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-profiling-collection/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-profiling-collection)<a href="https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/ascend-profiling-collection"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/ascend-profiling-collection/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-profiling-collection"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/ascend-profiling-collection.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 | $0.00131 | $0.03045 |
| Opus 5 | $0.00066 | $0.01522 |
| Sonnet 5 | $0.00026 | $0.00609 |
| Haiku 4.5 | $0.00013 | $0.00304 |
Grade A, and why
ascend-profiling-collection 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 7d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ascend Profiling Collection
Collect one torch-profiler case on a workspace-managed remote Ascend NPU container.
Remote substrate rule: use .remote-dev remote tools for ad hoc remote
read/edit/bash/search/patch work around profile setup and output inspection.
Use this skill for the domain collection workflow and keep its scripts as the
compatibility backend for managed VAWS sessions.
This skill is only about collection: start a profiled service, bracket a workload with /start_profile and /stop_profile, run torch_npu.profiler.profiler.analyse(...), verify the device-side data actually landed, and write a manifest. Interpreting the resulting kernel_details.csv is a separate concern owned by the analysis skill.
Use this skill when
- the user asks to collect / capture an Ascend torch-profiler trace for a specific config
- another skill (the analysis skill) needs a fresh profiling root with verified outputs
- the user wants to reproduce an existing root with a new model / mode / TP / DP
Do not use this skill when
- the task is performance benchmarking only — use
vllm-ascend-benchmark - the task is HBM / memory analysis — use
ascend-memory-profiling - the task is analysing an already-collected profiling root (no need to re-collect)
- the machine is not yet ready in inventory — use
machine-management
Boundary with other skills
| Skill | Owns | This skill uses it for |
|---|---|---|
vllm-ascend-serving |
Service lifecycle, --profiler-config passthrough |
serve_start.py / serve_stop.py only; serving is agnostic to the profiler window |
remote-code-parity |
Local-to-container code sync | Implicit — invoked by serve_start.py |
vllm-ascend-benchmark |
vllm bench serve performance numbers |
Not used; benchmark skill must not learn the profiler control plane |
ascend-memory-profiling |
HBM attribution via msprof | Independent; not invoked |
/start_profile and /stop_profile exist because of profiling, so the control-plane client lives here, not in vllm-ascend-serving.
What ships with it
7 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.
- 7d ago Changed · +1 lines 25f3b1681e11
- 11d ago First seen · 193 lines · 131 tokens per session scan A 7bb6c9d78cc0
ascend-profiling-collection is a skill published in the GitHub repository maoxx241/vllm-ascend-workspace (36 stars, last pushed 6d ago), licensed MIT. It adds 131 tokens to every session and 3,045 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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