three-man-team
A structured 3-agent AI dev team — Architect, Builder, Reviewer. Built from production use. Token-optimized. Works with Claude Code, VS Code, Cursor, and any AI that supports context files.
No outside reports on file
This skill automates code review through AI agents, which can miss real problems and expose your code to the AI service — review output carefully and keep secrets out of files these agents see.
Based on public adoption and transparency signals as of Oct 23, 2026. Our opinion, not a code audit or security guarantee.
What it can access
- Local filesNo local folder access notedLOW
- Command executionNo shell command runner notedLOW
- Network reachLocal or standard API callsLOW
- CredentialsRequires API key or tokenREVIEW
- Package sourceGitHub (russelleNVy/three-man-team)PUBLIC
Inferred from this listing’s public package details and description. Check the project’s source before installing.
Install
No npm or PyPI package is listed. Install from source: github.com/russelleNVy/three-man-team ↗
Everything known
We found nothing about this project in the sources we read (last checked 2026-10-30). That does not mean it has no problems.
0 incidents · 0 advisories · 0 news articles · 0 community threads · checked 2026-10-30
Popularity grade: C (from public signals such as stars and downloads, not a code review). Graded 2026-10-23.
Sources we read: OSV, GitHub Advisory Database, CISA Known Exploited Vulnerabilities, 44 security news feeds and our incident library. Last checked 2026-10-30.
About Three Man Team
This skill sets up three AI agents that work together like a small development team — one designs the plan, one builds the code, and one checks the work. It's designed to save tokens (the AI's memory units) and work with tools like Claude Code, VS Code, and Cursor.
Who it’s for
Software developers who want AI to help write code in an organized, team-like way. It's useful if you're already using Claude or similar AI tools and want a structured approach to code generation and review.
Alternatives
- GitHub Copilot for code review: Built-in AI review without custom agent setup.
- Traditional CI/CD linters and static analysis: Automated but rule-based, not AI-driven.
- Manual peer review workflows: Slower but fully under your control and without external dependencies.
Setup
Check these steps against the project’s own instructions before running anything.
Follow the project's README. The skill appears to be a context file or configuration system rather than an npm package, so installation steps may vary based on your AI tool.
Why this grade
Signal-based grade (3/7 points): well starred on GitHub, public source code. This reflects adoption and transparency, not a code review.
Popularity and upkeep
- GitHub stars
- 951
- Listing last refreshed
- 2026-10-29
These are public popularity signals, not a maintenance check. Look at the repository’s commit history and open issues.
Risks to watch for
- Prompt injection attacks: The reviewer and builder agents might be tricked by malicious code comments or file contents to approve unsafe changes.
- Data leakage: Code files the agents see could contain secrets, API keys, or proprietary information that gets processed by the AI.
- Blind trust in automation: The three-agent system might make mistakes seem more credible because multiple agents reviewed them, creating false confidence.
- Context pollution: If used across many projects, agent instructions and file contents from one project could accidentally influence decisions in another.
- Dependency on AI provider: The skill only works through AI services (Claude, etc.), so your code review workflow depends on their availability and policies.
How to lower the risk
Least-privilege permissions
- Never give the agents access to production keys, passwords, or secrets.
- Keep agents focused on code files only; exclude config files with sensitive data.
Network isolation
- Run locally through Claude Code or Cursor when possible.
- Review what data leaves your machine to the AI provider.
Monitoring
- Check agent decisions manually before committing or deploying code.
- Log which projects and files were analyzed for security audits.
Red flags to watch for
- Agents approving code changes you didn't expect or don't understand.
- Unexplained processing delays or unusual AI responses.
- Code suggestions that match patterns from other public projects.
Questions
How do I install Three Man Team?
Three Man Team does not publish a standalone npm or PyPI package. Clone or follow the setup instructions in its source repository at github.com/russelleNVy/three-man-team.
Are there security reports about Three Man Team?
No reports found for this package in our research (last checked 2026-10-30). Three Man Team currently holds Grade C (Moderate risk signals) based on public adoption and transparency signals. Key risk areas to watch include prompt injection attacks, data leakage, blind trust in automation.
Is Three Man Team actively maintained?
Three Man Team has 951 GitHub stars. Our directory data was last refreshed on 2026-10-29. Check recent commit activity, release tags, and open issues at github.com/russelleNVy/three-man-team before pinning it in production.
What are alternatives to Three Man Team?
Notable alternatives to Three Man Team include GitHub Copilot for code review (Built-in AI review without custom agent setup), Traditional CI/CD linters and static analysis (Automated but rule-based, not AI-driven), Manual peer review workflows (Slower but fully under your control and without external dependencies). You can also compare Three Man Team side by side with other skills in the Related tools section on this page.
README badge and team notes
Maintain or use Three Man Team? Show its live grade in your GitHub README, docs site, or run a check from the terminal before you install.
GitHub README Markdown:
[](https://agentarmorhq.com/skills/russellenvy-three-man-team)HTML embed:
<a href="https://agentarmorhq.com/skills/russellenvy-three-man-team"><img src="https://agentarmorhq.com/api/badge/russellenvy-three-man-team" alt="AgentArmorHQ Risk Grade for Three Man Team" height="20" /></a>Terminal check:
curl -fsSL https://agentarmorhq.com/api/cli | node --input-type=module - check "russellenvy-three-man-team"Share with your security lead
Need sign-off to use three-man-team? Copy this note for Slack, a ticket or a pull request.
### AgentArmorHQ Security Review Memo: three-man-team
- **Listing URL:** https://agentarmorhq.com/skills/russellenvy-three-man-team
- **Type:** AI agent skill
- **Risk Grade:** Grade C (Moderate risk signals)
- **Outside Security Advisories on File:** 0
- **Public Adoption:** 951 GitHub stars
#### Permission & Blast-Radius Signals
- **Local files:** No local folder access noted
- **Command execution:** No shell command runner noted
- **Network reach:** Local or standard API calls
- **Credentials:** Requires API key or token
- **Package source:** GitHub (russelleNVy/three-man-team)
#### Recommended Hardened Setup
- **Source Repo:** https://github.com/russelleNVy/three-man-team
- **Controls:** Pin an exact version in `.mcp.json`, scope access to a single workspace folder, and use a least-privilege read-only token where supported.
_Based on public adoption and transparency signals as of Oct 23, 2026. Our opinion, not a code audit or security guarantee._Key facts
- Type
- AI agent skill
- Publisher
- russelleNVy
- Source code
- github.com/russelleNVy/three-man-team
- Est. context load
- ~75 tokens idle · ~450 tokens when active (on-demand skill)
- Popularity
- 951 GitHub stars
- Last updated
- 2026-10-29
- Last graded
- 2026-10-23
Community
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