README Sync

在每次代码查询/修改工作开始前按需读取README.md了解项目状态，工作结束后按需更新README。触发场景：(1)用户询问项目相关问题（如"并发数是多少？"），(2)需要进行代码查询或修改时。读取时无需确认；写入README或代码时必须先确认。支持跨平台（Claude Code、OpenClaw、Trae、C...

This skill appears to do what it says (read/scan/update README), but there is a meaningful inconsistency: the documentation promises user confirmation before any write, yet the included script performs writes without prompting (adding pending items and the sync operation). Before installing or enabling it for autonomous use, consider: - Review the full scripts locally. Search for functions named sync, add, auto-init and confirm whether they prompt before writing. In particular, patch sync_to_readme to require explicit user confirmation if you expect interactive approval. - Test the tool in a disposable repository (backup your README first). Verify behavior of add, pending, sync, init, and auto-init commands. - If you plan to allow autonomous agent invocation, disable autonomous use until you can ensure writes are always gated by an explicit user prompt. Alternatively, restrict the skill to user-invoked only. - Note the skill creates a .readme_pending.json file in the project root; review that file for any cached content you may not want committed. - There is a truncated/possibly buggy section in the provided script (a partial token like "item.name.star" appears in the package listing). Verify the script runs correctly and fix any bugs before use. If the author updates the code so that all write actions prompt for explicit confirmation (or documents clearly which commands are non-interactive), the current concerns would be resolved.

Type: OpenClaw Skill Name: readme-sync Version: 1.0.0 The skill is a utility designed to help AI agents maintain project documentation by synchronizing updates to a README.md file. It includes a Python script (scripts/readme_operations.py) that handles reading specific Markdown sections, scanning project directory structures to infer technology stacks, and performing incremental updates to the README. The instructions in SKILL.md explicitly require user confirmation for any file modifications and focus on token-efficient reading strategies. No indicators of data exfiltration, unauthorized network access, or malicious execution were identified.