claudish-usage

name

claudish-usage

description

CRITICAL - Guide for using Claudish CLI ONLY through sub-agents to run Claude Code with any AI model (OpenRouter, Gemini, OpenAI, local models). NEVER run Claudish directly in main context unless user explicitly requests it. Use when user mentions external AI models, Claudish, OpenRouter, Gemini, OpenAI, Ollama, or alternative models. Includes mandatory sub-agent delegation patterns, agent selection guide, file-based instructions, and strict rules to prevent context window pollution.

Claudish Usage Skill

Version: 2.0.0 Purpose: Guide AI agents on how to use Claudish CLI to run Claude Code with any AI model Status: Production Ready

⚠️ CRITICAL RULES - READ FIRST

🚫 NEVER Run Claudish from Main Context

Claudish MUST ONLY be run through sub-agents unless the user explicitly requests direct execution.

Why:

• Running Claudish directly pollutes main context with 10K+ tokens (full conversation + reasoning)
• Destroys context window efficiency
• Makes main conversation unmanageable

When you can run Claudish directly:

• ✅ User explicitly says "run claudish directly" or "don't use a sub-agent"
• ✅ User is debugging and wants to see full output
• ✅ User specifically requests main context execution

When you MUST use sub-agent:

• ✅ User says "use Grok to implement X" (delegate to sub-agent)
• ✅ User says "ask GPT-5.3 to review X" (delegate to sub-agent)
• ✅ User mentions any model name without "directly" (delegate to sub-agent)
• ✅ Any production task (always delegate)

📋 Workflow Decision Tree

User Request
    ↓
Does it mention Claudish/OpenRouter/model name? → NO → Don't use this skill
    ↓ YES
    ↓
Does user say "directly" or "in main context"? → YES → Run in main context (rare)
    ↓ NO
    ↓
Find appropriate agent or create one → Delegate to sub-agent (default)

🤖 Agent Selection Guide

Step 1: Find the Right Agent

When user requests Claudish task, follow this process:

1. Check for existing agents that support proxy mode or external model delegation
2. If no suitable agent exists:
   • Suggest creating a new proxy-mode agent for this task type
   • Offer to proceed with generic general-purpose agent if user declines
3. If user declines agent creation:
   • Warn about context pollution
   • Ask if they want to proceed anyway

Step 2: Agent Type Selection Matrix

Task Type	Recommended Agent	Fallback	Notes
Code implementation	Create coding agent with proxy mode	general-purpose	Best: custom agent for project-specific patterns
Code review	Use existing code review agent + proxy	general-purpose	Check if plugin has review agent first
Architecture planning	Use existing architect agent + proxy	general-purpose	Look for architect or planner agents
Testing	Use existing test agent + proxy	general-purpose	Look for test-architect or tester agents
Refactoring	Create refactoring agent with proxy	general-purpose	Complex refactors benefit from specialized agent
Documentation	general-purpose	-	Simple task, generic agent OK
Analysis	Use existing analysis agent + proxy	general-purpose	Check for analyzer or detective agents
Other	general-purpose	-	Default for unknown task types

Step 3: Agent Creation Offer (When No Agent Exists)

Template response:

I notice you want to use [Model Name] for [task type].

RECOMMENDATION: Create a specialized [task type] agent with proxy mode support.

This would:
✅ Provide better task-specific guidance
✅ Reusable for future [task type] tasks
✅ Optimized prompting for [Model Name]

Options:
1. Create specialized agent (recommended) - takes 2-3 minutes
2. Use generic general-purpose agent - works but less optimized
3. Run directly in main context (NOT recommended - pollutes context)

Which would you prefer?

Step 4: Common Agents by Plugin

Frontend Plugin:

• typescript-frontend-dev - Use for UI implementation with external models
• frontend-architect - Use for architecture planning with external models
• senior-code-reviewer - Use for code review (can delegate to external models)
• test-architect - Use for test planning/implementation

Bun Backend Plugin:

• backend-developer - Use for API implementation with external models
• api-architect - Use for API design with external models

Code Analysis Plugin:

• codebase-detective - Use for investigation tasks with external models

No Plugin:

• general-purpose - Default fallback for any task

Step 5: Example Agent Selection

Example 1: User says "use Grok to implement authentication"

Task: Code implementation (authentication)
Plugin: Bun Backend (if backend) or Frontend (if UI)

Decision:
1. Check for backend-developer or typescript-frontend-dev agent
2. Found backend-developer? → Use it with Grok proxy
3. Not found? → Offer to create custom auth agent
4. User declines? → Use general-purpose with file-based pattern

Example 2: User says "ask GPT-5.3 to review my API design"

Task: Code review (API design)
Plugin: Bun Backend

Decision:
1. Check for api-architect or senior-code-reviewer agent
2. Found? → Use it with GPT-5.3 proxy
3. Not found? → Use general-purpose with review instructions
4. Never run directly in main context

Example 3: User says "use Gemini to refactor this component"

Task: Refactoring (component)
Plugin: Frontend

Decision:
1. No specialized refactoring agent exists
2. Offer to create component-refactoring agent
3. User declines? → Use typescript-frontend-dev with proxy
4. Still no agent? → Use general-purpose with file-based pattern

Overview

Claudish is a CLI tool that allows running Claude Code with any AI model via prefix-based routing. Supports OpenRouter (100+ models), direct Google Gemini API, direct OpenAI API, and local models (Ollama, LM Studio, vLLM, MLX).

Key Principle: ALWAYS use Claudish through sub-agents with file-based instructions to avoid context window pollution.

What is Claudish?

Claudish (Claude-ish) is a proxy tool that:

• ✅ Runs Claude Code with any AI model via prefix-based routing
• ✅ Supports OpenRouter, Gemini, OpenAI, and local models
• ✅ Uses local API-compatible proxy server
• ✅ Supports 100% of Claude Code features
• ✅ Provides cost tracking and model selection
• ✅ Enables multi-model workflows

Model Routing

Prefix	Backend	Example
(none)	OpenRouter	openai/gpt-5.3
g/ gemini/	Google Gemini	g/gemini-2.0-flash
oai/ openai/	OpenAI	oai/gpt-4o
ollama/	Ollama	ollama/llama3.2
lmstudio/	LM Studio	lmstudio/model
http://...	Custom	http://localhost:8000/model

Use Cases:

• Run tasks with different AI models (Grok for speed, GPT-5.3 for reasoning, Gemini for large context)
• Use direct APIs for lower latency (Gemini, OpenAI)
• Use local models for free, private inference (Ollama, LM Studio)
• Compare model performance on same task
• Reduce costs with cheaper models for simple tasks

Requirements

System Requirements

• Claudish CLI - Install with: npm install -g claudish or bun install -g claudish
• Claude Code - Must be installed
• At least one API key (see below)

Environment Variables

# API Keys (at least one required)
export OPENROUTER_API_KEY='sk-or-v1-...'  # OpenRouter (100+ models)
export GEMINI_API_KEY='...'               # Direct Gemini API (g/ prefix)
export OPENAI_API_KEY='sk-...'            # Direct OpenAI API (oai/ prefix)

# Placeholder (required to prevent Claude Code dialog)
export ANTHROPIC_API_KEY='sk-ant-api03-placeholder'

# Custom endpoints (optional)
export GEMINI_BASE_URL='https://...'      # Custom Gemini endpoint
export OPENAI_BASE_URL='https://...'      # Custom OpenAI/Azure endpoint
export OLLAMA_BASE_URL='http://...'       # Custom Ollama server
export LMSTUDIO_BASE_URL='http://...'     # Custom LM Studio server

# Default model (optional)
export CLAUDISH_MODEL='openai/gpt-5.3'    # Default model

Get API Keys:

• OpenRouter: https://openrouter.ai/keys (free tier available)
• Gemini: https://aistudio.google.com/apikey
• OpenAI: https://platform.openai.com/api-keys
• Local models: No API key needed

Quick Start Guide

Step 1: Install Claudish

# With npm (works everywhere)
npm install -g claudish

# With Bun (faster)
bun install -g claudish

# Verify installation
claudish --version

Step 2: Get Available Models

# List ALL OpenRouter models grouped by provider
claudish --models

# Fuzzy search models by name, ID, or description
claudish --models gemini
claudish --models "grok code"

# Show top recommended programming models (curated list)
claudish --top-models

# JSON output for parsing
claudish --models --json
claudish --top-models --json

# Force update from OpenRouter API
claudish --models --force-update

Step 3: Run Claudish

Interactive Mode (default):

