red-team-review
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
- Research & Analyze — Deep-dive into the problem domain. Create analysis docs, capture sources.
- Review Packet Generation — Prepare the context for the reviewer:
- Create Prompt: Write or update a
red-team-prompt.mdexplaining exactly what is being reviewed and what the reviewer should focus on. - Define Manifest: Update a
manifest.jsonor equivalent list dictating which source files and research artifacts to include. - Bundle Context: Execute the
context-bundlerplugin, 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.
- Create Prompt: Write or update a
- Dispatch to Reviewers — Send the bundle to:
More from richfrem/agent-plugins-skills
markdown-to-msword-converter
Converts Markdown files to one MS Word document per file using plugin-local scripts. V2 includes L5 Delegated Constraint Verification for strict binary artifact linting.
52excel-to-csv
>
32zip-bundling
Create technical ZIP bundles of code, design, and documentation for external review or context sharing. Use when you need to package multiple project files into a portable `.zip` archive instead of a single Markdown file.
29learning-loop
(Industry standard: Loop Agent / Single Agent) Primary Use Case: Self-contained research, content generation, and exploration where no inner delegation is required. Self-directed research and knowledge capture loop. Use when: starting a session (Orientation), performing research (Synthesis), or closing a session (Seal, Persist, Retrospective). Ensures knowledge survives across isolated agent sessions.
26ollama-launch
Start and verify the local Ollama LLM server. Use when Ollama is needed for RLM distillation, seal snapshots, embeddings, or any local LLM inference — and it's not already running. Checks if Ollama is running, starts it if not, and verifies the health endpoint.
26create-skill
>
26