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Red team review

Skill richfrem/agent-plugins-skills/plugins/agent-loops/skills/red-team-review

repo for reusable plugins and skills

Install
npx -y skills add richfrem/agent-plugins-skills --skill red-team-review

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(Industry standard: Review and Critique Pattern) Primary Use Case: Iterative generation paired with adversarial review, continuing until an 'Approved' verdict is reached. Orchestrated adversarial review loop. Use when: research, designs, architectures, or decisions need to be reviewed by red team agents (human, browser, or CLI). Iterates in rounds of research → bundle → review → feedback until approved.

SKILL.md

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Red Team Review Loop

An iterative review loop where research is bundled via context-bundler and dispatched to one or more adversarial reviewers. The loop continues until the red team approves.

When to Use

  • Architecture or design decisions that need adversarial scrutiny
  • Research findings that need epistemic validation
  • Security analysis that needs independent verification
  • Any work product where "more eyes" reduce risk

Process Flow

  1. Research & Analyze — Deep-dive into the problem domain. Create analysis docs, capture sources.
  2. Review Packet Generation — Prepare the context for the reviewer:
    • Create Prompt: Write or update a red-team-prompt.md explaining exactly what is being reviewed and what the reviewer should focus on.
    • Define Manifest: Update a manifest.json or equivalent list dictating which source files and research artifacts to include.
    • Bundle Context: Execute the context-bundler plugin, feeding it the manifest and prompt, to compile a single cohesive review packet.
    • Iteration Directory Isolation: Bundle the context and save the output to explicitly isolated directories (e.g., .history/review-iteration-1/) so that when the Red Team forces a rewrite, the baseline artifact is never destructively overwritten. 2.5. Interactively Determine CLI and Model (ask once during bootstrap): Before dispatching context bundles to CLI agents:
    • Interactively ask the user: "Which LLM CLI backend should be used for the adversarial review?" (Options: agy, claude, copilot, codex, llama).
    • Ask: "Which specific model should be used?" (Present defaults, e.g., Claude Opus 4.6 (Thinking) for high reasoning or Gemini 3.5 Flash (Low) for fast scans).
    • Ensure you append < /dev/null to the run command to prevent SIGTTIN hangs in headless execution engines.
  3. Dispatch to Reviewers — Send the bundle using the selected CLI and model to:
    • Human reviewers (paste-to-chat or browser)
    • CLI agents with adversarial personas (security auditor, devil's advocate)
    • Browser-based agents for interactive review
  4. Receive Feedback — Capture the red team's verdict:
    • "More Research Needed" → Loop back to step 1 with targeted questions
    • Asynchronous Benchmark Metric Capture: Explicitly log the total_tokens and duration_ms used by the adversarial agent during this specific iteration into an evals/timing.json file to calculate the true cost of approval. 4.5. Trust But Verify & TDD (Verification Gate): Do not blindly trust the reviewer's approval or feedback:
    • TDD Enforcement: Prioritize running unit and integration tests to ensure no regressions were introduced by any accepted recommendations.
    • Delta Inspection: Check modified files directly for stubs, stales, or placeholders.
    • Verify Critic Quality: Verify that the critic model's feedback is comprehensive and is not simply agreeing without actual critique.
  5. Completion & Handoff — Once the Red Team verdicts "Approved":
    • Terminate the review loop.
    • Pass the final, approved research and feedback documents back to the Orchestrator.
    • DO NOT attempt to seal the session or run a retrospective. The Orchestrator handles that.

Dependencies

  • context-bundler — Required for creating review packets
  • Adversarial personas: user-supplied system prompt, or from an installed CLI agent plugin (e.g., agent-personas). The personas/ directory is no longer bundled with agent-loops.

Diagram

See: red_team_review_loop.mmd

Keep looking

Skills are one crate of 328,083. 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.