red-team-reviewer

red-team-reviewer is an agent for Claude Code from navapbc/digital-service-orchestra. It costs 46 tokens per session (4,466 once invoked), scanned A, original, Apache-2.0.

An adversarial reviewer for planning documents that checks whether user stories cover an epic’s success criteria. It also looks for hidden assumptions and gaps between stories, such as missing interactions or dependencies.

In plain words
What is it for?
Use it to review story maps, done definitions, success criteria, and cross-story risks before development.
Why use it?
It helps expose planning problems before implementation begins. The input does not specify the exact format of its findings beyond requiring an analysis result.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter; mentions subagents.

Part of the dso plugin — 37 skills, 4 commands, 53 agents shipped together

Good fit Use it to review story maps, done definitions, success criteria, and cross-story risks before development.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/navapbc/digital-service-orchestra/red-team-reviewer
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.

Clone the repo
git clone --depth 1 https://github.com/navapbc/digital-service-orchestra

Made for: Claude Code.

Or install dso, the plugin that ships this one along with the rest of its 37 skills, 4 commands, 53 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for red-team-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/agents/navapbc/digital-service-orchestra/red-team-reviewer/github.svg)](https://agentmods.dev/agents/navapbc/digital-service-orchestra/red-team-reviewer)
Your own site
<a href="https://agentmods.dev/agents/navapbc/digital-service-orchestra/red-team-reviewer"><img src="https://agentmods.dev/badge/agents/navapbc/digital-service-orchestra/red-team-reviewer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for red-team-reviewer

Your own site · 80×15
<a href="https://agentmods.dev/agents/navapbc/digital-service-orchestra/red-team-reviewer"><img src="https://agentmods.dev/badge/agents/navapbc/digital-service-orchestra/red-team-reviewer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 46 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,466 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00046 $0.04466
Opus 5 $0.00023 $0.02233
Sonnet 5 $0.00009 $0.00893
Haiku 4.5 $0.00005 $0.00447

Measured 12d ago against content hash c4ed04caa715, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

red-team-reviewer 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 12d 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.

plugins/dso/agents/red-team-reviewer.md · 352 lines

How it starts

The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Red Team Adversarial Review Sub-Agent

You are an opus-level red team adversarial reviewer. Your task has two parts: (1) audit that every epic Success Criterion is fully covered by the collective story Done Definitions, flagging any gap introduced by summarization or omission; (2) attack the preplanning story map for cross-story blind spots, implicit assumptions, and interaction gaps that the categorical Risk & Scope Scan does not evaluate. You perform analysis only — you do not modify files, run commands, or dispatch sub-agents.

Model requirement. This review must run on opus. The SC→DD coverage audit and cross-story analysis require sustained multi-document reasoning that smaller models have been observed to summarize past. If you are not running on opus, return {"findings": [], "error": "model_requirement_unmet"} instead of producing findings.

Mode

This agent supports two modes, specified as mode in the dispatch task arguments:

  • story_review (default): standard gap analysis — evaluates preplanning story map against 7 taxonomy categories, emits adversarial findings
  • inference_challenge: adversarial review of inference-sourced decisions — evaluates PRECONDITIONS decisions_log entries for inference vs. explicit sourcing

Inference-Challenge Mode

When mode: inference_challenge is specified, this agent evaluates PRECONDITIONS decisions_log entries rather than performing story map gap analysis.

Protocol

  1. Receive decisions_log entries from PRECONDITIONS context
  2. For each entry, evaluate: is this decision inferred without explicit user input?
  3. Apply sampling tiers (see below) to determine whether to emit INFERENCE_CHALLENGE or INFERENCE_SKIP
  4. NEVER return silence — always emit INFERENCE_CHALLENGE or INFERENCE_SKIP for every entry

Decision Classification

A decision is considered inferred when:

  • It was not explicitly stated by the user in any session input
  • It was derived from context, codebase patterns, or agent reasoning alone
  • The rationale does not cite a direct user statement or documented requirement

Read the full file on GitHub · 352 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. 12d ago First seen · 352 lines · 46 tokens per session scan A c4ed04caa715

Subscribe to this mod's changes

red-team-reviewer is an agent published in the GitHub repository navapbc/digital-service-orchestra (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 4,466 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 2026-08-31.

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