Ex-Coder

CRITICAL - Guide for using Ex-Coder Autonomy CLI with OpenRouter/Groq models. Includes agent modes (build/plan), snapshot system, session management, LSP int...

SKILL.md Ex-Coder Autonomy Usage Skill

Command Description :taskfile new/open/run Create/open/run taskfiles for structured workflows :memory stats/inspect/purge Manage memory storage and usage :image-build Generate image scaffolds structures :enact issue phase Execute issue tracking phases :runs :run :ui :search :plan :execute :thinking :tokens :banner :capabilities` :delegate Agent delegation :interactive-launcher Interactive launcher configuration :repo Repository operations Full feature list now includes all commands from original request

Version: 1.3.0 Purpose: Guide AI agents on how to use Ex-Coder Autonomy CLI with advanced features Status: Production Ready

🆕 What's New in 1.3.0 New Features from OpenCode Integration Feature Command Description Interactive Mode :interactive Launch Claude Code UI with selected model (Groq/OpenRouter/SpaceLabs) Model Selection :models Interactive model picker across all providers Agent Modes :agent build|plan Switch between build (full access) and plan (read-only) modes Snapshot System :snapshot Git-based file change tracking and revert Session Enhancements :session compact|fork|export Compaction, forking, markdown export TUI Dialogs :agents, :sessions, :snapshots Interactive pickers for agents, sessions, snapshots Theming :themes 12 built-in themes (dracula, nord, tokyonight, etc.) LSP Integration :lsp TypeScript language server for diagnostics, symbols HTTP API Server :serve [port] RESTful API with SSE events MCP Support :mcp Model Context Protocol server integration ⚠️ CRITICAL RULES - READ FIRST 🚫 NEVER Run Excoder from Main Context Excoder MUST ONLY be run through sub-agents unless the user explicitly requests direct execution.

Why:

Running Excoder directly pollutes main context with 10K+ tokens (full conversation + reasoning) Destroys context window efficiency Makes main conversation unmanageable When you can run Excoder directly:

✅ User explicitly says "run excoder 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 Excoder/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 Excoder task, follow this process:

Check for existing agents that support proxy mode or external model delegation 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 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 Ex-Coder Autonomy is an advanced AI coding assistant CLI with multi-agent workflows, session management, and extensive tooling. It supports OpenRouter, Groq, SpaceLabs, Scrapegoat, and other models with features like agent modes, snapshots, LSP integration, and more.

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

What is Ex-Coder Autonomy? Ex-Coder Autonomy is a comprehensive AI coding tool that:

✅ Runs with any OpenRouter/Groq model (Grok, GPT-5, Gemini, etc.) ✅ Multi-agent workflow system (8 specialized roles) ✅ Agent modes (build/plan) for different task types ✅ Git-based snapshot/revert system ✅ Session management with compaction, forking, export ✅ LSP integration for code intelligence ✅ HTTP API server with SSE events ✅ MCP (Model Context Protocol) support ✅ 12 built-in themes ✅ Interactive TUI dialogs Use Cases:

Run tasks with different AI models (Grok for speed, GPT-5 for reasoning, Gemini for vision) Track and revert file changes with snapshots Manage long sessions with compaction and forking Get code intelligence via LSP (diagnostics, symbols, definitions) Remote control via HTTP API Extend functionality with MCP servers 🆕 New Features Reference Comprehensive Command Reference - Chat Mode - type your goal, then follow-up prompts Autonomy operates in natural language chat mode. Simply type your coding goal, and the system will respond with follow-up prompts and guidance.

Commands :exit - Exit the current session Agent Commands Command Description :agent Show current agent mode :agent build Switch to build mode (full access) :agent plan Switch to plan mode (read-only) :agent toggle Toggle between build and plan modes :agent list List available agents Snapshot System - :snapshot Command Description :snapshot Create a new snapshot :snapshot list List all snapshots :snapshot diff Show diff for a snapshot :snapshot revert Revert to a snapshot :snapshot cleanup Clean up old snapshots Session Management - :session Command Description :session Show session info :session compact Compact session history :session fork Fork current session :session export Export session to markdown Plan Management - :plan Command Description :plan Show current plan :execute phase Execute plan phase N :enact issue phase Execute issue tracking phase Image Operations - :image-build Command Description :image-build Generate image scaffolding Memory Management - :memory Command Description :memory stats Show memory usage statistics :memory inspect Inspect memory contents :memory purge Purge memory cache Taskfiles - :taskfile Command Description :taskfile new Create a new taskfile :taskfile open [path] Open a taskfile :taskfile run Run a taskfile Delegation - :delegate Command Description :delegate Delegate to sub-agents Interactive Mode - :interactive Command Description :interactive Enter interactive mode --interactive-launcher excoder claude Repository - :repo Command Description :repo show Show repository status :repo set Set repository path :repo status Get repository status :repo files List repository files UI / Search - :ui, :search, :pick Command Description :ui Open UI interface :search [query] Search for content :pick Interactive picker :agents Pick from agents :sessions Pick from sessions :snapshots Pick from snapshots :themes Pick from themes :lsp LSP integration :serve HTTP API server :mcp MCP support Thinking Mode - :thinking Command Description :thinking on off Token Management - :tokens Command Description :tokens Show token usage :cache-clear Clear token cache Display - :banner Command Description :banner on off :capabilities Show system capabilities Runs & Jobs - :runs Command Description :runs List background runs :run Run a background task :run show Show run details :cancel Cancel a run :jobs List jobs :fg Bring run to foreground :kill Kill a run Configuration Environment Variables for Models:

