claudish-usage

plugin: bun updated: 2026-01-20 # 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 | 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 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 ```bash # 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 ```bash # With npm (works everywhere) npm install -g claudish # With Bun (faster) bun install -g claudish # Verify installation claudish --version ``` ### Step 2: Get Available Models ```bash # 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):** ```bash # Shows model selector, persistent session claudish ``` **Single-shot Mode:** ```bash # One task and exit (requires --model) claudish --model x-ai/grok-code-fast-1 "implement user authentication" ``` **With stdin for large prompts:** ```bash # 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:** ```bash # 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:** ```bash # 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`):** ```bash claudish --model x-ai/grok-code-fast-1 "create button component" ``` **Complex task (use file-based):** ```typescript // 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** ```markdown # /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** ```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. ${summary}`; ``` ### Complete Example: Using Claudish in Sub-Agent ```typescript /** * Example: Run code review with Grok via Claudish sub-agent */ async function runCodeReviewWithGrok(files: string[]) { const timestamp = Date.now(); const instructionFile = `/tmp/claudish-review-instruction-${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