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BB huawei-cloud-msot-msopprof-operator-profiler

Collect operator-level performance data on Ascend NPU using msopprof tool. Supports both device mode and simulator mode, generates performance analysis reports. Use this skill when the user wants to: (1) profile operator performance on Ascend NPU, (2) collect performance data for analysis, (3) identify performance bottlenecks through operator execution characteristics. Trigger: user mentions "msopprof", "profiler", "performance profiling", "operator profiling", "Ascend", "NPU", "profile data", "性能采集", "算子性能", "性能剖析", "算子耗时采集"

Not recommendedcritical or high security findings
ClawHub Agent Skills author: huaweicloud-skills-team v1.0.0 MIT-0 8 files body ≈ 1 508 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Collect operator-level performance data on Ascend NPU using msopprof tool.

As a process B 78/100 · Nearly there — weak spots: result and completion, progress reporting

ProcedureData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
77/100
safety, quality, tests
Safety 60%
67
Quality 40%
93
Run on models
none yet
Process rating
B
78/100
Nearly there
Progress reporting w 2
0
Result and completion w 14
40
Failures and branches w 10
50
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 4

  • high Dangerous commands cmd-disable-security references/troubleshooting.md:34
    Disables a security control (Defender, Gatekeeper, SIP, firewall, execution policy)
    setenforce 0  # Temporarily disable if needed
Medium and low: 3
  • medium Dangerous commands cmd-privilege references/troubleshooting.md:143
    Privilege escalation / world-writable permissions
    chmod 777 /tmp/profiling
  • medium Dangerous commands cmd-privilege skill-card.md:21
    Privilege escalation / world-writable permissions
    Mitigation: Avoid disabling SELinux except as a last-resort diagnostic step, prefer least-privilege permissions over chmod 777, and review each command before running it. <br>
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: bash
    allowed-tools: python3 msopprof bash

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

✓ No remarks against the Agent Skills spec

Process rating: all ten parameters 78/100

  • 0Progress reporting. Says nothing while it works
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 74 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1508 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +3Description length 531: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 74 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (3 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

External checks

ClawHub: suspicious
This profiling skill mostly matches its stated purpose, but it includes unsafe troubleshooting commands that can weaken host security or expose output files on shared systems.
LLM: suspicious (high) · 21 Jul 2026