export GROQ_API_KEY='gsk...' # Your Groq API key export OPENROUTER_API_KEY='sk...' # Your OpenRouter API key export SPACELABS_API_KEY='sl...' # Your SpaceLabs API key export SCRAPEGOAT_API_KEY='sg...' # Your Scrapegoat API key Tips:

Tab toggles details in interactive UIs NO_COLOR environment variable disables colors Single-line thinking animation for long-running operations Use --resume to continue sessions 🆕 New Features Reference Interactive Mode (:interactive) - Claude Code Integration Launch Claude Code UI with your selected model from any provider (Groq, OpenRouter, SpaceLabs, Scrapegoat).

Launch interactive mode (opens model picker first)

:interactive

Or use :models to select model first, then :interactive

:models :interactive How It Works:

:interactive opens the model picker UI (or uses last selected model) Select provider (Groq, OpenRouter, SpaceLabs, etc.) Select model from that provider Claude Code UI launches with your selected model Full Claude Code experience with your model of choice Provider Selection:

┌─────────────────────────────────────────────────────────┐ │ ● ● ● Interactive mode Tab switch • ↑/↓ navigate │ ├─────────────────────────────────────────────────────────┤ │ Providers │ Models │ │ › groq ONLINE │ provider: OPENROUTER │ │ openrouter ONLINE │ Filter: [type to search] │ │ spacelabs ONLINE │ │ │ │ › moonshotai/kimi-k2-instruct-0905 │ │ │ qwen/qwen3-32b │ │ │ openai/gpt-oss-20b │ └─────────────────────────────────────────────────────────┘ After Selection:

Claude Code UI starts with your model Full agentic coding capabilities All Claude Code features available Session persists for reconnection Reconnecting to Session:

Reconnect to last interactive session

:interactive

Session info shows last interactive details

:session

Output includes: last interactive: groq • llama-3.3-70b • excoder

Environment Variables:

Set default provider

export EXCODER_PROVIDER=groq

Set default model

export EXCODER_MODEL=llama-3.3-70b-versatile

Set interactive launcher

export EXCODER_INTERACTIVE_LAUNCHER=excoder # or 'claude' Refer to Claude Code Skills: Once :interactive mode is loaded with your selected model, the agent (or human-in-the-loop) operates with full Claude Code capabilities. Refer to Claude Code documentation for:

File operations (read, write, edit) Shell command execution Multi-file refactoring Code search and navigation Git operations And all other Claude Code features The selected model handles all requests through the Ex-Coder proxy, giving you Claude Code's UI and workflow with your preferred model.

Model Selection (:models) Browse and select models from all configured providers.

Open interactive model picker

:models

Shows providers and their models

Tab to switch between provider list and model list

Type to filter models

Enter to select

Supported Providers:

Groq - Fast inference (llama, mixtral, gemma) OpenRouter - 100+ models (GPT-5, Grok, Gemini, Claude) SpaceLabs - Custom models Scrapegoat - Specialized models Quick Model Selection:

Set model via environment for session

export EXCODER_MODEL=x-ai/grok-code-fast-1

Or use model map for different roles

export EXCODER_MODEL_MAP='{"coordinator":{"provider":"groq","model":"llama-3.3-70b-versatile"}}' Agent Modes (:agent) Switch between build and plan modes for different task types.

Show current mode

:agent

Switch to build mode (full access, default)

:agent build

Switch to plan mode (read-only, analysis focused)

:agent plan

Toggle between modes

:agent toggle

List available modes

:agent list

Open interactive agent picker

:agents Mode Permissions:

Mode Write Files Run Shell Install Packages Require Approval build ✓ ✓ ✓ ✗ plan ✗ ✗ ✗ ✓ Snapshot System (:snapshot) Git-based tracking of file changes with revert capability.

