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 MadAppGang/magus --skill multi-model-validationgit clone --depth 1 https://github.com/MadAppGang/magusWrote 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/madappgang/magus/multi-model-validation)<a href="https://agentmods.dev/skills/madappgang/magus/multi-model-validation"><img src="https://agentmods.dev/badge/skills/madappgang/magus/multi-model-validation/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/madappgang/magus/multi-model-validation"><img src="https://agentmods.dev/badge/skills/madappgang/magus/multi-model-validation.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 | $0.00047 | $0.21345 |
| Opus 5.5 | $0.00019 | $0.08538 |
| Sonnet 5.5 | $0.00009 | $0.04269 |
| Haiku 4.5 | $0.00005 | $0.02134 |
Grade B, and why
multi-model-validation scanned grade B 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 20d 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
cat .claude/multimodel-team.json 2>/dev/null How it starts
The opening of the file, as written. The whole thing — 2,287 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Model Validation
Version: 3.3.0 Purpose: Patterns for running multiple AI models in parallel via Claudish proxy with context-aware preferences, dynamic model discovery, session-based workspaces, and performance statistics Status: Production Ready
Overview
Multi-model validation is the practice of running multiple AI models (Grok, Gemini, GPT, DeepSeek, etc.) in parallel to validate code, designs, or implementations from different perspectives. This achieves:
- 3-5x speedup via parallel execution (15 minutes → 5 minutes)
- Consensus-based prioritization (issues flagged by all models are CRITICAL)
- Diverse perspectives (different models catch different issues)
- Cost transparency (know before you spend)
- Free model discovery (NEW v3.0) - find high-quality free models from trusted providers
- Performance tracking - identify slow/failing models for future exclusion
- Data-driven recommendations - optimize model shortlist based on historical performance
Key Innovations:
- Context-Aware Preferences (NEW v3.3.0) - Automatically use saved model preferences per task type (debug/research/coding/review) from
.claude/multimodel-team.json - Dynamic Model Discovery (v3.0) - Read the live catalog (
list_models) for current available models (live, 24h cache) - Session-Based Workspaces (v3.0) - Each validation session gets a unique directory to prevent conflicts
- 4-Message Pattern - Ensures true parallel execution by using only Agent tool calls in a single message
- Pattern 7-8 - Statistics collection and data-driven model recommendations
This skill is extracted from the /review command and generalized for use in any multi-model workflow.
⚠️ MANDATORY: Learn and Reuse User Preferences
Model preferences are learned per context and reused automatically.
- First time a context is used → ASK user → SAVE to that context
- Next time same context → VALIDATE the saved IDs against
list_models, then use the survivors automatically (no asking). "No asking" applies to the selection, never to the catalog check — saved IDs go stale and must be re-checked every run.- User explicitly says "change models" or "different models" → ASK and UPDATE
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.
- 20d ago Changed · -12 lines 31c2620729a5
- 22d ago Changed · -152 lines · -2 tokens per session 35571f90df3f
- 1mo ago Changed 902747fcc9de
- 1mo ago First seen · 2,451 lines · 49 tokens per session scan B 3a92516bce18
multi-model-validation is a skill published in the GitHub repository MadAppGang/magus (13 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 21,345 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
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