S11-批判性审查

Audits literature cleaning results across six dimensions to detect systematic bias and misclassification.

Updated May 18, 2026
One-click install
npx skills add https://github.com/heyixue511-creator/ai --skill s11-heyixue511-creator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: S11-批判性审查
Source: https://github.com/heyixue511-creator/ai/tree/main/My-Own-Skills-main/%E6%96%87%E7%8C%AE%E8%AE%A1%E9%87%8F%E5%88%86%E6%9E%90%E6%8A%80%E8%83%BD/S11-%E6%89%B9%E5%88%A4%E6%80%A7%E5%AE%A1%E6%9F%A5
Command: npx skills add https://github.com/heyixue511-creator/ai --skill s11-heyixue511-creator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? After cleaning a bibliometric dataset, researchers cannot be sure whether noise records were wrongly kept or relevant papers wrongly excluded. This Skill performs a structured critical audit of the cleaning process and its verdicts, exposing systematic bias, logical gaps, and over-exclusion before the dataset is used for analysis. ## Core Features & Use Cases - Six-Dimension Review Framework: Checks rule consistency, false-positive risk, false-negative risk, rule soundness, research-goal alignment, and process traceability. - Expected vs Actual Comparison: Contrasts pre-cleaning noise predictions (from S05) with actual noise rates per concept group to surface systematic deviations. - Graded Quality Report: Produces a standardized audit report with an A/B/C/D quality rating and concrete remediation suggestions. - Use Case: After finishing round 3 of corpus cleaning, run a full audit that samples 20 NOISE and 20 RELEVANT verdicts, compares noise rates against expectations, and outputs a B-grade report recommending a targeted recheck of one noise type. ## Quick Start Run a full critical review of the round 3 cleaning results and generate the graded audit report.

Frequently Asked Questions about S11-批判性审查

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit the quality of a cleaned bibliometric dataset?▼

Run a structured review that randomly samples 5-10% of both excluded and retained records, checks verdict consistency across similar papers, and compares actual noise rates against pre-cleaning expectations. The process ends with a graded quality report.

How to detect false positives in literature screening?▼

Randomly sample 5-10% of records marked as noise and manually verify whether any are actually relevant. Also check borderline cases that moved from pending to excluded, since overly strict handling there is a common source of false positives.

What is the difference between quick review and deep review in data cleaning?▼

A quick review runs after each cleaning round: it tallies verdict counts, compares with prior rounds, and spot-checks about 40 records. A deep review runs after all cleaning finishes and applies the full six-dimension framework with a final A-to-D quality grade.

When should a cleaning round be redone instead of patched?▼

A redo is warranted when the audit finds fundamental problems, rated D-grade, such as contradictory rules applied across batches or large systematic deviation from expected noise rates. B or C grades only need documented caveats or targeted fixes.

Can this review work without pre-cleaning noise predictions?▼

The expected-versus-actual dimension depends on predictions from the earlier estimation step, but the other five dimensions work independently. Without predictions, that dimension is skipped and the remaining checks still produce a valid quality grade.