Proxy Mode Reference

External Models via Claudish CLI — Reference Guide

⚠️ Learn and Reuse Model Preferences

Models are learned per context and reused automatically:

cat .claude/multimodel-team.json 2>/dev/null

1. Detect context from task keywords (debug/research/coding/review)
2. If contextPreferences[context] exists → USE IT (no asking)
3. If empty (first time) → ASK user → SAVE for that context
4. User says "change models" → UPDATE preferences

How External Models Work

External models are invoked deterministically via the claudish CLI. The orchestrator (e.g., /team command) calls claudish directly through Bash — no LLM delegation needed.

Orchestrator → Bash(claudish --model {MODEL_ID} --stdin) → External Model

This approach is 100% reliable because it's a direct CLI invocation, not a prompt-based delegation.

Invoking External Models

From /team Command (Automatic)

The /team command handles this automatically:

• Internal models → Task(dev:researcher)
• External models → Bash(claudish --model {MODEL_ID} --stdin)

Direct CLI Usage

# Pattern
claudish --model {MODEL_ID} --stdin --quiet < prompt-file.md > result.md

# Examples
claudish --model x-ai/grok-code-fast-1 --stdin --quiet < task.md > grok-result.md
claudish --model google/gemini-3-pro-preview --stdin --quiet < task.md > gemini-result.md

Required flags:

• --model — The external model to use
• --stdin — Read prompt from stdin (for large prompts)
• --quiet — Suppress log messages (for clean output capture)

Multi-Backend Routing

Claudish routes to different backends based on model ID prefix:

⚠️ Prefix Collision Warning

OpenRouter model IDs may collide with routing prefixes. Check the prefix table above.

Collision-free models (safe for OpenRouter):

• x-ai/grok-* ✅
• deepseek/* ✅
• minimax/* ✅ (use mmax/ for MiniMax Direct)
• qwen/* ✅
• mistralai/* ✅
• moonshotai/* ✅ (use kimi/ for Kimi Direct)
• anthropic/* ✅
• z-ai/* ✅ (use glm/ for GLM Direct)
• google/* ✅ (use g/ for Gemini Direct)
• openai/* ✅ (use oai/ for OpenAI Direct)

Direct API prefixes for cost savings:

Correct Usage Patterns

Single External Model

claudish --model x-ai/grok-code-fast-1 --stdin --quiet < task.md > result.md

Parallel External Models (in /team)

# All launched in a single message with run_in_background: true
Bash("claudish --model x-ai/grok-code-fast-1 --stdin --quiet < vote-prompt.md > grok-result.md 2>grok-stderr.log; echo $? > grok.exit")
Bash("claudish --model google/gemini-3-pro-preview --stdin --quiet < vote-prompt.md > gemini-result.md 2>gemini-stderr.log; echo $? > gemini.exit")

Verifying Results

# Check exit code
cat grok.exit  # 0 = success

# Check output size
wc -c < grok-result.md  # Should be >50 bytes

# Check stderr for errors
cat grok-stderr.log

Common Mistakes

Mistake 1: Not capturing exit code

# ❌ WRONG - no way to detect failures
claudish --model grok --stdin < task.md > result.md

# ✅ CORRECT - capture exit code
claudish --model grok --stdin < task.md > result.md 2>stderr.log; echo $? > result.exit

Troubleshooting

"claudish: command not found"

Fix: npm install -g claudish

"OPENROUTER_API_KEY not set"

Fix: export OPENROUTER_API_KEY=your-key

Non-zero exit code

Fix: Check stderr log for error details. Common causes: rate limits, invalid model ID, API key issues.

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