No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Chorus-AIDLC · Agent Tool · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Chorus safe to install? View the security audit →
The Agent Harness for AI-Human Collaboration 中文 Chorus is an agent harness — the infrastructure that wraps around LLM agents to manage session lifecycle, task state, sub-agent orchestration, observability, and failure recovery. It lets multiple AI Agents (with fine-grained, configurable permissions) and humans collaborate through the full workflow from requirements to delivery. Inspired by the AI-DLC (AI-Driven Development Lifecycle) methodology. Core philosophy: Reversed Conversation — AI proposes, humans verify. AI-DLC Workflow Idea ── Proposal ── [Document + Task DAG] ── Execute ── Verify ── Done ^ ^ ^ ^ ^ ^ Human idea:write proposal:write task:write :admin
| Stars | 1,168 |
| Forks | 105 |
| Language | TypeScript |
| Category | Agent Tool |
| License | AGPL-3.0 |
| Quality Score | 71.9713144506344/100 |
| Open Issues | 28 |
| Last Updated | 2026-09-11 |
| Created | 2026-02-04 |
| Platforms | claude-code, node |
| Est. Tokens | ~18k |
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Chorus is The Agent Harness for AI-Human Collaboration, inspired by the AI-DLC (AI-Driven Development Lifecycle). It is categorized as a Agent Tool with 1.2k GitHub stars.
Chorus is primarily written in TypeScript. It covers topics such as agent-harness, ai-agents, ai-dlc.
You can find installation instructions and usage details in the Chorus GitHub repository at github.com/Chorus-AIDLC/Chorus. The project has 1.2k stars and 105 forks, indicating an active community.
Chorus is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to Chorus on Agent Skills Hub include agent-kanban, oh-my-agent, takt. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: