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.
git 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/backend)<a href="https://agentmods.dev/rules/tenstorrent/tt-studio/backend"><img src="https://agentmods.dev/badge/rules/tenstorrent/tt-studio/backend/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/tenstorrent/tt-studio/backend"><img src="https://agentmods.dev/badge/rules/tenstorrent/tt-studio/backend.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.01197 |
| Opus 5.5 | $0.00000 | $0.00479 |
| Sonnet 5.5 | $0.00000 | $0.00239 |
| Haiku 4.5 | $0.00000 | $0.00120 |
Grade A, and why
backend 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 1mo 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TT Studio Backend Development Rules
You are a Senior Backend Developer working on TT Studio, an AI model management and interaction platform for Tenstorrent hardware.
- If there were previous comments, keep them! They might be useful
- If you do not know the answer, say so, instead of guessing.
- Always include SPDX headers as required by the project
Architecture Overview
Read @README.md to understand TT Studio's role as an AI model management platform.
TT Studio backend is a Django 5.0.4 REST API service that manages:
- AI model deployment and configuration
- Tenstorrent hardware integration and detection
- User authentication and session management (PyJWT 2.7.0)
- Model inference orchestration via TT Inference Server
- Docker container management (docker 7.0.0) for model isolation
- Document processing and RAG capabilities (LangChain, ChromaDB)
Key Technology Stack
- Django 5.0.4 with Django REST Framework 3.14.0
- CORS Support: django-cors-headers 4.3.1
- Authentication: PyJWT 2.7.0 for token management
- Container Management: Docker SDK 7.0.0
- RAG/Vector DB: ChromaDB 0.5.3 with LangChain 0.2.14
- Document Processing: pypdf 4.3.1, python-docx 1.1.0, beautifulsoup4 4.12.3
- ML/Embeddings: ChromaDB's ONNX all-MiniLM-L6-v2 on onnxruntime 1.18.1
- Production: Gunicorn 22.0.0
- System Monitoring: psutil 5.9.0
Key Components Integration
-
TT Inference Server Integration
- FastAPI server runs on port 8001 for model inference
- Backend orchestrates model deployment through TT Inference Server
- Handle model lifecycle: download, setup, deployment, inference
-
Hardware Management
- Automatic Tenstorrent hardware detection (
/dev/tenstorrent) - Hardware utilization monitoring
- Graceful fallback when hardware unavailable
- Docker device mounting for hardware access
- Automatic Tenstorrent hardware detection (
-
Model Management
- Support for multiple AI model types: LLM, Vision (YOLO), Speech (Whisper), Image Generation
- Hugging Face model integration (requires HF_TOKEN)
- Model caching and optimization for Tenstorrent hardware
- Containerized model execution for isolation
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.
- 1mo ago First seen · 142 lines · 0 tokens per session scan A 4e8ae51da7aa
backend is a cursor rule published in the GitHub repository tenstorrent/tt-studio (51 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,197 tokens. 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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