ProClaw HumanOS Ultimate（专业人物技能分析与构建）

ProClaw人类操作系统终极版：从需求诊断到人格演化的全链路HumanOS构建与分析系统；当用户需要构建顶级HumanOS、深度分析人格、追踪演化或整合矛盾时使用

This package appears coherent with its stated goal of building and analyzing persona/"HumanOS" artifacts, but exercise caution before installing or running it: - Source and provenance: The skill's source/homepage are unknown; prefer code from trusted authors. Review the repository locally before running. - Network mode: research_orchestrator supports a 'network' mode and the SKILL.md lists requests/BeautifulSoup as dependencies. Inspect research_orchestrator.py to see which sites or APIs it queries; avoid running network mode until you confirm endpoints and that no sensitive inputs are sent externally. - Generated artifacts: os_builder creates a SKILL.md / 'runnable skill' from outputs. Treat generated skills and code as untrusted until reviewed — do not automatically load or execute generated skills in a production agent. - Data safety: The scripts read/write files under ./output/<target> and references/. Run them in an isolated sandbox or throwaway VM and avoid providing secrets, personal data, or system file paths as input. - Code review: Because many scripts are included, skim for unexpected network calls, subprocess.exec usage, or code that sends data externally. If you are not comfortable auditing Python code, ask a developer to review the specific files: research_orchestrator.py, os_builder.py, and any script that has 'network' or 'requests' usage. If you want, I can: - Summarize or audit the specific scripts that perform network access (search for 'requests', 'urllib', or external hostnames) and report any endpoints found. - Highlight any code that writes outside its output directory or invokes subprocesses/exec calls.

Type: OpenClaw Skill Name: proclaw-humanos-ultimate Version: 1.0.0 The bundle is a comprehensive and well-structured system for personality modeling and 'HumanOS' generation. The Python scripts (e.g., research_orchestrator.py, framework_extractor.py, and neural_network_sim.py) perform logical data processing, statistical simulations using numpy, and template-based file generation. No evidence of data exfiltration, malicious command execution, or prompt injection was found. The code is transparent, modular, and aligns perfectly with the stated purpose of analyzing and building personality frameworks.