Create a snapshot of current state

:snapshot

List recent snapshots

:snapshot list

Show diff since snapshot

:snapshot diff [hash]

List changed files since snapshot

:snapshot files [hash]

Revert to a snapshot

:snapshot revert [hash]

Run garbage collection

:snapshot cleanup

Open interactive snapshot browser

:snapshots Environment Variables:

EXCODER_AUTO_SNAPSHOT=1 - Auto-snapshot before :delegate runs Session Enhancements (:session) Advanced session management with compaction, forking, and export.

Show session info (enhanced)

:session

Compact old turns to reduce context

:session compact

Fork session at a specific turn

:session fork [turnIndex]

Export session as markdown

:session export [path]

Show session commands

:session help

Open interactive session browser

:sessions New Session Fields:

forkedFrom - Source session and turn index if forked lastCompactedAt - Timestamp of last compaction compactionSummary - Summary of compacted turns agentMode - Current agent mode (build/plan) lastSnapshotHash - Last snapshot hash Theming System (:themes) 12 built-in themes with runtime switching.

Open interactive theme picker

:themes

Set theme via environment

export EXCODER_THEME=dracula Available Themes:

default - Default dark theme dracula - Popular dark theme nord - Arctic, north-bluish color palette tokyonight - Clean dark theme catppuccin - Soothing pastel theme gruvbox - Retro groove color scheme monokai - Sublime Text inspired onedark - Atom One Dark solarized - Precision colors github - GitHub light theme rosepine - All natural pine synthwave - Neon 80s aesthetic matrix - Green on black LSP Integration (:lsp) Language Server Protocol support for code intelligence.

Show LSP status

:lsp status

Initialize LSP servers

:lsp init

Get diagnostics for all files

:lsp diagnostics

Get diagnostics for specific file

:lsp diagnostics src/index.ts

Search workspace symbols

:lsp symbols

Go to definition

:lsp definition ::

Find references

:lsp references :: Enable LSP:

export EXCODER_LSP=1 Supported Languages:

TypeScript/JavaScript (via tsserver) HTTP API Server (:serve) RESTful API server with SSE events for remote control.

Show server status

:serve

Start server on default port (4096)

:serve start

Start server on custom port

:serve 8080

Stop server

:serve stop API Endpoints:

Method Endpoint Description GET /health Health check GET /session Current session info GET /session/turns Session conversation GET /config Configuration GET /events SSE event stream POST /message Send message to agent Authentication:

export EXCODER_SERVER_PASSWORD=secret

Then use: Authorization: Bearer secret

MCP Support (:mcp) Model Context Protocol server integration for tool discovery.

Show MCP server status

:mcp status

Connect to configured servers

:mcp init

List available tools

:mcp tools

Call a tool

:mcp call /

Disconnect from all servers

:mcp disconnect Configuration (.ex-coder/mcp.json):

{ "filesystem": { "type": "stdio", "command": ["npx", "-y", "@modelcontextprotocol/server-filesystem", "/path/to/dir"], "enabled": true }, "github": { "type": "stdio", "command": ["npx", "-y", "@modelcontextprotocol/server-github"], "env": { "GITHUB_TOKEN": "ghp_..." } } } Or via environment:

export EXCODER_MCP_CONFIG='{"filesystem":{"type":"stdio","command":["npx","-y","@modelcontextprotocol/server-filesystem","/"]}}' Interactive TUI Dialogs New interactive dialogs for common operations:

Command Dialog Description :agents Agent Picker Select agent mode with preview :sessions Session Browser Browse and select sessions :snapshots Snapshot Browser Browse snapshots with actions :themes Theme Picker Select theme with color preview Navigation:

↑/↓ - Navigate items Enter - Select / - Filter (where supported) Esc - Cancel Requirements System Requirements OpenRouter API Key - Required (set as OPENROUTER_API_KEY environment variable) Excoder CLI - Install with: npm install -g excoder or bun install -g excoder 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:

Visit https://openrouter.ai/keys Sign up (free tier available) Create API key Set as environment variable Quick Start Guide Step 1: Install Excoder

With npm (works everywhere)

npm install -g ex-coder-autonomy@3.0.0 npm install -g scrapegoat-ex-coder@4.1.3

Note: After running updates, press n and Enter to avoid session closure due to version changes.

Note: After running updates, press n and Enter to avoid session closure due to version changes.

