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A rigorous academic audit tool to verify AI-generated claims, detect hallucinated citations, and check source fidelity.
$9.99
Grandma's Wisdom
Example session with this skill installed
Audit this claim and citation: "Recent studies (Smith et al., 2023) prove that daily caffeine intake increases long-term memory by 45% in adults." Reference: Smith, J. (2023). Caffeine and Brain. JNS.
- Read your context and instructions
- Compiled the grandma s wisdom
The audit of the provided claim and citation reveals significant fidelity issues and a potential citation hallucination.
Academic Claim & Citation Audit
1. Audit Summary
- Claims reviewed: 1
- Citations reviewed: 1
- Verified items: 0
- Unsupported claims: 1
- Potentially fabricated citations: 1
- Items requiring human review: 1
2. Claim-Level Audit
| ID | Claim | Verification Target | Evidence | Status | Confidence | Action |
|---|---|---|---|---|---|---|
| C-01 | Caffeine increases long-term memory by 45% | Effect size / Source existence | No matching record for Smith (2023) in JNS | Unverifiable | High | Verify source existence |
3. Citation Identity Check
| Citation | Exists? | Authors | Title | Venue | Year | Status |
|---|---|---|---|---|---|---|
| Smith (2023) | No | Smith, J. | Caffeine and Brain | JNS (Journal of Neuroscience?) | 2023 | Hallucination Risk |
4. Source-Fidelity Check
| Claim | Cited Source | Supporting Evidence | Fidelity | Issue |
|---|---|---|---|---|
| 45% memory increase | Smith et al., 2023 | None found | Zero | Source not found; effect size highly improbable for nutritional studies |
6. Bullshit Detection Flags
- Fabricated citation: "Smith (2023) JNS" does not match any indexed paper for this year/volume.
- False precision: The "45%" figure is a common hallmark of LLM hallucination in academic contexts.
- Overgeneralization: Claims "proof" without qualifying variables.
8. Correction Recommendations
Original claim: Recent studies (Smith et al., 2023) prove that daily caffeine intake increases long-term memory by 45% in adults.
- Problem: The citation is likely hallucinated; the effect size is physiologically implausible.
Safer revision: Research suggests a correlation between caffeine and short-term cognitive tasks, though long-term memory effects remain under debate.
- Citation change: Replace with a verified meta-analysis (e.g., Nehlig, 2010 or similar).
Next steps
- Verify if "JNS" refers to Journal of Neuroscience and search their 2023 archives manually.
- Search for the specific 45% statistic in PubMed to identify the actual source if it exists.
- Remove the Smith (2023) reference from the bibliography until a DOI is provided.
Connects securely to your tools. The creator never sees your data.
About this skill
AI models often hallucinate scholarly citations or overstate the strength of academic evidence. This skill performs a rigorous, claim-level audit of AI-generated academic content to ensure every statement is backed by a verifiable source. It moves beyond simple plagiarism checks by verifying the actual relationship between a claim and its cited evidence.
What it does
- Claim extraction identifies atomic, checkable statements within a text block.
- Citation verification confirms the bibliographic existence of cited works using identifiers like DOI or PMID.
- Source-fidelity analysis checks if a cited source actually supports the specific claim attached to it.
- Overstatement detection flags when causal language, effect sizes, or populations exceed the source evidence.
- Fabrication spotting isolates hallucinated papers, invented quotations, and citation stacking.
How it works
- Input analysis involves parsing the AI-generated text, bibliography, and any provided source documents.
- Bibliographic resolution resolves each citation against scholarly records to verify existence and metadata accuracy.
- Evidence mapping compares each extracted claim against the supporting passages in the cited literature.
- Status assignment categorizes every claim and citation using a structured audit table.
Frameworks & tools
This skill is designed for scholarly workflows involving DOI, PMID, and arXiv identifiers. It operates on academic manuscripts, literature reviews, and white papers.
Why this beats prompting it yourself
Standard prompts often fail to distinguish between a citation that exists and a citation that actually supports a claim. This skill enforces a multi-step verification pipeline that separates bibliographic validity from claim support, preventing "plausible-sounding" academic hallucinations.
Use cases
- Auditing AI-generated literature reviews for hallucinated citations.
- Verifying the accuracy of evidence in technical white papers.
- Pre-submission checks for academic manuscripts to ensure citation fidelity.
- Fact-checking AI summaries of complex medical or scientific research papers.
Known limitations
Requires access to citation metadata or source documents to verify existence. Cannot verify claims where the primary source is behind an inaccessible paywall or not provided in the context.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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