Borrowing it
Nothing to install: this file belongs to maoxx241/vllm-ascend-workspace. 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/maoxx241/vllm-ascend-workspace/main/.agents/skills/ascend-operator-debug/SKILL.mdgit clone --depth 1 https://github.com/maoxx241/vllm-ascend-workspaceWrote 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/maoxx241/vllm-ascend-workspace/ascend-operator-debug)<a href="https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/ascend-operator-debug"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/ascend-operator-debug/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/maoxx241/vllm-ascend-workspace/ascend-operator-debug"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/ascend-operator-debug.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00091 | $0.00589 |
| Opus 5 | $0.00046 | $0.00295 |
| Sonnet 5 | $0.00018 | $0.00118 |
| Haiku 4.5 | $0.00009 | $0.00059 |
Grade A, and why
ascend-operator-debug 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 9d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ascend Operator Debug
Turn a suspected operator failure into a portable reproducer and an explicit case matrix. A model-level symptom is not an operator bug until the isolated call reproduces it.
Workflow
- Capture the failing call's operator name, arguments, input metadata, execution mode, environment, and source stack without copying full tensors by default.
- Define a trusted reference implementation and tolerances before comparing.
- Create an explicit case matrix with
scripts/operator_debug.py plan. - Run the generated cases on a remote Ascend environment. Change one dimension at a time: dtype, shape, layout, mode, or operator option.
- Record each normalized result with
record. - Run
analyzeto separate numerical mismatch, crash, unsupported combination, missing evidence, and operator-pass/integration-fail outcomes. - Add the smallest failing case as a regression test, then rerun the original model integration after the operator fix.
Entry point
scripts/operator_debug.py provides:
plan: validate the explicit case matrix and create the evidence layout;record: accept one result for a planned case without overwriting evidence;analyze: summarize failure axes and create a Run Manifest-linked report.
Read only the reference needed for the current phase:
Boundaries
- This skill begins after evidence identifies one operator boundary or the user explicitly supplies an operator reproducer.
- Whole-model eager-versus-graph localization belongs to
vllm-ascend-graph-debug; hand off only after one operator call is isolated. - Rank-dependent and collective failures belong to
vllm-ascend-distributed-debug. - Model-level throughput regressions belong to performance workflows. An optional operator timing value here is only supporting evidence for the isolated case.
Rules
What ships with it
6 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.
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.
- 9d ago First seen · 58 lines · 91 tokens per session scan A 9bf94487695a
ascend-operator-debug is a skill published in the GitHub repository maoxx241/vllm-ascend-workspace (36 stars, last pushed 4d ago), licensed MIT. It adds 91 tokens to every session and 589 once invoked, about $0.0005 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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