Quality Convergence Engine

Multi-dimensional Quality Acceptance and Problem Convergence Engine - Deeply deconstruct requirements, eliminate extreme defects, define absolutely objective...

This skill itself appears to do what it says: it's an instruction-only QA/convergence engine with no network calls or credential requests. The main concern is the metadata 'always: true' which forces the skill into every agent session even when it isn't relevant. Before enabling it permanently: 1) ask the publisher why always:true is needed and request removal or narrowing (e.g., trigger keywords or explicit opt-in). 2) Verify the repository/author (package.json points to a GitHub URL) and confirm the source matches the registry owner. 3) Test the skill using only non-sensitive example inputs to confirm it respects its block conditions and doesn't leak data. 4) If your agent can access external networks or secrets, be cautious: a permanently included skill increases exposure if later changed. If the author provides a clear justification for always:true and a verified repo, confidence would increase.

Type: OpenClaw Skill Name: quality-convergence-engine Version: 1.0.0 This skill bundle is designed to function as a 'Quality Acceptance and Problem Convergence Engine'. Its instructions in SKILL.md and examples in EXAMPLES.md consistently guide the AI agent to deconstruct requirements, identify potential defects, and define objective acceptance criteria, with a strong emphasis on security vulnerabilities (e.g., authentication bypass, CSRF, weak password storage, token security). There is no evidence of prompt injection attempting to subvert the agent for malicious purposes, nor any code or instructions indicating data exfiltration, malicious execution, persistence, or other harmful behaviors. The skill's purpose is to help users identify flaws in their own designs, making it a beneficial security-focused tool.