Extracts core principles from your input, tests your understanding with challenging questions, and rewards progress with evolution points for cognitive growth.

What to consider before installing or running this skill: - The code and instructions are coherent with the claimed purpose (extracting key points, generating quizzes, tracking points). There are no required secrets or special binaries. - A prompt-injection signal (unicode control characters) was detected in the SKILL.md/prompt files. That can be used to try to manipulate model behavior or hide content; open the SKILL.md and prompt.md in a plain-text editor, remove suspicious invisible characters, and re-check the prompts before use. - The package has no enforced install step in the registry, but README shows example installation (pip, Docker, GitHub). If you install dependencies or run the Docker image, do so in an isolated environment (virtualenv, container) and verify the upstream repository/image authors (the provided URLs are examples and should be vetted). - The scripts write a local file (evolution_data.json). If you need to keep your environment clean, run the skill in a sandboxed directory or container. - The README contains optional network examples (bookmarklet, API endpoints, ghcr Docker image). If you deploy any of those, verify the remote endpoints and images — they could introduce network I/O or external code not present in the shipped scripts. Recommended actions before proceeding: 1. Inspect prompt.md and SKILL.md for hidden/unicode control characters and remove them. 2. Review scripts and requirements.txt locally; if you will install dependencies, do so inside a virtualenv or container. 3. If you plan to deploy a web/API or use the Docker image, validate the source (GitHub/ghcr) and review any server-side code for external network behavior. 4. Run the scripts on non-sensitive sample files first to confirm behavior, and back up or isolate directories where evolution_data.json will be written. If you want, I can: (a) show lines from SKILL.md around the detected control chars, (b) produce a sanitized prompt.md with invisible characters removed, or (c) scan the included Python files for any additional red flags.

Type: OpenClaw Skill Name: human-level-up Version: 3.1.0 The skill bundle is a gamified educational tool designed to help users learn from documents through 'cognitive atom' extraction and quizzes. The Python scripts (extract.py, quiz_generator.py, and evolution_tracker.py) handle local text processing and state management via a local JSON file without any network calls, obfuscation, or attempts to access sensitive system files. The instructions in skill.md and prompt.md are strictly aligned with the stated purpose of providing a Feynman-style learning experience and tracking 'evolution points'.