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:

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

Check latest versions available:

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

## 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**

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

1. Run implementation with Grok:

excoder --model x-ai/grok-code-fast-1 "${featureDescription}"

1. 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 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 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

1. ✅ 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

1. ✅ 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')

1. ✅ 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"

});

1. ✅ 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

1. ✅ 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