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AC research-integrity-audit

学术审查 / research-integrity screening of a manuscript's figures and reported numbers. Finds duplicated, reused, or transformed image panels and data anomalies: copied value blocks, fixed differences/ratios between groups, digit patterns, Benford deviations, GRIM/GRIMMER-inconsistent means and SDs, p-values mismatching their statistics. Use for 学术诚信, 图片查重, 论文图像重复, 数据造假筛查, Source Data 审查, 末位数字, 本福特, GRIM, statcheck, p 值核对, or when the user attaches a PDF, image directory, or CSV/Excel source data to audit.

xuzhougeng/wisp-science Agent Skills author: xuzhougeng AGPL-3.0 8 files · 4 scripts body ≈ 2 422 tokens Open the sourcegithub.com↗ analyzed 7 d ago

学术审查 / research-integrity screening of a manuscript's figures and reported numbers.

As a process C 62/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
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 62/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2422 tokens
    • low 10 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 507: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.