by Araaf7 · Agent Tool · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 51 |
| Forks | 0 |
| Language | HTML |
| Category | Agent Tool |
| Quality Score | 62/100 |
| Last Updated | 2026-10-21 |
| Created | 2026-06-15 |
| Platforms | claude-code |
| Est. Tokens | ~17k |
🧪 LabRat Autonomous Multi-Agent Research Orchestrator Turn your research chaos into a symphony of intelligent agents. LabRat is not an experiment tracker. It is a self-organizing colony of AI research assistants that autonomously explore hypotheses, allocate market-style computational credits, and produce reproducible findings—all coordinated through a single YAML configuration.
These tools work well together with swarm-factory for enhanced workflows:
Looking for a swarm-factory alternative? If you're comparing swarm-factory with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Autonomous multi-branch research lab. Branches compete for compute budget. The system converges on what works.
Top Autonomous Research Multi-Agent System for 2026 Lab Experiments
Claude Code plugin for autonomous AI research — multi-agent loops take a bare topic all the way to running exp
An open-source research system for autonomous investigation, recursive self-improvement, and human–AI collabor
Autonomous AI backend for deep research AI applications.
Reusable project context and specialist workflows for the AI tools you already use. Agents, skills, rules and
Explore other popular agent tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: