agent-team

A workflow for coordinating two software agents: one implements a task and the other reviews the result. They repeat this process until the review finds no high-severity issues and no more than three medium-severity issues, with a limit of ten rounds.

In plain words
What is it for?
Use it for tasks that benefit from repeated implementation and review, such as code changes, bug fixes, and larger development requests.
Why use it?
It adds a structured review loop to coding work, reducing the chance that serious problems remain unnoticed. The reviewer’s findings are passed back to the implementer for fixes.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/dyoshikawa/rulesync/agent-team
Any agent
npx skills add dyoshikawa/rulesync --skill agent-team
Clone the repo
git clone --depth 1 https://github.com/dyoshikawa/rulesync

Made for: Claude Code, Codex.

Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 959 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00042 $0.00959
Opus 5 $0.00021 $0.00479
Sonnet 5 $0.00008 $0.00192
Haiku 4.5 $0.00004 $0.00096

Measured 3d ago against content hash 33a2bf953f6d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agent-team scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.rulesync/skills/agent-team/SKILL.md · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Team

TASK = the user's request

If TASK is not provided, ask the user for the task description and stop.

Coordinate an Agent Team composed of an Implementer Agent and a Reviewer Agent. Iterate the implementation/review loop until the exit condition is satisfied.

0. Exit Condition

The loop exits when both of the following hold in a single review round:

  • 0 findings of severity high or critical.
  • 3 or fewer findings of severity mid.

Findings of severity low do not affect the exit condition.

Set a hard safety cap of 10 iterations. If the exit condition is still not met at the cap, stop the loop and report the remaining findings to the user for manual decision.

1. Iteration Loop

Repeat the following steps until the exit condition is satisfied.

1-1. Implementation Phase

Delegate to the Implementer Agent via the Agent tool.

  • subagent_type: general-purpose
  • Role framing: "You are the Implementer Agent on an Agent Team."
  • Inputs to pass:
    • The original TASK.
    • All Reviewer findings from the previous round (if any), grouped by severity.
  • Instructions to include in the prompt:
    • Implement the TASK end-to-end in the current repository.
    • Address every Reviewer finding from the previous round. For each finding, either fix it or, if you intentionally reject it, record the reason.
    • Edit files directly; do not only describe changes.
    • Run pnpm cicheck (or the narrower pnpm cicheck:code / cicheck:content when appropriate) and fix any failures before returning.
    • Report: a concise summary of the changes, the list of modified files, how each previous finding was handled, and the result of the checks.

1-2. Review Phase

Delegate to the Reviewer Agent via the Agent tool.

  • subagent_type: code-reviewer
  • Role framing: "You are the Reviewer Agent on an Agent Team."
  • Inputs to pass:
    • The original TASK.
    • The Implementer's summary and the list of modified files from this round.
  • Instructions to include in the prompt:
    • Review the changes for correctness, design quality, tests, and adherence to project conventions (see CLAUDE.md, docs/**/*.md, and .claude/rules/feature-change-guidelines.md).
    • Also consider security concerns.
    • Produce a findings list. For each finding, include:
      • Sequential number (e.g., #1, #2).
      • Severity: low / mid / high / critical.
      • File path and line number(s).
      • Problem description and recommended fix.
    • At the end of the report, include a Severity Summary with the counts per severity level so the exit condition can be evaluated mechanically.

Read the full file on GitHub · 106 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 106 lines · 42 tokens per session scan A 33a2bf953f6d

Subscribe to this mod's changes

agent-team is a skill published in the GitHub repository dyoshikawa/rulesync (1,373 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 959 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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