madappgang/claude-code/task-external-models

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Quick-reference for using external AI models in orchestration workflows. External models are invoked via Bash+claudish CLI (deterministic, 100% reliable). Use when confused about how to run external models, "claudish with Bash", "external model in /team", or "how to specify external model". Trigger keywords - "external model", "claudish", "Bash claudish", "external LLM", "model parameter".

Install

npx vskill@latest install madappgang/claude-code/task-external-models

SKILL.md

External Models: Quick Reference

⚠️ Learn and Reuse Model Preferences

Models are learned per context and reused automatically:

bash
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.

bash
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.

bash
# 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:

javascript
// 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

MistakeWhy It FailsFix
Missing --stdin flagclaudish expects prompt as argument, truncated for large promptsUse --stdin with < prompt-file.md
Not capturing exit codeNo way to detect failuresAdd ; echo $? > result.exit
Not capturing stderrError details lostAdd 2>stderr.log
$(cat file.md) in Task promptShell expansion doesn't work in JSON string parametersRead file content first, then include in prompt

Model IDs

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

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewPassed

See the full security report

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Source

Popularity

Repository stars
270
Repository forks
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7-day trend score
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Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 6 May 2026, 5 months ago.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

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