirm no unexpected network calls occur. If you want safer operation, modify generate_post.py to redact or summarize sample tweets instead of including verbatim text in prompts, and add explicit logging of which external endpoints receive generation requests. If you need help auditing where prompts would be sent in your agent (which LLM endpoint) or hardening the scripts to avoid sending raw tweet text externally, I can suggest concrete code changes.

Type: OpenClaw Skill Name: x-voice-match Version: 1.0.0 The skill bundle is designed to analyze Twitter/X account voice and generate matching posts. It relies on an external utility, `/data/workspace/bird.sh`, to fetch tweets, which is a common pattern for skills interacting with the environment. The Python scripts (`analyze_voice.py`, `generate_post.py`) execute this utility using `subprocess.run` with controlled arguments, mitigating direct shell injection risks. The `SKILL.md` and Python scripts construct detailed prompts for the LLM to guide content generation, but these do not contain instructions to subvert the agent's core functions, exfiltrate data, or perform unauthorized actions. All actions are aligned with the stated purpose.