These commands install the latest released versions of ex-coder-autonomy (v3.0.0) and scrapegoat-ex-coder (v4.1.3).

excoder-autonomy (to run Excoder via :interactive command in Autonomy REPL. Note: it requires valid Anthropic API key in .env file) excoder-autonomy-goatmode (opencode shell) Tip: To make agent live and breathe on the terminal use a conda environment with python 3.11 or higher

Verify installation

excoder --version

Version Info

Check installed versions with:

bash

npm list -g excoder-autonomy
npm list -g scrapegoat-ex-coder

Check latest versions available:

bash

npm show excoder-autonomy version
npm show scrapegoat-ex-coder version

List ALL OpenRouter models grouped by provider

excoder --models

Fuzzy search models by name, ID, or description

excoder --models gemini excoder --models "grok code"

Show top recommended programming models (curated list)

excoder --top-models

JSON output for parsing

excoder --models --json excoder --top-models --json

Force update from OpenRouter API

excoder --models --force-update Step 3: Run Excoder Interactive Mode (default):

Shows model selector, persistent session

excoder Single-shot Mode:

One task and exit (requires --model)

excoder --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 | excoder --stdin --model openai/gpt-5-codex "Review these changes" Recommended Models Top Models for Development (verified from OpenRouter):

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

Category: coding Context: 256K Best for: Quick iterations, agentic coding google/gemini-2.5-flash - Google's Gemini (state-of-the-art reasoning)

Category: reasoning Context: 1000K Best for: Complex analysis, multi-step reasoning minimax/minimax-m2 - MiniMax M2 (high performance)

Category: coding Context: 128K Best for: General coding tasks openai/gpt-5 - OpenAI's GPT-5 (advanced reasoning)

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

excoder --models

Search for specific models

excoder --models grok excoder --models "gemini flash"

Show curated top models

excoder --top-models

Force immediate update

excoder --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)

excoder --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

excoder --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):

