Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/tenstorrent/tt-studio/project-overviewgit clone --depth 1 https://github.com/tenstorrent/tt-studioWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/tenstorrent/tt-studio/project-overview)<a href="https://agentmods.dev/rules/tenstorrent/tt-studio/project-overview"><img src="https://agentmods.dev/badge/rules/tenstorrent/tt-studio/project-overview.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00606 | $0.00606 |
| Opus 5 | $0.00303 | $0.00303 |
| Sonnet 5 | $0.00121 | $0.00121 |
| Haiku 4.5 | $0.00061 | $0.00061 |
Grade A, and why
project-overview scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TT-Studio
TT-Studio is an easy-to-use web interface for running AI models on Tenstorrent hardware. It combines TT Inference Server's core packaging setup, containerization, and deployment automation with TT-Metal's model execution framework specifically optimized for Tenstorrent hardware.
Important notes:
- TT-Studio requires access to a Tenstorrent AI accelerator for full deployment features
- Alternatively, you can connect just the frontend to a remote API endpoint without direct hardware access
- The platform provides automatic hardware detection and seamless integration with Tenstorrent devices
- Uses containerized deployment through Docker for isolation and easy deployment
- The startup.sh script is deprecated - use
python run.pyfor all operations
Docs
- Main README: Complete overview, setup instructions, and quick start guide
- Setup Guide: Complete installation & configuration using run.py
- FAQ: Quick answers to common questions about TT-Studio
- Model Interface Guide: Using TT-Studio as AI playground (Chat, Vision, Speech, Images)
- Troubleshooting Guide: Solutions for common setup and runtime issues
- Contributing Guide: How to contribute code to the project
- Development Setup: Development environment configuration
Examples
- AI Model Interface: Complete examples of using Chat, Vision, Speech, and Image models
- vLLM Models Guide: Specific examples for running vLLM models
- AI Agent Setup: Setting up and using the AI assistant functionality
Key Components
- Frontend Interface: Modern React-based UI for model interaction and management
- Backend API: Django-based service for model management, deployment, and API endpoints
- TT Inference Server: FastAPI server for handling model inference requests
- Docker Containers: Complete containerization for isolation and easy deployment
- Automatic Hardware Detection: Seamless integration and auto-mounting of Tenstorrent devices (/dev/tenstorrent)
- Automated Setup: Complete environment configuration and model setup automation via run.py script
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 52 lines · 606 tokens per session scan A ec5e53fef786
project-overview is a cursor rule published in the GitHub repository tenstorrent/tt-studio (49 stars, last pushed yesterday), licensed Apache-2.0. It adds 606 tokens to every session, about $0.0030 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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