By torque-ai
AI task orchestration — multi-provider routing, DAG workflows, quality gates, and distributed execution
View TORQUE cost tracking, budget status, and provider statistics
Cancel running or queued TORQUE tasks
Monitor CI runs — watch repos, diagnose failures, view history
View and modify TORQUE configuration — tuning, hardware, safeguards
Manage Ollama hosts — add, remove, enable, disable, refresh models, check health
Monitor running TORQUE workflows — check progress, handle stalls, resubmit failures. Use when: 'watch this workflow', 'monitor tasks', 'check batch progress'
Review completed TORQUE task output — validate quality, check for stubs/truncation, approve or flag issues. Use when: 'review this task', 'check task output', 'validate task results'
Design TORQUE DAG workflows from feature specs — analyze parallelism, select providers per step, output workflow definition. Use when: 'design a workflow', 'plan this feature', 'create a DAG'
View TORQUE cost tracking, budget status, and provider statistics
Cancel running or queued TORQUE tasks
View and modify TORQUE configuration — tuning, hardware, safeguards
Restart the TORQUE MCP server to apply code changes
Review completed TORQUE task output — validate, approve, or reject
Admin access level
Server config contains admin-level keywords
Uses power tools
Uses Bash, Write, or Edit tools
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An automated software factory for Claude Code.
TORQUE turns Claude Code into an autonomous software factory. It discovers what needs building, plans the work, dispatches tasks across local and cloud LLM providers in parallel, verifies quality, auto-remediates failures, and cuts releases — with you as the architect at the control panel.
# Install
git clone https://github.com/torque-ai/torque-ai.git
cd torque-ai/server && npm install
# Start the server
node index.js
On first startup, TORQUE registers itself as an MCP server in your Claude Code configuration. Open any Claude Code session and TORQUE's tools are available immediately.
Tell Claude what you need. Claude decides when to use TORQUE:
"Write unit tests for auth.ts"
Claude analyzes the task, calls TORQUE's smart_submit_task tool, and TORQUE selects the best available provider, dispatches the work, monitors the output for stubs and truncation, verifies the build, and returns the result. If the provider fails, TORQUE retries on the next one in the fallback chain.
"Refactor the payment module and write tests for it — run both in parallel"
Claude creates a TORQUE workflow with two tasks, routes each to the appropriate provider, and monitors both simultaneously.
TORQUE can inspect rendered UI through the optional @torque-ai/peek companion. Install it on the workstation whose display TORQUE should capture, then start the local Peek server:
npm install -g @torque-ai/peek
torque-peek check
torque-peek start
By default, Peek binds to 127.0.0.1:9876. When TORQUE can reach that host, tools such as peek_ui, peek_diagnose, and peek_interact can capture screenshots, read UI element trees, and send basic input events for visual verification.
For browser-native work, SnapScope also exposes an accessibility-tree-first contract in server/plugins/snapscope/handlers/browser-capture.js. Use peek_browser_state or peek_ui with browser_state: true to get indexed interactive elements from Playwright/CDP, and use peek_browser_action for the safe action vocabulary: click, input, extract, scroll, and switch_tab. Screenshots are a fallback via vision_fallback or include_screenshot, not the default browser observation. storage_state_path can preserve Playwright session cookies and local storage without putting credentials in prompts.
For a separate display host, expose Peek with a shared token and register the host in TORQUE:
torque-peek start --host 0.0.0.0 --token <shared-secret>
register_peek_host { name: "remote-display", url: "http://display-host:9876" }
Use --host 0.0.0.0 only for trusted remote display hosts, and pair it with --token so requests must include the matching Peek token.
Claude Code talks to TORQUE through MCP (Model Context Protocol). The factory operates as an autonomous pipeline:
You set the direction. The factory runs lights-out.
TORQUE ships with support for several provider types. What's available depends on what you have installed and configured.
| Type | Examples | Setup |
|---|---|---|
| Local LLM | Ollama (any model you've pulled) | Install Ollama, pull a model, TORQUE auto-discovers |
| CLI Tools | Codex, Claude Code | Install the CLI, authenticate, enable in TORQUE |
| Cloud APIs | DeepInfra, Groq, Cerebras, Google AI, Hyperbolic, OpenRouter | Set your API key, enable the provider |
Additional providers can be configured through Claude — ask it to run configure_provider or add_ollama_host to add new providers, models, or remote Ollama instances on your LAN. Provider routing, fallback chains, and model assignments are all configurable at runtime.
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Superpowers Plus core skills library for Claude Code: planning, execution routing, TDD, debugging, and collaboration workflows
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Tools to maintain and improve CLAUDE.md files - audit quality, capture session learnings, and keep project memory current.
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