task-external-models
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".
Best use case
task-external-models is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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".
Teams using task-external-models should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/task-external-models/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How task-external-models Compares
| Feature / Agent | task-external-models | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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
| Mistake | Why It Fails | Fix |
|---------|--------------|-----|
| Missing `--stdin` flag | claudish expects prompt as argument, truncated for large prompts | Use `--stdin` with `< prompt-file.md` |
| Not capturing exit code | No way to detect failures | Add `; echo $? > result.exit` |
| Not capturing stderr | Error details lost | Add `2>stderr.log` |
| `$(cat file.md)` in Task prompt | Shell expansion doesn't work in JSON string parameters | Read 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
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