# Shows model selector, persistent session
claudish

Single-shot Mode:

# One task and exit (requires --model)
claudish --model x-ai/grok-code-fast-1 "implement user authentication"

With stdin for large prompts:

# Read prompt from stdin (useful for git diffs, code review)
git diff | claudish --stdin --model openai/gpt-5-codex "Review these changes"

Recommended Models

Top Models for Development (v3.1.1):

Model	Provider	Best For
openai/gpt-5.3	OpenAI	Default - Most advanced reasoning
minimax/minimax-m2.1	MiniMax	Budget-friendly, fast
z-ai/glm-4.7	Z.AI	Balanced performance
google/gemini-3-pro-preview	Google	1M context window
moonshotai/kimi-k2-thinking	MoonShot	Extended thinking
deepseek/deepseek-v3.2	DeepSeek	Code specialist
qwen/qwen3-vl-235b-a22b-thinking	Alibaba	Vision + reasoning

Direct API Options (lower latency):

Model	Backend	Best For
g/gemini-2.0-flash	Gemini	Fast tasks, large context
oai/gpt-4o	OpenAI	General purpose
ollama/llama3.2	Local	Free, private

Get Latest Models:

# List all models (auto-updates every 2 days)
claudish --models

# Search for specific models
claudish --models grok
claudish --models "gemini flash"

# Show curated top models
claudish --top-models

# Force immediate update
claudish --models --force-update

NEW: Direct Agent Selection (v2.1.0)

Use --agent flag to invoke agents directly without the file-based pattern:

# Use specific agent (prepends @agent- automatically)
claudish --model x-ai/grok-code-fast-1 --agent frontend:developer "implement React component"

# Claude receives: "Use the @agent-frontend:developer agent to: implement React component"

# List available agents in project
claudish --list-agents

When to use --agent vs file-based pattern:

Use --agent when:

• Single, simple task that needs agent specialization
• Direct conversation with one agent
• Testing agent behavior
• CLI convenience

Use file-based pattern when:

• Complex multi-step workflows
• Multiple agents needed
• Large codebases
• Production tasks requiring review
• Need isolation from main conversation

Example comparisons:

Simple task (use --agent):

claudish --model x-ai/grok-code-fast-1 --agent frontend:developer "create button component"

Complex task (use file-based):

// multi-phase-workflow.md
Phase 1: Use api-architect to design API
Phase 2: Use backend-developer to implement
Phase 3: Use test-architect to add tests
Phase 4: Use senior-code-reviewer to review

then:
claudish --model x-ai/grok-code-fast-1 --stdin < multi-phase-workflow.md

Best Practice: File-Based Sub-Agent Pattern

⚠️ CRITICAL: Don't Run Claudish Directly from Main Conversation

Why: Running Claudish directly in main conversation pollutes context window with:

• Entire conversation transcript
• All tool outputs
• Model reasoning (can be 10K+ tokens)

Solution: Use file-based sub-agent pattern

File-Based Pattern (Recommended)

Step 1: Create instruction file

# /tmp/claudish-task-{timestamp}.md

## Task
Implement user authentication with JWT tokens

## Requirements
- Use bcrypt for password hashing
- Generate JWT with 24h expiration
- Add middleware for protected routes

## Deliverables
Write implementation to: /tmp/claudish-result-{timestamp}.md

## Output Format
```markdown
## Implementation

[code here]

## Files Created/Modified
- path/to/file1.ts
- path/to/file2.ts

## Tests
[test code if applicable]

## Notes
[any important notes]

**Step 2: Run Claudish with file instruction**
```bash
# Read instruction from file, write result to file
claudish --model x-ai/grok-code-fast-1 --stdin < /tmp/claudish-task-{timestamp}.md > /tmp/claudish-result-{timestamp}.md

Step 3: Read result file and provide summary

// In your agent/command:
const result = await Read({ file_path: "/tmp/claudish-result-{timestamp}.md" });

