Borrowing it
Nothing to install: this file belongs to tenstorrent/tt-studio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tenstorrent/tt-studio/main/.claude/skills/tt-studio-debug-bundle/SKILL.mdgit 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/skills/tenstorrent/tt-studio/tt-studio-debug-bundle)<a href="https://agentmods.dev/skills/tenstorrent/tt-studio/tt-studio-debug-bundle"><img src="https://agentmods.dev/badge/skills/tenstorrent/tt-studio/tt-studio-debug-bundle.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 5.1 | $0.00146 | $0.03222 |
| Opus 5 | $0.00073 | $0.01611 |
| Sonnet 5 | $0.00029 | $0.00644 |
| Haiku 4.5 | $0.00015 | $0.00322 |
Grade A, and why
tt-studio-debug-bundle 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 6d 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TT-Studio Debug-Bundle Triage
When this fires
User shares any of:
- A path like
/.../tt-studio-logs-ttbr-<hex>/or a ZIP with the same name - A
ttbr-<hex>reference (the diagnostic ID auto-generated by the TT-Studio Bug Reporter) - A screenshot of GitHub issue from
tenstorrent/tt-studiowhose title contains "TT-Studio bug report" - Log lines like
Allocation failed for ...: Invalid device_id,ChipSlotAllocator initialized: board=,Raw board_type:
Bundle layout (assume this shape)
tt-studio-logs-ttbr-<hex>/
├── agent.log # AI-agent service (LangGraph, voice, etc.)
├── backend.log # Django backend — the main signal
├── current_models.json # Live docker containers + deploy_cache snapshot
├── deployments.json # Persisted deploy records (id, device, device_ids, status)
├── docker-control-service.log # docker daemon control
├── model_run.log # FastAPI inference-control server
├── model_run_logs/ # Per-deploy model run logs:
│ └── model_run_<ts>_<model>_<device>_server.log
├── inference_artifacts/
│ ├── docker_server/ # Container stdout per deploy. Two flavors:
│ │ ├── vllm_<ts>_<model>_<device>_server.log # LLMs (Llama, Qwen, …)
│ │ └── media_<ts>_<model>_<device>_server.log # TTS / ASR / Whisper
│ │ # (speecht5_tts, distil-large-v3, …)
│ └── run_logs/ # tt-inference-server run.py logs (one per deploy)
├── startup.log
└── tt_smi.json # Raw hardware enumeration from tt-smi
Workflow
Copy this checklist and tick as you go:
Triage Progress:
- [ ] 1. Inventory the bundle
- [ ] 2. Read the three metadata JSONs
- [ ] 3. Pull the smoking-gun lines from backend.log
- [ ] 4. Read the failing deploy's fastapi + inference logs
- [ ] 5. Fan out parallel Explore subagents on source
- [ ] 6. Synthesize the chain (hardware → detection → allocation → request → user-visible error)
- [ ] 7. Report root cause + minimal fix shape
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.
- 6d ago First seen · 188 lines · 146 tokens per session scan A 377f1b4ddc40
tt-studio-debug-bundle is a skill published in the GitHub repository tenstorrent/tt-studio (49 stars, last pushed yesterday), licensed Apache-2.0. It adds 146 tokens to every session and 3,222 once invoked, about $0.0007 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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