Task External Models

External Models: Quick Reference

⚠️ Learn and Reuse Model Preferences

Models are learned per context and reused automatically:

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

Flow:

1. Detect context from task keywords (debug/research/coding/review)
2. If contextPreferences[context] has models → USE THEM (no asking)
3. If empty (first time for context) → ASK user → SAVE to that context
4. User says "use different models" → ASK and UPDATE

Override triggers: "use different models", "change models", "update preferences"

The Simple Truth

External AI models are invoked via Bash+claudish CLI. This is deterministic and 100% reliable.

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

In /team orchestration:

• Internal model (Claude) → Task(subagent_type: "dev:researcher")
• External models (Grok, Gemini, etc.) → Bash(claudish --model {MODEL_ID} --stdin)

Bash + claudish Pattern

Works with ANY agent — deterministic, no LLM compliance needed.

# Pattern
claudish --model {MODEL_ID} --stdin --quiet < prompt.md > result.md 2>stderr.log; echo $? > result.exit

# Examples
claudish --model x-ai/grok-code-fast-1 --stdin --quiet < task.md > grok.md 2>grok-err.log; echo $? > grok.exit
claudish --model google/gemini-3-pro-preview --stdin --quiet < task.md > gemini.md 2>gemini-err.log; echo $? > gemini.exit
claudish --model openai/gpt-5.2-codex --stdin --quiet < task.md > gpt5.md 2>gpt5-err.log; echo $? > gpt5.exit

CLI Reference:

claudish [options]

--model <id>         AI model to use (e.g., x-ai/grok-code-fast-1)
--stdin              Read prompt from stdin
--quiet              Minimal output

Parallel Execution in /team:

All Bash calls are launched in a SINGLE message with run_in_background: true:

// Internal model via Task
Task({
  subagent_type: "dev:researcher",
  description: "Internal Claude vote",
  run_in_background: true,
  prompt: "{VOTE_PROMPT}\n\nWrite to: {SESSION_DIR}/internal-result.md"
})

// External models via Bash+claudish (all in same message)
Bash({
  command: "claudish --model x-ai/grok-code-fast-1 --stdin --quiet < {SESSION_DIR}/vote-prompt.md > {SESSION_DIR}/grok-result.md 2>{SESSION_DIR}/grok-stderr.log; echo $? > {SESSION_DIR}/grok.exit",
  run_in_background: true
})

Bash({
  command: "claudish --model google/gemini-3-pro-preview --stdin --quiet < {SESSION_DIR}/vote-prompt.md > {SESSION_DIR}/gemini-result.md 2>{SESSION_DIR}/gemini-stderr.log; echo $? > {SESSION_DIR}/gemini.exit",
  run_in_background: true
})

Common Mistakes

Model IDs

Note: Model IDs change frequently. Use claudish --top-models for current list.

# Get current available models
claudish --top-models    # Best value paid models
claudish --free          # Free models

# Example model IDs (verify with commands above)
x-ai/grok-code-fast-1       # Grok (fast coding)
minimax/minimax-m2.5        # MiniMax M2.5
google/gemini-3-pro-preview # Gemini Pro
openai/gpt-5.2-codex        # GPT-5.2 Codex
z-ai/glm-4.7                # GLM 4.7
deepseek/deepseek-v3.2      # DeepSeek v3.2

Prefix routing: Use direct API prefixes for cost savings: oai/ (OpenAI), g/ (Gemini), mmax/ (MiniMax), kimi/ (Kimi), glm/ (GLM).

Verifying Models Actually Ran

After collecting results from external models, always verify:

1. Check exit code: cat {model-slug}.exit → should be 0
2. Check output size: wc -c < {model-slug}-result.md → should be >50 bytes
3. Check stderr: cat {model-slug}-stderr.log → should be empty or just info
4. Record in verification table for /team results display

Verification checklist:

For each external model result:
  ☐ Exit code is 0
  ☐ Result file exists and has >50 bytes
  ☐ Response contains substantive analysis (not just acknowledgment)
  ☐ No error messages in stderr log

Related Skills

• multimodel:proxy-mode-reference - Complete claudish CLI documentation with routing prefixes
• multimodel:multi-model-validation - Full parallel validation patterns
• multimodel:model-tracking-protocol - Progress tracking during reviews
• multimodel:error-recovery - Handle failures and timeouts

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