SKILLEMALL.ai

AF engineering-drawing-parser

EN: Extract structured information from engineering drawings — mechanical 2D drafts, P&IDs, electrical schematics, construction architectural/structural drawings — into machine-readable BOM, dimension lists, tolerance tables, title-block metadata and symbol inventories. Supports DWG/DXF/PDF/image input. Use when the user provides a drawing and asks "解析图纸 / 抽 BOM / 读尺寸 / 出明细表 / parse drawing / extract BOM". 中文:从工程图纸(机械二维图、P&ID 工艺管道仪表流程图、电气原理图、建筑结构施工图)中抽取结构化信息,输出 BOM 物料清单、尺寸表、公差表、标题栏元数据、符号清单。支持 DWG/DXF/PDF/图片输入。当用户提供图纸并要求"解析/抽 BOM/读尺寸/出明细"时触发。

ClawHub Agent Skills author: boboy v1.0.0 MIT-0 14 files body ≈ 1 640 tokens Open the sourceclawhub.ai analyzed 22 h ago

EN: Extract structured information from engineering drawings — mechanical 2D drafts, P&IDs, electrical schematics, construction architectural/structural…

As a process F 53/100 · Will not run — References files that are not bundled: scripts/segment_regions.py

ProcedureExcelInfrastructureSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
F
53/100
Will not run
References files that are not bundled: scripts/segment_regions.py
Tools and files w 18
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. 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: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/segment_regions.py

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: scripts/segment_regions.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/segment_regions.py
  • 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
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 40 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1640 tokens
  • 100Running it twice. No mutating operations

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)
  • -32 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 547: enough signal without eating the budget
  • +4Structure: 21 headings
  • +3Step-by-step instructions: 40 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

External checks

ClawHub: clean
This skill locally parses user-provided engineering drawing text into structured output, with no evidence of uploads, credential access, persistence, or destructive behavior.
LLM: benign (high) · VirusTotal: · 28 May 2026