AutoBCI-Harness
New你的 7×24 小时自动研究助手;可审计的 AI 研究循环控制面,不是脑机接口系统 / Auditable research-loop control plane for AI agent workflows; not a brain-computer interface system
Summary
AutoBCI-Harness provides a 7×24 automated research loop control plane for AI agent workflows, enabling auditable and continuous investigation cycles.
- It helps developers build and manage persistent, self-improving research agents that can autonomously gather data, test hypotheses, and document findings without manual intervention.
Install & Usage
mkdir -p .claude/skillsAdd the configuration to .claude/skills/autobci-harness.md
/autobci-harnessUse Cases
Usage Examples
/autobci-harness start research-loop --target example.com --mode recon --output /reports
Launch a continuous vulnerability research agent for the latest CVEs and summarize findings every 6 hours.
/autobci-harness audit session --id session_123 --format json
Security Audits
Frequently Asked Questions
What is AutoBCI-Harness?
AutoBCI-Harness provides a 7×24 automated research loop control plane for AI agent workflows, enabling auditable and continuous investigation cycles. It helps developers build and manage persistent, self-improving research agents that can autonomously gather data, test hypotheses, and document findings without manual intervention.
How to install AutoBCI-Harness?
To install AutoBCI-Harness: create the skills directory (mkdir -p .claude/skills), then add the config to .claude/skills/autobci-harness.md. Finally, /autobci-harness in Claude Code.
What is AutoBCI-Harness best for?
AutoBCI-Harness is a other categorized under General. It is designed for: agent. Created by infoechoes.
What can I use AutoBCI-Harness for?
AutoBCI-Harness is useful for: Automate vulnerability research by running continuous scans and correlating findings across multiple sources.; Maintain a persistent threat intelligence agent that monitors security feeds and generates daily briefs.; Create an auditable research pipeline for penetration testing, where each step is logged and reviewable.; Deploy a self-improving code review agent that learns from past findings and adapts its analysis rules.; Run long-running security experiments that iterate on attack vectors and automatically document results.; Orchestrate multi-step research workflows that require chaining tools, APIs, and reasoning loops..