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ai output validator
by Jiangdongbo
Audit AI-generated drafts for hallucinations, logical errors, and factual inaccuracies with quantified scoring.
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See it in action
You say
Validate this summary: In 2025, Apple released the iPhone 20, which uses a battery that weighs only 1 gram, causing a 50% increase in global GDP.
Your agent does
📊 Credibility Score: 15/100
- Rating: High-Risk
🔍 Detailed Checklist
| # | Issue Type | Original Excerpt | Description | Severity | Suggestion | | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | Unknown Hallucination | "iPhone 20... in 2025" | Post-cutoff event; highly unlikely product numbering. | 🛑 High | Delete immediately; verify via web. | | 2 | Logical Fallacy | "weighs only 1 gram" | Physical implausibility for smartphone battery. | 🛑 High | Correct weight or unit. | | 3 | Causal Reasoning | "causing a 50% increase in global GDP" | Extreme correlation-as-causation error. | 🛑 High | Remove hyperbolic claim. |
✅ Final Verdict
- Usability: High-risk hallucination. Do not use.
- Revision priority: P0 (Rewrite entire summary).
What you get
About this skill
The problem
AI-generated content often suffers from "hallucinations," logical gaps, and factual errors that are difficult to spot manually. Relying on unverified AI drafts for professional reports or publications risks your reputation and spreads misinformation.
What it does
- Detects factual inaccuracies in data, dates, statistics, and entity names.
- Identifies logical fallacies, reversed causation, and internal contradictions.
- Flags potential hallucinations and information staleness based on a July 2024 knowledge cutoff.
- Produces a quantified credibility score (0-100) with prioritized (P0-P2) revision advice.
Why this beats prompting it yourself
General prompts like "check this for errors" often miss subtle logical inconsistencies or fail to quantify risk. This skill enforces a weighted triple-dimension audit (Factual, Logical, Timeliness) and produces a standardized QA report with specific severity levels, ensuring no category of error is overlooked.
Use cases
- Performing a pre-publication audit on AI-generated blog posts or whitepapers.
- Stress-testing logical arguments in project proposals or internal memos.
- Verifying technical specifications and data points in automated reports.
- Cross-lingual validation of translated drafts to ensure meaning and facts remain intact.
Known limitations
Knowledge cutoff is July 2024; events after this date require manual live web verification. Inputs over 5,000 words should be split to maintain accuracy.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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26+ total installs · 26 skills on Agensi
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