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BD 现场不良数据分析助手

制造业质量现场数据分析；当用户需要分析车间巡检、制程不良、成品检验台账数据或处理现场不合格记录时使用；覆盖数据清洗、统计分析、图表可视化

ClawHub Agent Skills author: engicool v0.1.0 MIT-0 9 files body ≈ 856 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

制造业质量现场数据分析；当用户需要分析车间巡检、制程不良、成品检验台账数据或处理现场不合格记录时使用；覆盖数据清洗、统计分析、图表可视化

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureQuality controltype 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
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
65
Run on models
none yet
Process rating
D
41/100
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: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"

Process rating: all ten parameters 41/100

  • 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
  • 40Consistency. Frontmatter name (现场不良数据分析助手) differs from the folder (skill-manufacturing-quality-data-analysis)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 72 steps
  • 100Execution cost. Instruction body is 856 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 68: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 72 items
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.

External checks

ClawHub: clean
This skill performs the manufacturing quality data analysis it advertises, with local parsing and report generation, but users should be aware that generated reports may load an external Google font.
LLM: benign (high) · VirusTotal: · 17 Jul 2026