// Parse result
const filesModified = extractFilesModified(result);
const summary = extractSummary(result);

// Provide short feedback to main agent
return `✅ Task completed. Modified ${filesModified.length} files. ${summary}`;

Complete Example: Using Claudish in Sub-Agent

/**
 * Example: Run code review with Grok via Claudish sub-agent
 */
async function runCodeReviewWithGrok(files: string[]) {
  const timestamp = Date.now();
  const instructionFile = `/tmp/claudish-review-instruction-${timestamp}.md`;
  const resultFile = `/tmp/claudish-review-result-${timestamp}.md`;

  // Step 1: Create instruction file
  const instruction = `# Code Review Task

## Files to Review
${files.map(f => `- ${f}`).join('\n')}

## Review Criteria
- Code quality and maintainability
- Potential bugs or issues
- Performance considerations
- Security vulnerabilities

## Output Format
Write your review to: ${resultFile}

Use this format:
\`\`\`markdown
## Summary
[Brief overview]

## Issues Found
### Critical
- [issue 1]

### Medium
- [issue 2]

### Low
- [issue 3]

## Recommendations
- [recommendation 1]

## Files Reviewed
- [file 1]: [status]
\`\`\`
`;

  await Write({ file_path: instructionFile, content: instruction });

  // Step 2: Run Claudish with stdin
  await Bash(`claudish --model x-ai/grok-code-fast-1 --stdin < ${instructionFile}`);

  // Step 3: Read result
  const result = await Read({ file_path: resultFile });

  // Step 4: Parse and return summary
  const summary = extractSummary(result);
   issueCount = (result);

  
   ();

  
   {
    : ,
    summary,
    issueCount,
    : result  
  };
}

 ():  {
   match = review.(s);
   match ? match[].() : ;
}

 (): { : ; : ; :  } {
   critical = (review.(s)?.[].() || []).;
   medium = (review.(s)?.[].() || []).;
   low = (review.(s)?.[].() || []).;

   { critical, medium, low };
}

Sub-Agent Delegation Pattern

When running Claudish from an agent, use the Task tool to create a sub-agent:

Pattern 1: Simple Task Delegation

/**
 * Example: Delegate implementation to Grok via Claudish
 */
async function implementFeatureWithGrok(featureDescription: string) {
  // Use Task tool to create sub-agent
  const result = await Task({
    subagent_type: "general-purpose",
    description: "Implement feature with Grok",
    prompt: `
Use Claudish CLI to implement this feature with Grok model:

${featureDescription}

INSTRUCTIONS:
1. Search for available models:
   claudish --models grok

2. Run implementation with Grok:
   claudish --model x-ai/grok-code-fast-1 "${featureDescription}"

3. Return ONLY:
   - List of files created/modified
   - Brief summary (2-3 sentences)
   - Any errors encountered

DO NOT return the full conversation transcript or implementation details.
Keep your response under 500 tokens.
    `
  });

  return result;
}

Pattern 2: File-Based Task Delegation

/**
 * Example: Use file-based instruction pattern in sub-agent
 */
async function analyzeCodeWithGemini(codebasePath: string) {
  const timestamp = Date.now();
  const instructionFile = `/tmp/claudish-analyze-${timestamp}.md`;
  const resultFile = `/tmp/claudish-analyze-result-${timestamp}.md`;

  // Create instruction file
  const instruction = `# Codebase Analysis Task

## Codebase Path
${codebasePath}

## Analysis Required
- Architecture overview
- Key patterns used
- Potential improvements
- Security considerations

## Output
Write analysis to: ${resultFile}

Keep analysis concise (under 1000 words).
`;

  await Write({ file_path: instructionFile, content: instruction });

  // Delegate to sub-agent
  const result = await Task({
    subagent_type: "general-purpose",
    description: "Analyze codebase with Gemini",
    prompt: `
Use Claudish to analyze codebase with Gemini model.

Instruction file: ${instructionFile}
Result file: ${resultFile}

STEPS:
1. Read instruction file: ${instructionFile}
2. Run: claudish --model google/gemini-2.5-flash --stdin < ${instructionFile}
3. Wait for completion
4. Read result file: ${resultFile}
5. Return ONLY a 2-3 sentence summary

DO NOT include the full analysis in your response.
The full analysis is in  if needed.
    `
  });

  
   fullAnalysis =  ({ : resultFile });

  
   ();

   {
    : result,
    fullAnalysis
  };
}

Pattern 3: Multi-Model Comparison

/**
 * Example: Run same task with multiple models and compare
 */
async function compareModels(task: string, models: string[]) {
  const results = [];

  for (const model of models) {
    const timestamp = Date.now();
    const resultFile = `/tmp/claudish-${model.replace('/', '-')}-${timestamp}.md`;

    // Run task with each model
    await Task({
      subagent_type: "general-purpose",
      description: `Run task with ${model}`,
      prompt: `
Use Claudish to run this task with ${model}:

${task}

STEPS:
1. Run: claudish --model ${model} --json "${task}"
2. Parse JSON output
3. Return ONLY:
   - Cost (from total_cost_usd)
   - Duration (from duration_ms)
   - Token usage (from usage.input_tokens and usage.output_tokens)
   - Brief quality assessment (1-2 sentences)

DO NOT return full output.
      `
    });

    results.push({
      model,
      resultFile
    });
  }

  return results;
}

Common Workflows

Workflow 1: Quick Code Generation with Grok

# Fast, agentic coding with visible reasoning
claudish --model x-ai/grok-code-fast-1 "add error handling to api routes"

Workflow 2: Complex Refactoring with GPT-5.3

# Advanced reasoning for complex tasks
claudish --model openai/gpt-5 "refactor authentication system to use OAuth2"

Workflow 3: UI Implementation with Qwen (Vision)

# Vision-language model for UI tasks
claudish --model qwen/qwen3-vl-235b-a22b-instruct "implement dashboard from figma design"

Workflow 4: Code Review with Gemini

# State-of-the-art reasoning for thorough review
git diff | claudish --stdin --model google/gemini-2.5-flash "Review these changes for bugs and improvements"

Workflow 5: Multi-Model Consensus

# Run same task with multiple models
for model in "x-ai/grok-code-fast-1" "google/gemini-2.5-flash" "openai/gpt-5"; do
  echo "=== Testing with $model ==="
  claudish --model "$model" "find security vulnerabilities in auth.ts"
done

Claudish CLI Flags Reference

Essential Flags

Flag	Description	Example
--model <model>	OpenRouter model to use	--model x-ai/grok-code-fast-1
--stdin	Read prompt from stdin	git diff | claudish --stdin --model grok
--models	List all models or search	claudish --models or claudish --models gemini
--top-models	Show top recommended models	claudish --top-models
--json	JSON output (implies --quiet)	claudish --json "task"
--help-ai	Print AI agent usage guide	claudish --help-ai

Advanced Flags

Flag	Description	Default
--interactive / -i	Interactive mode	Auto (no prompt = interactive)
--quiet / -q	Suppress log messages	Quiet in single-shot
--verbose / -v	Show log messages	Verbose in interactive
--debug / -d	Enable debug logging to file	Disabled
--port <port>	Proxy server port	Random (3000-9000)
--no-auto-approve	Require permission prompts	Auto-approve enabled
--dangerous	Disable sandbox	Disabled
--monitor	Proxy to real Anthropic API (debug)	Disabled
--force-update	Force refresh model cache	Auto (>2 days)

Output Modes

1. Quiet Mode (default in single-shot)

   claudish --model grok "task"
   # Clean output, no [claudish] logs
   

2. Verbose Mode

   claudish --verbose "task"
   # Shows all [claudish] logs for debugging
   

3. JSON Mode

   claudish --json "task"
   # Structured output: {result, cost, usage, duration}
   

Cost Tracking

Claudish automatically tracks costs in the status line:

directory • model-id • $cost • ctx%

Example:

my-project • x-ai/grok-code-fast-1 • $0.12 • 67%

Shows:

• 💰 Cost: $0.12 USD spent in current session
• 📊 Context: 67% of context window remaining

JSON Output Cost:

claudish --json "task" | jq '.total_cost_usd'
# Output: 0.068

Error Handling

Error 1: OPENROUTER_API_KEY Not Set

Error:

Error: OPENROUTER_API_KEY environment variable is required

Fix:

export OPENROUTER_API_KEY='sk-or-v1-...'
# Or add to ~/.zshrc or ~/.bashrc

Error 2: Claudish Not Installed

Error:

command not found: claudish

Fix:

npm install -g claudish
# Or: bun install -g claudish

Error 3: Model Not Found

Error:

Model 'invalid/model' not found

Fix:

# List available models
claudish --models

# Use valid model ID
claudish --model x-ai/grok-code-fast-1 "task"

Error 4: OpenRouter API Error

Error:

OpenRouter API error: 401 Unauthorized

Fix:

1. Check API key is correct
2. Verify API key at https://openrouter.ai/keys
3. Check API key has credits (free tier or paid)

Error 5: Port Already in Use

Error:

Error: Port 3000 already in use

Fix:

# Let Claudish pick random port (default)
claudish --model grok "task"

# Or specify different port
claudish --port 8080 --model grok "task"

Best Practices

1. ✅ Use File-Based Instructions

Why: Avoids context window pollution

How:

# Write instruction to file
echo "Implement feature X" > /tmp/task.md

# Run with stdin
claudish --stdin --model grok < /tmp/task.md > /tmp/result.md

# Read result
cat /tmp/result.md

2. ✅ Choose Right Model for Task

Fast Coding: x-ai/grok-code-fast-1 Complex Reasoning: google/gemini-2.5-flash or openai/gpt-5 Vision/UI: qwen/qwen3-vl-235b-a22b-instruct

3. ✅ Use --json for Automation

Why: Structured output, easier parsing

How:

RESULT=$(claudish --json "task" | jq -r '.result')
COST=$(claudish --json "task" | jq -r '.total_cost_usd')

4. ✅ Delegate to Sub-Agents

Why: Keeps main conversation context clean

How:

await Task({
  subagent_type: "general-purpose",
  description: "Task with Claudish",
  prompt: "Use claudish --model grok '...' and return summary only"
});

5. ✅ Update Models Regularly

Why: Get latest model recommendations

How:

# Auto-updates every 2 days
claudish --models

# Search for specific models
claudish --models deepseek

# Force update now
claudish --models --force-update

6. ✅ Use --stdin for Large Prompts

Why: Avoid command line length limits

How:

git diff | claudish --stdin --model grok "Review changes"

Anti-Patterns (Avoid These)

❌❌❌ NEVER Run Claudish Directly in Main Conversation (CRITICAL)

This is the #1 mistake. Never do this unless user explicitly requests it.

WRONG - Destroys context window:

// ❌ NEVER DO THIS - Pollutes main context with 10K+ tokens
await Bash("claudish --model grok 'implement feature'");

// ❌ NEVER DO THIS - Full conversation in main context
await Bash("claudish --model gemini 'review code'");

// ❌ NEVER DO THIS - Even with --json, output is huge
const result = await Bash("claudish --json --model gpt-5 'refactor'");

RIGHT - Always use sub-agents:

// ✅ ALWAYS DO THIS - Delegate to sub-agent
const result = await Task({
  subagent_type: "general-purpose", // or specific agent
  description: "Implement feature with Grok",
  prompt: `
Use Claudish to implement the feature with Grok model.

CRITICAL INSTRUCTIONS:
1. Create instruction file: /tmp/claudish-task-${Date.now()}.md
2. Write detailed task requirements to file
3. Run: claudish --model x-ai/grok-code-fast-1 --stdin < /tmp/claudish-task-*.md
4. Read result file and return ONLY a 2-3 sentence summary

DO NOT return full implementation or conversation.
Keep response under 300 tokens.
  `
});

// ✅ Even better - Use specialized agent if available
const result = await Task({
  subagent_type: "backend-developer", // or frontend-dev, etc.
  description: "Implement with external model",
  prompt: `
Use Claudish with x-ai/grok-code-fast-1 model to implement authentication.
Follow file-based instruction pattern.
Return summary only.
  `
});

When you CAN run directly (rare exceptions):

// ✅ Only when user explicitly requests
// User: "Run claudish directly in main context for debugging"
if (userExplicitlyRequestedDirect) {
  await Bash("claudish --model grok 'task'");
}

❌ Don't Ignore Model Selection

Wrong:

# Always using default model
claudish "any task"

Right:

# Choose appropriate model
claudish --model x-ai/grok-code-fast-1 "quick fix"
claudish --model google/gemini-2.5-flash "complex analysis"

❌ Don't Parse Text Output

Wrong:

OUTPUT=$(claudish --model grok "task")
COST=$(echo "$OUTPUT" | grep cost | awk '{print $2}')

Right:

# Use JSON output
COST=$(claudish --json --model grok "task" | jq -r '.total_cost_usd')

❌ Don't Hardcode Model Lists

Wrong:

const MODELS = ["x-ai/grok-code-fast-1", "openai/gpt-5"];

Right:

// Query dynamically
const { stdout } = await Bash("claudish --models --json");
const models = JSON.parse(stdout).models.map(m => m.id);

✅ Do Accept Custom Models From Users

Problem: User provides a custom model ID that's not in --top-models

Wrong (rejecting custom models):

const availableModels = ["x-ai/grok-code-fast-1", "openai/gpt-5"];
const userModel = "custom/provider/model-123";

if (!availableModels.includes(userModel)) {
  throw new Error("Model not in my shortlist"); // ❌ DON'T DO THIS
}

Right (accept any valid model ID):

// Claudish accepts ANY valid OpenRouter model ID, even if not in --top-models
const userModel = "custom/provider/model-123";

// Validate it's a non-empty string with provider format
if (!userModel.includes("/")) {
  console.warn("Model should be in format: provider/model-name");
}

// Use it directly - Claudish will validate with OpenRouter
await Bash(`claudish --model ${userModel} "task"`);

Why: Users may have access to:

• Beta/experimental models
• Private/custom fine-tuned models
• Newly released models not yet in rankings
• Regional/enterprise models
• Cost-saving alternatives

Always accept user-provided model IDs unless they're clearly invalid (empty, wrong format).

✅ Do Handle User-Preferred Models

Scenario: User says "use my custom model X" and expects it to be remembered

Solution 1: Environment Variable (Recommended)

// Set for the session
process.env.CLAUDISH_MODEL = userPreferredModel;

// Or set permanently in user's shell profile
await Bash(`echo 'export CLAUDISH_MODEL="${userPreferredModel}"' >> ~/.zshrc`);

Solution 2: Session Cache

// Store in a temporary session file
const sessionFile = "/tmp/claudish-user-preferences.json";
const prefs = {
  preferredModel: userPreferredModel,
  lastUsed: new Date().toISOString()
};
await Write({ file_path: sessionFile, content: JSON.stringify(prefs, null, 2) });

// Load in subsequent commands
const { stdout } = await Read({ file_path: sessionFile });
const prefs = JSON.parse(stdout);
const model = prefs.preferredModel || defaultModel;

Solution 3: Prompt Once, Remember for Session

// In a multi-step workflow, ask once
if (!process.env.CLAUDISH_MODEL) {
  const { stdout } = await Bash("claudish --models --json");
  const models = JSON.parse(stdout).models;

  const response = await AskUserQuestion({
    question: "Select model (or enter custom model ID):",
    options: models.map((m, i) => ({ label: m.name, value: m.id })).concat([
      { label: "Enter custom model...", value: "custom" }
    ])
  });

  if (response === "custom") {
    const customModel = await AskUserQuestion({
      question: "Enter OpenRouter model ID (format: provider/model):"
    });
    process.env.CLAUDISH_MODEL = customModel;
  } else {
    process.env.CLAUDISH_MODEL = response;
  }
}

// Use the selected model for all subsequent calls
const model = process.env.CLAUDISH_MODEL;
await Bash(`claudish --model  "task 1"`);
 ();

Guidance for Agents:

1. ✅ Accept any model ID user provides (unless obviously malformed)
2. ✅ Don't filter based on your "shortlist" - let Claudish handle validation
3. ✅ Offer to set CLAUDISH_MODEL environment variable for session persistence
4. ✅ Explain that --top-models shows curated recommendations, --models shows all
5. ✅ Validate format (should contain "/") but not restrict to known models
6. ❌ Never reject a user's custom model with "not in my shortlist"

