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)

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?

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

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

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

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**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

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**Single-shot Mode:**
```bash

**With stdin for large prompts:**
```bash

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

// 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

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**Step 3: Read result file and provide summary**
```typescript
// 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}`;

/**
 * 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

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/**
 * 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;
}

/**
 * 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

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/**
 * 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;
}

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directory • model-id • $cost • ctx%

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

claudish --json "task" | jq '.total_cost_usd'

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Error: OPENROUTER_API_KEY environment variable is required

export OPENROUTER_API_KEY='sk-or-v1-...'

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command not found: claudish

npm install -g claudish

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Model 'invalid/model' not found

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OpenRouter API error: 401 Unauthorized

Error: Port 3000 already in use

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

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

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

// ❌ 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'");

// ✅ 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.
  `
});

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

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**Right:**
```bash

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

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

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

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
}

// 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"`);

// 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`);

// 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;

// 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"`);

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

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: 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;
}

/**
 * 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

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claudish --version

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