SKILLEMALL.ai

AD perf-prof

使用perf-prof进行Linux系统问题分析。perf-prof是基于perf_event的系统级分析工具,事件在内存中实时处理,可长期运行。触发场景:(1) CPU使用率高、热点分析 (2) 进程状态异常(D/S状态多) (3) 延迟抖动、响应慢 (4) 内存泄露或增长异常 (5) 块设备IO慢 (6) 虚拟机性能问题 (7) 事件聚合统计 (8) 自定义脚本分析。核心分析器:profile(CPU采样)、task-state(进程状态)、multi-trace(延迟分析)、kmemleak(内存泄露)、blktrace(IO延迟)、top/sql(聚合统计)、kvm-exit(虚拟化退出)、rundelay(调度延迟)、syscalls(系统调用耗时)、python(自定义脚本分析)。适用于:性能问题定位、内核/应用开发调试、学习理解Linux内核机制(调度、内存、IO、中断等)。

ClawHub Agent Skills author: duane v1.0.1 MIT-0 23 files body ≈ 4 604 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 0

✓ No critical or high findings

Files scanned: 23. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 42/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Execution cost. Instruction body is 4604 tokens
  • 100Steps. 156 steps
  • 100Consistency. Name and required fields are in place

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

  • +5Description has no quoted example phrases that should trigger the skill
  • +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
  • +3Description length 400: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 156 items
  • +4Has examples (26 code blocks)
  • +4Reference files are cited in the instructions (4 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
The skill has a legitimate Linux performance-troubleshooting purpose, but it should be reviewed because it includes broad root-level tracing, kernel/user memory inspection, network trace sharing, and shell-command execution hooks without enough safety guardrails.
LLM: suspicious (high) · 5 Jun 2026