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杰峰开放平台通用AI算法调用助手(开发版)。交互式引导用户明确检测场景(如明厨亮灶、安全生产、消防安全、门店巡检、周界安防),从52个免底库算法清单中推荐适配算法(不含需底库的比对类算法),支持对本地图片/图片URL/摄像头设备抓图(配合 jf-open-pro-capture 技能)调用算法分析并输出结构化检测报告。Use when the user mentions 明厨亮灶、AI检测、算法调用、图片分析、摄像头智能分析、口罩/安全帽/烟火/垃圾检测、开放平台算法、jf算法、检测服务。

ClawHub Agent Skills author: jftech v1.0.0 MIT-0 6 files body ≈ 2 014 tokens Open the sourceclawhub.ai analyzed 2 d ago

杰峰开放平台通用AI算法调用助手(开发版)。交互式引导用户明确检测场景(如明厨亮灶、安全生产、消防安全、门店巡检、周界安防),从52个免底库算法清单中推荐适配算法(不含需底库的比对类算法),支持对本地图片/图片URL/摄像头设备抓图(配合 jf-open-pro-capture…

As a process F 39/100 · Will not run — References files that are not bundled: 路径

ProcedureInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
F
39/100
Will not run
References files that are not bundled: 路径
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: 路径

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: 路径
  • 0Tools and files. 1 referenced file(s) missing: 路径
  • 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
  • 30Running it twice. 3 mutating operations with no state check
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 41 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2014 tokens
  • low 12 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

  • +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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 247: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 41 items
  • +4Has examples (11 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
The skill is mostly coherent, but it needs Review because it can send surveillance images to a third-party API, store API secrets locally, and install another skill from an unpinned remote repository.
LLM: suspicious (high) · 27 Aug 2026