excoder --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: excoder --model x-ai/grok-code-fast-1 --stdin < multi-phase-workflow.md Best Practice: File-Based Sub-Agent Pattern ⚠️ CRITICAL: Don't Run Excoder Directly from Main Conversation Why: Running Excoder 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/excoder-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/excoder-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 Excoder with file instruction**
```bash
# Read instruction from file, write result to file
excoder --model x-ai/grok-code-fast-1 --stdin < /tmp/excoder-task-{timestamp}.md > /tmp/excoder-result-{timestamp}.md
Step 3: Read result file and provide summary

// In your agent/command:
const result = await Read({ file_path: "/tmp/excoder-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. ${summary}`;
Complete Example: Using Excoder in Sub-Agent
/**
 * Example: Run code review with Grok via Excoder sub-agent
 */
async function runCodeReviewWithGrok(files: string[]) {
  const timestamp = Date.now();
  const instructionFile = `/tmp/excoder-review-instruction-${timestamp}.md`;
  const resultFile = `/tmp/excoder-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 Excoder with stdin
  await Bash(`excoder --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 Excoder from an agent, use the Task tool to create a sub-agent:

Pattern 1: Simple Task Delegation
/**
 * Example: Delegate implementation to Grok via Excoder
 */
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 Excoder CLI to implement this feature with Grok model:

${featureDescription}

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

2. Run implementation with Grok:
   excoder --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/excoder-analyze-${timestamp}.md`;
  const resultFile = `/tmp/excoder-analyze-result-${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 Excoder to analyze codebase with Gemini model.

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

STEPS:
1. Read instruction file: ${instructionFile}
2. Run: excoder --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/excoder-${model.replace('/', '-')}-${timestamp}.md`;

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

${task}

STEPS:
1. Run: excoder --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
excoder --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
excoder --model openai/gpt-5 "refactor authentication system to use OAuth2"
Workflow 3: UI Implementation with Qwen (Vision)
# Vision-language model for UI tasks
excoder --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 | excoder --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 $model ==="
  excoder --model "$model" "find security vulnerabilities in auth.ts"
done
Excoder 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 | excoder --stdin --model grok
--models	List all models or search	excoder --models or excoder --models gemini
--top-models	Show top recommended models	excoder --top-models
--json	JSON output (implies --quiet)	excoder --json "task"
--help-ai	Print AI agent usage guide	excoder --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
Quiet Mode (default in single-shot)

excoder --model grok "task"
# Clean output, no [excoder] logs
Verbose Mode

excoder --verbose "task"
# Shows all [excoder] logs for debugging
JSON Mode

excoder --json "task"
# Structured output: {result, cost, usage, duration}
Cost Tracking
Excoder 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:

excoder --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: Excoder Not Installed
Error:

command not found: excoder
Fix:

npm install -g excoder
# Or: bun install -g excoder
Error 3: Model Not Found
Error:

Model 'invalid/model' not found
Fix:

# List available models
excoder --models

# Use valid model ID
excoder --model x-ai/grok-code-fast-1 "task"
Error 4: OpenRouter API Error
Error:

OpenRouter API error: 401 Unauthorized
Fix:

Check API key is correct
Verify API key at https://openrouter.ai/keys
Check API key has credits (free tier or paid)
Error 5: Port Already in Use
Error:

Error: Port 3000 already in use
Fix:

# Let Excoder pick random port (default)
excoder --model grok "task"

# Or specify different port
excoder --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
excoder --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=$(excoder --json "task" | jq -r '.result')
COST=$(excoder --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 Excoder",
  prompt: "Use excoder --model grok '...' and return summary only"
});
5. ✅ Update Models Regularly
Why: Get latest model recommendations

How:

# Auto-updates every 2 days
excoder --models

# Search for specific models
excoder --models deepseek

# Force update now
excoder --models --force-update
6. ✅ Use --stdin for Large Prompts
Why: Avoid command line length limits

How:

git diff | excoder --stdin --model grok "Review changes"
Anti-Patterns (Avoid These)
❌❌❌ NEVER Run Excoder 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("excoder --model grok 'implement feature'");

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

// ❌ NEVER DO THIS - Even with --json, output is huge
const result = await Bash("excoder --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 Excoder to implement the feature with Grok model.

CRITICAL INSTRUCTIONS:
1. Create instruction file: /tmp/excoder-task-${Date.now()}.md
2. Write detailed task requirements to file
3. Run: excoder --model x-ai/grok-code-fast-1 --stdin < /tmp/excoder-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 Excoder 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 excoder directly in main context for debugging"
if (userExplicitlyRequestedDirect) {
  await Bash("excoder --model grok 'task'");
}
❌ Don't Ignore Model Selection
Wrong:

# Always using default model
excoder "any task"
Right:

# Choose appropriate model
excoder --model x-ai/grok-code-fast-1 "quick fix"
excoder --model google/gemini-2.5-flash "complex analysis"
❌ Don't Parse Text Output
Wrong:

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

# Use JSON output
COST=$(excoder --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("excoder --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):

// Excoder 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 - Excoder will validate with OpenRouter
await Bash(`excoder --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/excoder-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("excoder --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(`excoder --model ${model} "task 1"`);
await Bash(`excoder --model ${model} "task 2"`);
Guidance for Agents:

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

const result = await Bash("excoder --model grok 'task'");
Right:

try {
  const result = await Bash("excoder --model grok 'task'");
} catch (error) {
  console.error("Excoder failed:", error.message);
  // Fallback to embedded Claude or handle error
}
Agent Integration Examples
Example 1: Code Review Agent
/**
 * Agent: code-reviewer (using Excoder 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(`excoder --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("excoder --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(`excoder --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: Excoder takes long time to respond

Solutions:

Use faster model: x-ai/grok-code-fast-1 or minimax/minimax-m2
Reduce prompt size (use --stdin with concise instructions)
Check internet connection to OpenRouter
Issue: High Costs
Symptoms: Unexpected API costs

Solutions:

Use budget-friendly models (check pricing with --models or --top-models)
Enable cost tracking: --cost-tracker
Use --json to monitor costs: excoder --json "task" | jq '.total_cost_usd'
Issue: Context Window Exceeded
Symptoms: Error about token limits

Solutions:

Use model with larger context (Gemini: 1000K, Grok: 256K)
Break task into smaller subtasks
Use file-based pattern to avoid conversation history
Issue: Model Not Available
Symptoms: "Model not found" error

Solutions:

Update model cache: excoder --models --force-update
Check OpenRouter website for model availability
Use alternative model from same category
Additional Resources
Documentation:

Full README: mcp/excoder/README.md (in repository root)
AI Agent Guide: Print with excoder --help-ai
Model Integration: skills/excoder_usage/SKILL.md (in repository root)
External Links:

Excoder GitHub: https://github.com/ex-coder/ex-coder
OpenRouter: https://openrouter.ai
OpenRouter Models: https://openrouter.ai/models
OpenRouter API Docs: https://openrouter.ai/docs
Version Information:

excoder --version
Get Help:

excoder --help        # CLI usage
excoder --help-ai     # AI agent usage guide
Maintained by: Ex-Coder Contributors Last Updated: March 4, 2026 Skill Version: 1.2.0
Copyright: 2026 Space-labs.pro