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CD multi-source-data-cleaner

(no description)

ClawHub Agent Skills author: YK-Global v1.0.0 MIT-0 17 files body ≈ 1 058 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
C
71/100
safety, quality, tests
Safety 60%
100
Quality 40%
28
Run on models
none yet
Process rating
D
39/100
Unfinished process
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Add a description to the frontmatter: without it the skill never triggers.
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

  • error description-missing SKILL.md: no `description` — the skill can never trigger
  • note frontmatter-key unknown frontmatter key "label"
  • note frontmatter-key unknown frontmatter key "language"
  • note frontmatter-key unknown frontmatter key "runtime"
  • note frontmatter-key unknown frontmatter key "trigger_words"

Process rating: all ten parameters 39/100

  • 0Result and completion. Does not say what the result is
  • 0When it triggers. No condition that starts the skill
  • 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
  • 40Consistency. Frontmatter name (multi-source-data-cleaner) differs from the folder (data-cleaner)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 22 steps
  • 100Execution cost. Instruction body is 1058 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 0: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -236 emoji in the instructions: noise for the model
  • -31 of 10 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 28.

External checks

ClawHub: suspicious
This data-cleaning skill appears legitimate, but it can send dataset-derived content to AI providers and Feishu despite claiming processing is local.
LLM: suspicious (high) · VirusTotal: benign · 27 May 2026