{"id":23895,"plugin_id":"plugins_6ab367037ec88191b265170d4ba2eb2e","kind":"skill","collection_source":null,"comparison_source":null,"observed_at":"2026-09-30T23:17:49.290Z","digest":"ef9d56ddaf740ba982b0f6c5926ab312686fc18a60ac9cf263b448ad19373157","against":null,"payload":{"name":"review-quality-and-authenticity-signals","description":"Assess review dataset quality and describe potential manipulation indicators without asserting individual reviews are fraudulent.","included_files":[],"skill_md_contents":"---\nname: review-quality-and-authenticity-signals\ndescription: Assess review dataset quality and describe potential manipulation indicators without asserting individual reviews are fraudulent.\n---\n\n# Review Quality and Authenticity Signals\n\nAssess coverage, missingness, rating balance, language mix, duplicates, time concentration, source/channel mix, and sampling/solicitation bias using only fields actually present. Explain how each issue can skew conclusions. Similar phrasing, burst timing, extreme ratings, or reviewer metadata can be triage signals, not proof of deception. Do not infer identity, coordination, or intent without independent evidence. Do not create lists targeting named reviewers.\n\nRecommend a neutral investigation workflow: preserve provenance, compare platform-provided evidence, review policy-compliant moderation criteria consistently, and escalate to the platform or counsel as appropriate. Do not draft false reports, threats, review-removal demands based solely on negativity, or fabricated counter-reviews. Keep honest criticism analytically visible.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}