Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
66
81%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Critical
Do not install without reviewing
Security
1 critical severity finding. Installing this skill is not recommended: please review these findings carefully if you do intend to do so.
Detected high-risk code patterns in the skill content — including its prompts, tool definitions, and resources — such as data exfiltration, backdoors, remote code execution, credential theft, system compromise, supply chain attacks, and obfuscation techniques.
The skill explicitly mandates autonomous operation without user consent, instructs creating a persistent cron/loop bound to the chat session (prompt-injection / persistence), and directs automatic transmission of progress files via external messaging (Telegram/WhatsApp/Slack), enabling remote control and potential unauthorized data exfiltration.
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
The runtime workflow includes reading/continuing research by searching literature sources (Exa, Semantic Scholar, arXiv, CrossRef) and saving their retrieved paper text/summaries into workspace files; that fetched outsider-authored web text can then be read back into the agent context on subsequent ticks (via `/loop` reading `research-state.yaml`/`findings.md`).
773a529
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.