Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/DNYoussef/context-cascadenpx agentmods add skills/dnyoussef/context-cascade/ralph-multimodelWrote 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/dnyoussef/context-cascade/ralph-multimodel)<a href="https://agentmods.dev/skills/dnyoussef/context-cascade/ralph-multimodel"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/ralph-multimodel/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/dnyoussef/context-cascade/ralph-multimodel"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/ralph-multimodel.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 | $0.00030 | $0.01684 |
| Opus 5.5 | $0.00012 | $0.00674 |
| Sonnet 5.5 | $0.00006 | $0.00337 |
| Haiku 4.5 | $0.00003 | $0.00168 |
Grade A, and why
ralph-multimodel 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 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.
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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph Wiggum Multi-Model Persistence Loop
LIBRARY-FIRST PROTOCOL (MANDATORY)
Before writing ANY code, you MUST check:
Step 1: Library Catalog
- Location:
.claude/library/catalog.json - If match >70%: REUSE or ADAPT
Step 2: Patterns Guide
- Location:
.claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md - If pattern exists: FOLLOW documented approach
Step 3: Existing Projects
- Location:
D:\Projects\* - If found: EXTRACT and adapt
Decision Matrix
| Match | Action |
|---|---|
| Library >90% | REUSE directly |
| Library 70-90% | ADAPT minimally |
| Pattern exists | FOLLOW pattern |
| In project | EXTRACT |
| No match | BUILD (add to library after) |
Purpose
Extend the Ralph Wiggum persistence pattern with intelligent model routing:
- Gemini for research phases (search, megacontext, media)
- Codex for autonomous iteration (yolo, full-auto, sandbox)
- Claude for complex reasoning
- LLM Council for critical decisions
Unique Capability
What This Adds:
- Automatic model selection based on task phase
- Best-of-breed capabilities per iteration
- Fire-and-forget with optimal tool selection
- Multi-model consensus for critical decisions
When to Use
Perfect For:
- Complex tasks requiring multiple model strengths
- Overnight autonomous development
- Tasks mixing research + implementation + testing
- Critical decisions needing consensus
- Large codebase refactoring with validation
Don't Use When:
- Simple single-model tasks
- Time-critical (model switching adds latency)
- Need human oversight at each step
How It Works
ITERATION N:
|
+---> Detect Task Phase
| |
| +---> Research? --> Gemini (search/megacontext)
| +---> Media? --> Gemini (imagen/veo)
| +---> Iterate? --> Codex (yolo/full-auto)
| +---> Decide? --> LLM Council (consensus)
| +---> Reason? --> Claude (agents)
|
+---> Execute with Optimal Model
|
+---> Check Completion Promise
| |
| +---> Found? --> EXIT SUCCESS
| +---> Not found? --> CONTINUE
|
+---> ITERATION N+1 (until max)
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 First seen · 235 lines · 30 tokens per session scan A f8525d228d11
ralph-multimodel is a skill published in the GitHub repository DNYoussef/context-cascade (32 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 1,684 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
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