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 justinstimatze/gemot --skill deliberated-reviewgit clone --depth 1 https://github.com/justinstimatze/gemotWrote 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/justinstimatze/gemot/deliberated-review)<a href="https://agentmods.dev/skills/justinstimatze/gemot/deliberated-review"><img src="https://agentmods.dev/badge/skills/justinstimatze/gemot/deliberated-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/justinstimatze/gemot/deliberated-review"><img src="https://agentmods.dev/badge/skills/justinstimatze/gemot/deliberated-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 | $0.00032 | $0.01379 |
| Opus 5 | $0.00016 | $0.00690 |
| Sonnet 5 | $0.00006 | $0.00276 |
| Haiku 4.5 | $0.00003 | $0.00138 |
Grade A, and why
deliberated-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 10d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deliberated Code Review
When you need a thorough code review from multiple perspectives, delegate to specialist reviewers, cross-vote, and use gemot to find the crux of any disagreements.
When to Use
- PR review where security, performance, and correctness might conflict
- Architecture decisions where subagents return different recommendations
- Any multi-perspective review where you need to know WHY reviewers disagree, not just THAT they disagree
Process
Step 1: Delegate specialist reviews
Use delegate_task to spawn 3 independent reviewers. Each reviews the same code from their expertise:
delegate_task([
{"goal": "Review this code for security vulnerabilities. Cite specific lines, explain attack vectors, suggest fixes.", "context": "<the code>"},
{"goal": "Review this code for reliability and correctness. Cite specific lines, explain what breaks, suggest fixes.", "context": "<the code>"},
{"goal": "Review this code for performance issues. Cite specific lines, estimate impact, suggest fixes.", "context": "<the code>"}
])
Step 2: Create a gemot deliberation
Call mcp_gemot_deliberation with:
- action: "create"
- topic: a one-line summary of what's being reviewed (e.g., "Payment processing code review")
- template: "review"
- group_id: a consistent identifier for the project or workflow (e.g., "myproject-reviews")
Step 3: Submit each reviewer's findings
For each subagent result, call mcp_gemot_participate with:
- action: "submit_position"
- deliberation_id: from step 2
- agent_id: the reviewer role (e.g., "security-reviewer")
- content: the reviewer's full findings
Step 4: Cross-vote
Call mcp_gemot_participate with action: "get_positions" to fetch all positions. Then have each reviewer vote on the other reviewers' findings:
For each position, each OTHER reviewer calls mcp_gemot_participate with:
- action: "vote"
- deliberation_id: from step 2
- agent_id: the voting reviewer's ID
- position_id: the position being voted on
- value: 1 (agree with this finding), 0 (no opinion), or -1 (disagree)
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.
- 10d ago First seen · 111 lines · 32 tokens per session scan A c787503d7fc4
deliberated-review is a skill published in the GitHub repository justinstimatze/gemot (3 stars, last pushed 5d ago), licensed Apache-2.0. It adds 32 tokens to every session and 1,379 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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