# Mvilrokx Claude Skills Multi Agent Debate Review > Multi-Agent Debate Review - Skill: `tomevault-io/mvilrokx-claude-skills-multi-agent-debate-review` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/mvilrokx-claude-skills-multi-agent-debate-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/mvilrokx-claude-skills-multi-agent-debate-review/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/mvilrokx-claude-skills-multi-agent-debate-review --- # Multi-Agent Debate Review Run 6+ specialized agents in parallel against a codebase, compare findings, re-invoke agents on points of disagreement, and produce a consolidated report with prioritized actions. ## Mode Selection Ask the user which mode to use, or infer from context: - **Direct mode** (default): You coordinate — launch agents, collect results, run debates, write report. More interactive, allows user to discuss findings between phases. - **Delegated mode**: Launch a single `general-purpose` agent that coordinates the entire debate autonomously. Hands-off — user gets a final report. Use when user says "run it hands-off", "do it all automatically", or wants minimal interaction. ## Direct Mode ### Step 1: Load Standards Load relevant skills to give agents better baselines. Select based on project language: - **Go**: `coding-standards`, `golang-patterns`, `security-review`, `backend-patterns` - **Python**: `coding-standards`, `python-patterns`, `security-review` - **TypeScript/JS**: `coding-standards`, `frontend-patterns`, `security-review` ### Step 2: Launch Agents in Parallel Launch all agents with `mode: "background"`. Each agent gets a focused prompt with explicit instructions to provide severity ratings, file references, and suggested fixes. Select agents based on the project. See `references/agent-configs.md` for the full prompt templates for each agent type. - **Go projects**: go-reviewer, go-security-reviewer, go-refactor-cleaner, code-reviewer, database-reviewer, architect - **Python projects**: python-reviewer, python-security-reviewer, python-refactor-cleaner, code-reviewer, database-reviewer, architect - **JS/TS projects**: code-reviewer, js-security-reviewer, js-refactor-cleaner, database-reviewer, architect Always include `code-reviewer`, `database-reviewer`, and `architect` regardless of language. #### Model Selection Use the `model` parameter on each agent to assign different models. Two strategies: **Cost-optimized** (default): Match model capability to task complexity. | Role | Model | Rationale | | --------------------- | ------------------- | ---------------------------------------------- | | Security reviewer | `claude-opus-4.6` | Deep reasoning for exploit scenarios | | Architect | `claude-sonnet-4-5` | Good balance for system-level thinking | | Code reviewer | `claude-sonnet-4-5` | Standard code review | | Refactor/cleaner | `claude-haiku-4.5` | Mechanical analysis, fast/cheap | | Database reviewer | `claude-sonnet-4-5` | Schema analysis | | Debate re-invocations | `claude-opus-4.6` | Nuanced position changes need strong reasoning | **Diversity-optimized**: Use different model families to get genuinely different perspectives. Model diversity reduces shared blind spots — if Claude and GPT both flag the same issue independently, confidence is very high. If only one flags it, the debate round is more informative. | Role | Model | Rationale | | --------------------- | ---------------------- | ------------------------------ | | Code reviewer | `claude-sonnet-4-5` | Claude perspective | | Security reviewer | `gpt-5.2-codex` | GPT perspective | | Architect | `gemini-3-pro-preview` | Gemini perspective | | Database reviewer | `gpt-5.2` | GPT perspective | | Refactor/cleaner | `claude-haiku-4.5` | Fast/cheap for mechanical work | | Debate re-invocations | `claude-opus-4.6` | Strongest model resolves ties | Ask the user which strategy they prefer, or default to cost-optimized. ### Step 3: Track Findings After collecting results, insert all findings into a SQL table: ```sql CREATE TABLE findings ( id TEXT PRIMARY KEY, category TEXT NOT NULL, -- SECURITY, DATABASE, CODE, OBSERVABILITY, etc. severity TEXT NOT NULL, -- CRITICAL, HIGH, MEDIUM, LOW title TEXT NOT NULL, agents_agreeing TEXT NOT NULL, -- comma-separated agent names agents_disagreeing TEXT DEFAULT '', status TEXT DEFAULT 'open', consensus TEXT DEFAULT 'pending' -- UNANIMOUS, RESOLVED, SINGLE_AGENT, UNRESOLVED ); ``` Mark consensus: - **UNANIMOUS**: 3+ agents independently flagged the same issue - **SINGLE_AGENT**: Only one agent found it (may be novel or false positive) - **UNRESOLVED**: Agents actively disagree ### Step 4: Identify Debates Look for these patterns: - **Severity disagreement**: One agent says CRITICAL, another says it's fine - **Approach conflict**: One says "remove X", another says "keep X" - **Scope tension**: One says "fix now", another says "fix later" ### Step 5: Re-invoke Disagreeing Agents For each debate, re-invoke the agents with the opposing position stated explicitly. Prompt template: ``` You are participating in a multi-agent debate review. [Agent A] found: [their position with reasoning] [Agent B] found: [opposing position with reasoning] Review the actual code at [path]. Then give your FINAL POSITION with reasoning in 3-8 sentences. Acknowledge what the other agent got right. ``` Update the findings table with resolved positions. ### Step 6: Consolidated Report Structure the final report in this order: 1. **Executive Summary** — overall grade, agent scores table 2. **Unanimous Findings** — issues 3+ agents agree on (highest confidence) 3. **Resolved Debates** — disagreements settled through re-invocation 4. **Unresolved Trade-offs** — genuine tensions with no clear winner 5. **Unique Insights** — single-agent findings worth noting 6. **Prioritized Action Plan** — grouped by urgency with effort estimates 7. **Strengths** — what all agents praised (important for morale/context) Save the report to the session artifacts directory. ## Delegated Mode Launch a single `general-purpose` agent that acts as debate coordinator. This agent has full access to the `task` tool and can launch sub-agents itself. Use `agent_type: "general-purpose"` — this is the only agent type that can spawn sub-agents. Prompt the coordinator with the full workflow. See `references/delegated-coordinator-prompt.md` for the complete prompt template. Key points for the coordinator prompt: - Tell it which agent types to launch (based on project language) - Tell it to launch all review agents in parallel using `mode: "background"` - Tell it to collect all results via `read_agent` - Tell it to identify disagreements and re-invoke with opposing positions - Tell it to produce the consolidated report in the standard format - Give it the codebase path and any project-specific context The coordinator runs autonomously — it launches reviewers as sub-agents, collects findings, runs the debate rounds, and returns the final consolidated report. ### When to Prefer Delegated Mode - User wants a fully autonomous review with no interaction - Context window is getting full and you want to offload the work - Running as part of an automated pipeline (e.g., pre-push hook, CI trigger) ### Limitations of Delegated Mode - No interactive discussion between phases — user gets final report only - Coordinator context window is separate, so it won't have prior conversation history - Coordinator must be given all relevant context in its prompt (project language, paths, focus areas) --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/mvilrokx) — claim your Tome and manage your conversions.