by Jpickard1 · Agent Tool · ★ 64
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install -U BRAD-Agent
docker pull thedoodler/brad:mainTaken from the project's README on GitHub. Check the security audit before running it.
Flagged: install step in a code block runs a script from an untrusted host. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
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 | 64 |
| Forks | 7 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 62/100 |
| Open Issues | 3 |
| Last Updated | 2025-05-14 |
| Created | 2024-05-11 |
| Platforms | python |
| Est. Tokens | ~27260k |
Bioinformatics Retrieval Augmented Digital (BRAD) Assistant 📢 Update (May 2025): Our manuscript, Automatic biomarker discovery and enrichment with BRAD, is now published in Bioinformatics. BRAD is a digital assistant designed to streamline bioinformatics workflows by leveraging the power of Large Language Models (LLMs).
Looking for a BRAD alternative? If you're comparing BRAD 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.
Multi-Agent Conversation Framework in TypeScript
Roy: A lightweight, model-agnostic framework for crafting advanced multi-agent systems using large language mo
Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dyna
Autogen studio docker
AI Lawyer is an intelligent reasoning legal assistant powered by DeepSeek , Ollama RAG and LangChain, designed
TapeAgents is a framework that facilitates all stages of the LLM Agent development lifecycle
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: