Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for reviewers, research leads, and labs that need a more concrete first-pass integrity check on autoresearch submissions than a generic AI-detector or a manual skim can provide.
Who should use it
Who should skip it
Pass on wanshuiyin/Anti-Autoresearch if its scope or audience does not match what your team is building right now.
About this signal
wanshuiyin/Anti-Autoresearch is tracked by RepoRadar as a research audit in the Evaluation section. It was first seen on 2026-06-28 and last updated on 2026-06-28. The current verdict is 'worth watch' with a Silver tier and moderate setup difficulty. wanshuiyin/Anti-Autoresearch leads on open-source/build quality (8.4) and workflow potential (8.4); its lowest signal is maturity (5.8), so factor that in before investing setup time. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned wanshuiyin/Anti-Autoresearch a composite score of 8.0 out of 10, placing it in the Silver tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to read AI benchmarks without getting fooled for the checklist behind this score.
Risk explanation
The richest checks expect paper artifacts, code, or result files and can route that material through a cross-model reviewer setup, so confidential submissions should stay on approved review machines and declared observability levels; It is strongest at self-consistency and evidence forensics, not at proving misconduct or authorship, so findings still need human review before any public accusation.