Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add donnfelker/loop-skills/plugin install multi-llm-convergenceWrote 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/donnfelker/loop-skills/multi-llm-convergence)<a href="https://agentmods.dev/skills/donnfelker/loop-skills/multi-llm-convergence"><img src="https://agentmods.dev/badge/skills/donnfelker/loop-skills/multi-llm-convergence/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/donnfelker/loop-skills/multi-llm-convergence"><img src="https://agentmods.dev/badge/skills/donnfelker/loop-skills/multi-llm-convergence.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 | $0.00192 | $0.05042 |
| Opus 5 | $0.00096 | $0.02521 |
| Sonnet 5 | $0.00038 | $0.01008 |
| Haiku 4.5 | $0.00019 | $0.00504 |
Grade A, and why
multi-llm-convergence 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 9d 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.
> Why this matters: a reviewer asked to verify "does library X's function still exist" will `curl` GitHub, fail (no network → repeated `curl` exit 6), and go quiet — looking exactly like a hang. Local clones turn "fetch How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-LLM Convergence
You are the convergence driver. You own an artifact, and your job is to bounce it between two genuinely different LLM reviewers — round after round — until both independently bless it. You apply findings, you commit each round, and you stop only when there is real cross-model consensus (or a principled stall).
Announce at start: "I'm using the multi-llm-convergence skill — let me confirm the artifact and the bar, then I'll alternate two different reviewers until they agree."
Why this skill exists
A single reviewer — even a good one — has blind spots, and a single model has correlated blind spots: ask the same model twice and it tends to miss the same things twice. Genuine convergence comes from alternating different model families (here: a Codex/GPT reviewer and a Claude review subagent) and letting each catch what the other introduced or missed. This is not theater. In the session this skill was distilled from, the second reviewer caught a defect the first reviewer's fix introduced, and the third pass caught a defect the second pass's fix introduced. Each round's value came precisely from the reviewer being a different mind than the one that last touched the artifact.
Three things make or break this loop, and all three are baked into the steps below:
- Ground the reviewers in local source-of-truth. Treat the Codex reviewer as offline / network-unreliable (its sandbox runs read-only with approvals off, and may have no outbound network) — it will stall or hallucinate if it has to fetch the libraries/APIs your artifact depends on. Clone them locally first.
- Never let a reviewer go silent. A backgrounded reviewer can hang without ever firing a completion signal. A liveness watchdog detects the silence and recovers, instead of waiting forever.
- Stop on real consensus, not the first "looks good." Convergence means a full clean round from each model on the same artifact state — not one reviewer's approval.
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.
- 9d ago First seen · 181 lines · 192 tokens per session scan A 4fe90a4aefab
multi-llm-convergence is a skill published in the GitHub repository donnfelker/loop-skills (18 stars, last pushed 1mo ago), licensed MIT. It adds 192 tokens to every session and 5,042 once invoked, about $0.0010 per session on Opus 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 2026-08-30.
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