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 skills add Ascend/agent-skills --skill atc-model-convertergit clone --depth 1 https://github.com/Ascend/agent-skillsWrote 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/ascend/agent-skills/atc-model-converter)<a href="https://agentmods.dev/skills/ascend/agent-skills/atc-model-converter"><img src="https://agentmods.dev/badge/skills/ascend/agent-skills/atc-model-converter/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/skills/ascend/agent-skills/atc-model-converter"><img src="https://agentmods.dev/badge/skills/ascend/agent-skills/atc-model-converter.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 | $0.00142 | $0.07098 |
| Opus 5 | $0.00071 | $0.03549 |
| Sonnet 5 | $0.00028 | $0.01420 |
| Haiku 4.5 | $0.00014 | $0.00710 |
Grade A, and why
atc-model-converter 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 10d 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.
The source is not reproduced here
Licensed MulanPSL-2.0
The repository is licensed MulanPSL-2.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
12 files 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.
- references/AIPP_CONFIG.md 5.4 KB
- references/CANN_VERSIONS.md 5.4 KB
- references/EXAMPLE_README.md 15 KB
- references/FAQ.md 11 KB
- references/INFERENCE.md 8.3 KB
- references/PARAMETERS.md 5.7 KB
- scripts/check_env_enhanced.sh 6.7 KB runs code
- scripts/compare_precision.py 9.8 KB runs code
- scripts/export_onnx.py 12 KB runs code
- scripts/get_onnx_info.py 2.4 KB runs code
- scripts/infer_om.py 7.7 KB runs code
- scripts/setup_env.sh 6.3 KB runs code
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.
- 10d ago First seen · 679 lines · 142 tokens per session scan A 0a1aeef4b6ff
atc-model-converter is a skill published in the GitHub repository Ascend/agent-skills (40 stars, last pushed 4mo ago), licensed MulanPSL-2.0. It adds 142 tokens to every session and 7,098 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.
Other skills, from other repositories
submit-github-bug-issue
Use when converting QA findings, black-box failures, red-team reports, regression evidence, or local bug notes into GitHub Issues for NVIDIA/TensorRT-Model-Connect. Standardizes checking issue templates, checking labels, de-duplicating existing issues, drafting a bug report, creating the issue on GitHub, applying the…
submit-github-pr
Use when publishing an existing TensorRT-Model-Connect change as a GitHub pull request. Verifies authenticated repository access, branch and diff scope, validation evidence, commit identity, reviewer-facing text, exact pushed head, and the created draft PR without merging it.
optimize-model-precision
Evaluate supported precision, quantization, or selected FP32-layer choices for one TensorRT-Model-Connect family with matched correctness and timing.
transform-model
Add a Hugging Face or local checkpoint to TensorRT-Model-Connect as a self-contained family, or extend the family that already owns it.
debug-trt-mismatch
Diagnose a TensorRT-Model-Connect family whose native output disagrees with its declared reference or family-owned correctness contract.
profile-model
Measure one TensorRT-Model-Connect model through its public Task API or run a checked-in performance-matrix entry with comparable evidence.