Skill: lordmahakaal/excoder-autonomy (version 1.1.4)

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<id> phase<N> 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 <hash> Show diff for a snapshot :snapshot revert <hash> 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<N> Execute plan phase N :enact issue <id> phase<N> Execute issue tracking phase Image Operations - :image-build Command Description :image-build <image-path> 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 <path> 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 <path> 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 <goal> Run a background task :run show <runId> Show run details :cancel <runId> Cancel a run :jobs List jobs :fg <runId> Bring run to foreground :kill <runId> 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 <query> # Go to definition :lsp definition <file>:<line>:<col> # Find references :lsp references <file>:<line>:<col> 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 <server>/<tool> <json-args> # 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='[REDACTED_SECRET]' # 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


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)