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 dryvist/claude-code-plugins --skill multi-model-reviewgit clone --depth 1 https://github.com/dryvist/claude-code-pluginsWrote 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/dryvist/claude-code-plugins/multi-model-review)<a href="https://agentmods.dev/skills/dryvist/claude-code-plugins/multi-model-review"><img src="https://agentmods.dev/badge/skills/dryvist/claude-code-plugins/multi-model-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/dryvist/claude-code-plugins/multi-model-review"><img src="https://agentmods.dev/badge/skills/dryvist/claude-code-plugins/multi-model-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 | $0.00055 | $0.00949 |
| Opus 5.5 | $0.00022 | $0.00380 |
| Sonnet 5.5 | $0.00011 | $0.00190 |
| Haiku 4.5 | $0.00006 | $0.00095 |
Grade A, and why
multi-model-review scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf http://localhost:<port>/v1/models | jq -r '.data[].id' How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-model review
Independent model families tend to converge on the same top issues — that agreement is the signal worth the fan-out. Three invocation shapes cover most setups: a cloud coding-agent CLI (sandboxed, repo-aware), a cloud reasoning-agent CLI (general-purpose, also repo-aware), and a local model server (OpenAI-compatible HTTP endpoint). Each has its own gotcha; skipping past it wastes the whole review.
Cloud coding-agent CLI (e.g. an MCP-integrated coding agent)
- Run it read-only: sandbox/approval flags set so it can inspect the repo but never edit it. Point it at the plan or diff path and let it read the repo itself rather than pasting the diff inline.
- Account-pinned sessions reject explicit model overrides. A session authenticated against a subscription account (rather than a raw API key) will error on a hardcoded model id with something like "model X is not supported when using <tool> with a <provider> account." Omit the model parameter and let the session use its account default; only pass an explicit override on a session backed by a raw API key.
Cloud reasoning-agent CLI (e.g. a general-purpose agent CLI)
- Use the CLI's non-interactive single-prompt mode with explicit directory grants for whatever it needs to read — don't rely on default working-dir scope.
- It should be read-only by default (no auto-apply flag set); that's what makes it safe to fan a review out to without babysitting it.
- Use the strongest reasoning tier, not the cheapest. A fast/economy tier will run, but on a factual review of current tooling it can confidently hallucinate — e.g. claiming a tool "doesn't exist" or "was deprecated" because its knowledge predates the tool's current state. Pick the provider's top reasoning tier for anything that requires up-to-date factual grounding, not just code-quality opinion.
Local model server (OpenAI-compatible)
- Check the server is actually up before assuming a review failed. If nothing answers the local port, the inference service is stopped — start it via its own service manager (systemd unit, launchd job, container), then poll the models endpoint until it reports loaded:
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.
- 1mo ago Changed · -36 tokens per session efba4a6e0d5b
- 1mo ago Changed 5932431d349f
- 1mo ago First seen · 81 lines · 91 tokens per session scan A 57cbe0d7ed55
multi-model-review is a skill published in the GitHub repository dryvist/claude-code-plugins (3 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 949 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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