SKILLEMALL.ai

AB psych-questionnaire-cleaner

Clean, score, and audit psychology questionnaire or survey datasets with reproducible rules, participant-level quality flags, privacy protection, Chinese-language outputs, and traceable reports. Use for 心理学问卷、量表、调查数据的缺失值、异常编码、重复记录、作答质量、反向计分和分量表清洗;do not use to diagnose participants or invent an instrument's scoring key.

ClawHub Agent Skills author: Lin Liang v1.0.0 MIT-0 5 files body ≈ 2 292 tokens Open the sourceclawhub.ai analyzed 3 d ago

Clean, score, and audit psychology questionnaire or survey datasets with reproducible rules, participant-level quality flags, privacy protection…

As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerData and analyticsInfrastructureResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    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: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 68/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 5 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 43 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 2292 tokens
    • 100Progress reporting. Reports progress

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 321: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: clean
    This is a documentation-only data-cleaning skill whose sensitive behavior is disclosed, bounded to user-provided questionnaire datasets, and designed to preserve raw data with audit records.
    LLM: benign (high) · VirusTotal: · 27 Aug 2026