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.
npx skills add mvilrokx/claude-skills --skill multi-agent-debate-reviewgit clone --depth 1 https://github.com/mvilrokx/claude-skillsWrote 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.
[](https://agentmods.dev/skills/mvilrokx/claude-skills/multi-agent-debate-review)<a href="https://agentmods.dev/skills/mvilrokx/claude-skills/multi-agent-debate-review"><img src="https://agentmods.dev/badge/skills/mvilrokx/claude-skills/multi-agent-debate-review/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.
<a href="https://agentmods.dev/skills/mvilrokx/claude-skills/multi-agent-debate-review"><img src="https://agentmods.dev/badge/skills/mvilrokx/claude-skills/multi-agent-debate-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00104 | $0.01825 |
| Opus 5 | $0.00052 | $0.00912 |
| Sonnet 5 | $0.00021 | $0.00365 |
| Haiku 4.5 | $0.00010 | $0.00183 |
Grade A, and why
multi-agent-debate-review 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-purposeagent 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.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 195 lines · 104 tokens per session scan A cbeecf34be33
multi-agent-debate-review is a skill published in the GitHub repository mvilrokx/claude-skills (2 stars, last pushed 7mo ago), licensed MIT. It adds 104 tokens to every session and 1,825 once invoked, about $0.0005 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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