Lightweight Autoresearch V2

CPU-based autonomous optimization loop for skill quality improvement. Runs experiments, evaluates results, keeps improvements. Use when: 自主优化, skill optimiza...

This skill is coherent for its stated purpose but grants an agent the ability to edit, run, and commit code inside a target directory — an action with high potential impact. Before installing or running it: (1) only point it at a disposable or sandboxed repository (not production code or repos containing secrets); (2) require and verify human review of diffs before any git commit; (3) ensure the platform enforces the checkpoints (don’t rely solely on prose in SKILL.md); (4) inspect any run_loop.py/experiment.py provided by the target repo before execution; (5) monitor subprocess calls and network activity while the skill runs. If the publisher can supply the actual run_loop.py implementation and an explicit enforcement mechanism for confirmations and sandboxing, re-evaluate — that information would raise confidence and could change this assessment to benign.

Type: OpenClaw Skill Name: shike-autoresearch Version: 1.0.0 The skill implements an autonomous 'optimization loop' that explicitly instructs the AI agent to modify Python code (experiment.py) and execute it via subprocess. While the stated intent is skill optimization based on Karpathy's autoresearch, this architecture facilitates self-modifying code and arbitrary execution, which is a high-risk pattern. Although SKILL.md includes human-in-the-loop checkpoints to verify changes, the core functionality provides a direct mechanism for potential RCE if the agent is misdirected or the loop is automated without oversight.