❌ Don't Skip Error Handling

Wrong:

const result = await Bash("claudish --model grok 'task'");

Right:

try {
  const result = await Bash("claudish --model grok 'task'");
} catch (error) {
  console.error("Claudish failed:", error.message);
  // Fallback to embedded Claude or handle error
}

Agent Integration Examples

Example 1: Code Review Agent

/**
 * Agent: code-reviewer (using Claudish with multiple models)
 */
async function reviewCodeWithMultipleModels(files: string[]) {
  const models = [
    "x-ai/grok-code-fast-1",      // Fast initial scan
    "google/gemini-2.5-flash",    // Deep analysis
    "openai/gpt-5"                // Final validation
  ];

  const reviews = [];

  for (const model of models) {
    const timestamp = Date.now();
    const instructionFile = `/tmp/review-${model.replace('/', '-')}-${timestamp}.md`;
    const resultFile = `/tmp/review-result-${model.replace('/', '-')}-${timestamp}.md`;

    // Create instruction
    const instruction = createReviewInstruction(files, resultFile);
    await Write({ file_path: instructionFile, content: instruction });

    // Run review with model
    await Bash(`claudish --model ${model} --stdin < ${instructionFile}`);

    // Read result
    const result =  ({ : resultFile });

    
    reviews.({
      model,
      : (result),
      : (result)
    });

    
     ();
  }

   reviews;
}

Example 2: Feature Implementation Command

/**
 * Command: /implement-with-model
 * Usage: /implement-with-model "feature description"
 */
async function implementWithModel(featureDescription: string) {
  // Step 1: Get available models
  const { stdout } = await Bash("claudish --models --json");
  const models = JSON.parse(stdout).models;

  // Step 2: Let user select model
  const selectedModel = await promptUserForModel(models);

  // Step 3: Create instruction file
  const timestamp = Date.now();
  const instructionFile = `/tmp/implement-${timestamp}.md`;
  const resultFile = `/tmp/implement-result-${timestamp}.md`;

  const instruction = `# Feature Implementation

## Description
${featureDescription}

## Requirements
- Write clean, maintainable code
- Add comprehensive tests
- Include error handling
- Follow project conventions

## Output
Write implementation details to: ${resultFile}

Include:
- Files created/modified
- Code snippets
- Test coverage
- Documentation updates
`;

  await Write({ file_path: instructionFile, content: instruction });

  // Step 4: Run implementation
  await Bash(`claudish --model ${selectedModel} --stdin < `);

  
   result =  ({ : resultFile });

  
   ();

   result;
}

Troubleshooting

Issue: Slow Performance

Symptoms: Claudish takes long time to respond

Solutions:

1. Use faster model: x-ai/grok-code-fast-1 or minimax/minimax-m2
2. Reduce prompt size (use --stdin with concise instructions)
3. Check internet connection to OpenRouter

Issue: High Costs

Symptoms: Unexpected API costs

Solutions:

1. Use budget-friendly models (check pricing with --models or --top-models)
2. Enable cost tracking: --cost-tracker
3. Use --json to monitor costs: claudish --json "task" | jq '.total_cost_usd'

Issue: Context Window Exceeded

Symptoms: Error about token limits

Solutions:

1. Use model with larger context (Gemini: 1000K, Grok: 256K)
2. Break task into smaller subtasks
3. Use file-based pattern to avoid conversation history

Issue: Model Not Available

Symptoms: "Model not found" error

Solutions:

1. Update model cache: claudish --models --force-update
2. Check OpenRouter website for model availability
3. Use alternative model from same category

Additional Resources

Documentation:

• Full README: mcp/claudish/README.md (in repository root)
• AI Agent Guide: Print with claudish --help-ai
• Model Integration: skills/claudish-integration/SKILL.md (in repository root)

External Links:

• Claudish GitHub: https://github.com/MadAppGang/claude-code
• OpenRouter: https://openrouter.ai
• OpenRouter Models: https://openrouter.ai/models
• OpenRouter API Docs: https://openrouter.ai/docs

Version Information:

claudish --version

Get Help:

claudish --help        # CLI usage
claudish --help-ai     # AI agent usage guide

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Maintained by: MadAppGang Last Updated: January 5, 2026 Skill Version: 2.0.0