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 skills/nvidia/tensorrt-model-connect/debug-trt-mismatchnpx skills add NVIDIA/TensorRT-Model-Connect --skill debug-trt-mismatchgit clone --depth 1 https://github.com/NVIDIA/TensorRT-Model-ConnectWrote 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/nvidia/tensorrt-model-connect/debug-trt-mismatch)<a href="https://agentmods.dev/skills/nvidia/tensorrt-model-connect/debug-trt-mismatch"><img src="https://agentmods.dev/badge/skills/nvidia/tensorrt-model-connect/debug-trt-mismatch.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 | $0.00061 | $0.01579 |
| Opus 5 | $0.00030 | $0.00790 |
| Sonnet 5 | $0.00012 | $0.00316 |
| Haiku 4.5 | $0.00006 | $0.00158 |
Grade A, and why
debug-trt-mismatch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug TRT Mismatch
Goal
Find the first boundary where TensorRT and the declared reference disagree. Preserve the failing workload, sampling settings, model revision, bundle, and runtime strategy while narrowing the problem. Do not hide a mismatch by loosening validation thresholds.
Preflight
Record the exact revision and environment:
git rev-parse HEAD
nvidia-smi --query-gpu=name,driver_version --format=csv,noheader
python3 -c "import tensorrt as trt; print(trt.__version__)"
PYTHONPATH=python:. python3 -c "import tensorrt_model_connect; print('package: OK')"
test -x ./build/trtmc
If the local checkout does not have the required GPU or TensorRT environment, use the existing team container rather than changing the investigation:
docker ps -a --filter "name=trtmc-dev-gb300" --format "{{.Names}} {{.Status}}"
./scripts/bootstrap_workspace.sh --id <team-id> --branch "$(git branch --show-current)" --detach
Run later commands inside trtmc-dev-gb300-<team-id> when needed.
Reproduce Through The Owned Validation Path
List model-first workloads and dry-run the failing one:
PYTHONPATH=python:. python3 tools/trtmc_validate.py --list
PYTHONPATH=python:. python3 tools/trtmc_validate.py \
<model> <workload> \
--dry-run \
--output /tmp/trtmc-validation
The model binding in tests/validation/model_workloads.yaml, workload contract
in tests/validation/workloads.yaml, family manifests, and their sidecars are
the source of truth for inputs, sampling, and comparison gates. Read
tools/validation/README.md before changing the engine: the persisted
task_eval artifact key remains intentionally stable even though executable
code moved to tools/validation/. Keep reference generation and comparison
separate in the report; an execution failure is not a numerical mismatch.
Route By Model Capability
Inspect the model's Python, C++, and E2E MODEL.toml entries before selecting a
debugger:
| Model path | First focused tool |
|---|---|
Decoder family with decoder_debug validation profile |
tools/diff_logits.py |
| Decoder layer localization | tools/diff_layers.py |
| Vision-language | tools/diff_vl.py |
| Audio/Bark | tools/diff_audio.py |
| Diffusion | tools/debug_diffusion_pipeline.py |
| Python runner versus C++ | tools/test_runner_parity.py |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 204 lines · 61 tokens per session scan A 2a27694da85e
debug-trt-mismatch is a skill published in the GitHub repository NVIDIA/TensorRT-Model-Connect (188 stars, last pushed 3d ago), licensed Apache-2.0. It adds 61 tokens to every session and 1,579 once invoked, about $0.0003 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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