claudish-usage

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3
Source

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.

Install

mkdir -p .claude/skills/claudish-usage && curl -L -o skill.zip "https://mcp.directory/api/skills/download/1278" && unzip -o skill.zip -d .claude/skills/claudish-usage && rm skill.zip

Installs to .claude/skills/claudish-usage

About this skill

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 TypeRecommended AgentFallbackNotes
Code implementationCreate coding agent with proxy modegeneral-purposeBest: custom agent for project-specific patterns
Code reviewUse existing code review agent + proxygeneral-purposeCheck if plugin has review agent first
Architecture planningUse existing architect agent + proxygeneral-purposeLook for architect or planner agents
TestingUse existing test agent + proxygeneral-purposeLook for test-architect or tester agents
RefactoringCreate refactoring agent with proxygeneral-purposeComplex refactors benefit from specialized agent
Documentationgeneral-purpose-Simple task, generic agent OK
AnalysisUse existing analysis agent + proxygeneral-purposeCheck for analyzer or detective agents
Othergeneral-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

PrefixBackendExample
(none)OpenRouteropenai/gpt-5.3
g/ gemini/Google Geminig/gemini-2.0-flash
oai/ openai/OpenAIoai/gpt-4o
ollama/Ollamaollama/llama3.2
lmstudio/LM Studiolmstudio/model
http://...Customhttp://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:

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):

ModelProviderBest For
openai/gpt-5.3OpenAIDefault - Most advanced reasoning
minimax/minimax-m2.1MiniMaxBudget-friendly, fast
z-ai/glm-4.7Z.AIBalanced performance
google/gemini-3-pro-previewGoogle1M context window
moonshotai/kimi-k2-thinkingMoonShotExtended thinking
deepseek/deepseek-v3.2DeepSeekCode specialist
qwen/qwen3-vl-235b-a22b-thinkingAlibabaVision + reasoning

Direct API Options (lower latency):

ModelBackendBest For
g/gemini-2.0-flashGeminiFast tasks, large context
oai/gpt-4oOpenAIGeneral purpose
ollama/llama3.2LocalFree, private

Get Latest Models:

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

# Search for specific m

---

*Content truncated.*

schemas

MadAppGang

YAML frontmatter schemas for Claude Code agents and commands. Use when creating or validating agent/command files.

23

golang

MadAppGang

Use when building Go backend services, implementing goroutines/channels, handling errors idiomatically, writing tests with testify, or following Go best practices for APIs/CLI tools.

102

golang-performance

MadAppGang

Use when profiling Go applications (pprof), running benchmarks, optimizing memory/CPU usage, or debugging performance bottlenecks in production Go code.

52

external-model-selection

MadAppGang

Choose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via claudish, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.

172

hierarchical-coordinator

MadAppGang

Prevent goal drift in long-running multi-agent workflows using a coordinator agent that validates outputs against original objectives at checkpoints. Use when orchestrating 3+ agents, multi-phase features, complex implementations, or any workflow where agents may lose sight of original requirements. Trigger keywords - "hierarchical", "coordinator", "anti-drift", "checkpoint", "validation", "goal-alignment", "decomposition", "phase-gate", "shared-state", "drift detection".

21

email-deliverability

MadAppGang

Email deliverability best practices and troubleshooting

11

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