Claudish Usage

Claudish Usage Skill

Version: 1.1.0 Purpose: Guide AI agents on how to use Claudish CLI to run Claude Code with OpenRouter models 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 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

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 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 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 OpenRouter model (Grok, GPT-5, MiniMax, Gemini, etc.) by proxying requests through a local Anthropic API-compatible server.

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 OpenRouter model (not just Anthropic models)
• ✅ Uses local API-compatible proxy server
• ✅ Supports 100% of Claude Code features
• ✅ Provides cost tracking and model selection
• ✅ Enables multi-model workflows

Use Cases:

• Run tasks with different AI models (Grok for speed, GPT-5 for reasoning, Gemini for vision)
• Compare model performance on same task
• Reduce costs with cheaper models for simple tasks
• Access models with specialized capabilities

Requirements

System Requirements

• OpenRouter API Key - Required (set as OPENROUTER_API_KEY environment variable)
• Claudish CLI - Install with: npm install -g claudish or bun install -g claudish
• Claude Code - Must be installed

Environment Variables

# Required
export OPENROUTER_API_KEY='sk-or-v1-...'  # Your OpenRouter API key

# Optional (but recommended)
export ANTHROPIC_API_KEY='sk-ant-api03-placeholder'  # Prevents Claude Code dialog

# Optional - default model
export CLAUDISH_MODEL='x-ai/grok-code-fast-1'  # or ANTHROPIC_MODEL

Get OpenRouter API Key:

1. Visit https://openrouter.ai/keys
2. Sign up (free tier available)
3. Create API key
4. Set as environment variable

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 (verified from OpenRouter):

1. x-ai/grok-code-fast-1 - xAI's Grok (fast coding, visible reasoning)

   • Category: coding
   • Context: 256K
   • Best for: Quick iterations, agentic coding

2. google/gemini-2.5-flash - Google's Gemini (state-of-the-art reasoning)

   • Category: reasoning
   • Context: 1000K
   • Best for: Complex analysis, multi-step reasoning

3. minimax/minimax-m2 - MiniMax M2 (high performance)

   • Category: coding
   • Context: 128K
   • Best for: General coding tasks

4. openai/gpt-5 - OpenAI's GPT-5 (advanced reasoning)

   • Category: reasoning
   • Context: 128K
   • Best for: Complex implementations, architecture decisions

5. qwen/qwen3-vl-235b-a22b-instruct - Alibaba's Qwen (vision-language)

   • Category: vision
   • Context: 32K
   • Best for: UI/visual tasks, design implementation

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 "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 "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);
  const issueCount = extractIssueCount(result);

  // Step 5: Clean up temp files
  await Bash(`rm ${instructionFile} ${resultFile}`);

  // Step 6: Return concise feedback
  return {
    success: true,
    summary,
    issueCount,
    fullReview: result  // Available if needed, but not in main context
  };
}

function extractSummary(review: string): string {
  const match = review.match(/## Summary\s*\n(.*?)(?=\n##|$)/s);
  return match ? match[1].trim() : "Review completed";
}

function extractIssueCount(review: string): { critical: number; medium: number; low: number } {
  const critical = (review.match(/### Critical\s*\n(.*?)(?=\n###|$)/s)?.[1].match(/^-/gm) || []).length;
  const medium = (review.match(/### Medium\s*\n(.*?)(?=\n###|$)/s)?.[1].match(/^-/gm) || []).length;
  const low = (review.match(/### Low\s*\n(.*?)(?=\n###|$)/s)?.[1].match(/^-/gm) || []).length;

  return { 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 ${resultFile} if needed.
    `
  });

  // Read full result if needed
  const fullAnalysis = await Read({ file_path: resultFile });

  // Clean up
  await Bash(`rm ${instructionFile} ${resultFile}`);

  return {
    summary: 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

# 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

Advanced Flags

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 ${model} "task 1"`);
await Bash(`claudish --model ${model} "task 2"`);

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 = await Read({ file_path: resultFile });

    // Extract summary
    reviews.push({
      model,
      summary: extractSummary(result),
      issueCount: extractIssueCount(result)
    });

    // Clean up
    await Bash(`rm ${instructionFile} ${resultFile}`);
  }

  return 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 < ${instructionFile}`);

  // Step 5: Read and present results
  const result = await Read({ file_path: resultFile });

  // Step 6: Clean up
  await Bash(`rm ${instructionFile} ${resultFile}`);

  return 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:

• AI Agent Guide: Print with claudish --help-ai
• Full documentation at GitHub repository

External Links:

• Claudish GitHub: https://github.com/MadAppGang/claudish
• Install: npm install -g claudish
• 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

Maintained by: MadAppGang Last Updated: November 25, 2025 Skill Version: 1.1.0