agentsclimarketplace

Agent red team blue team

Skill Amey-Thakur/AI-SKILLS/skills/multi-agent-teams/agent-red-team-blue-team

Run attacker and defender agents in a fix-and-re-attack loop so exploitable bugs get found, triaged, fixed, and verified before ship. Use when you want a system probed and hardened by an agent team rather than one pass of review.From its SKILL.md

Install
npx -y skills add Amey-Thakur/AI-SKILLS --skill agent-red-team-blue-team

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 29 days oldThe repository was created 29 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

3.6 KB, 783 tokens by cl100k_base, as published. Nobody here has run it

Red team, blue team agents

A single review pass finds only what one agent thinks to look for. Pit an attacker agent against a defender agent with an independent referee between them, and security becomes a loop that keeps score: every fix is re-attacked, every finding graded by someone who did not write it. Skip the referee and the attacker inflates its own exploits while the defender declares victory on the first patch.

Team

  • Red agent (security-engineer-role, penetration-test-prep): attacks an assigned surface and files findings.
  • Blue agent (security-code-review): fixes the highest-ranked findings and writes fix notes.
  • Referee (vulnerability-triage): dedupes, scores, and confirms, so neither side marks its own homework.

Shape: an adversarial pair driven around a fix-and-re-attack loop.

Method

  1. Assign the surface with rules of engagement. Split targets by trust boundary: authentication, input parsing, multi-tenancy, deserialization. Hand red a scope file listing in-bounds targets and forbidding the rest, so it probes the system, not the harness or third-party hosts.
  2. Run the red pass into findings.jsonl. One record per finding: target, technique, repro steps, evidence, claimed severity. Require a working repro; a finding without one is a hypothesis the referee will drop.
  3. Triage before anyone fixes. The referee dedupes, scores with CVSS adjusted for reachability, and cuts false positives into a ranked triage.md. Attackers over-rate severity and defenders under-rate it; a neutral score ends that argument.
  4. Blue fixes highest risk first. For each finding id, patch and write a fix note stating what changed and why the whole bug class is closed, not just the one repro.
  5. Red re-verifies against the patched build. Re-run every fixed finding (pass or fail) plus a regression sweep for new surface the patch opened. Verdicts return to findings.jsonl as verified or reopened.
  6. Loop to the bar. Stop when a full red pass yields no new critical or high and all triaged criticals are verified closed, or a round budget of three to five is spent. Ship remaining mediums under a written risk acceptance.

Run it

In Claude Code, spawn red and blue as separate subagents with their role skills loaded and one shared directory for the artifact files; the orchestrator runs red, referee, blue, red again, reading each file to pick the next call, and keeps the referee distinct so scoring stays independent. Port the loop to CrewAI as three agents in a sequential process re-queued each round, to AutoGen as a GroupChat whose referee gates turns, or to LangGraph as a cyclic graph with a conditional edge on the termination check.

Signals it works

  • The referee reopens some fixes, proving verification is real, not a rubber stamp.
  • Shipped severity is the referee's number, not red's or blue's.
  • New-critical count per round trends to zero instead of oscillating.

Boundaries

This runs the adversarial loop; it does not replace a human penetration test or a design threat model, which threat-modeling owns. Agents miss novel logic flaws and anything needing real credentials or physical access. Rules of engagement, disclosure, and the meaning of "critical" are your organization's policy, and a human signs the final risk acceptance.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 326,144. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.