serenakeyitan/open-exam-skills/citation-check

Name: citation-check
Author: serenakeyitan

Claude Code and agent skill by serenakeyitan/open-exam-skills

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Checking that the SKILL.md file is still in the repository.

Verify citations, claims, and numbers before answering.

Install

npx vskill@latest install serenakeyitan/open-exam-skills/citation-check

SKILL.md

Citation & Hallucination Checker v2

Verification tool with vision + web search. Validates every claim against authoritative sources or provided documents. Works with content in any language.

Design principle: Deterministic verification. Same input → Same output.

Output Contract

Pass 1 outputs a numbered claim list in the required format.
Pass 2 outputs a verification report with status for every claim.
If any claim is unverified, hallucinated, or misquoted, block final output until fixed.

Two Verification Modes

Mode 1: Search Verification (Default)

Searches web for authoritative sources
Validates citations actually exist
Checks if cited sources say what's claimed
Finds original data for statistics

Mode 2: Doc-Only Verification

User provides source document(s)
EVERYTHING must trace to those docs
Flags anything that appears to come from external knowledge
Trigger: "only use this document" / "verify against the PDF only" / "don't search the web"

Two-Pass Architecture

Critical: Always use two separate passes. Never interleave extraction and verification.

Pass 1: Extraction Only

Read entire document/slides/images
Extract ALL claims using the Claim Extraction Rules below
Output numbered list: [claim_id] | [claim_text] | [claim_type] | [location]
NO verification in this pass
Present extraction to user for confirmation before proceeding

Pass 2: Verification Only

Take Pass 1 output as fixed input
Verify each claim_id in sequential order
NO re-extraction allowed — work only with Pass 1 claims
Apply Status Decision Tree to each claim
Generate final report

This prevents "discovering new claims" mid-verification and ensures consistency.

Claim Extraction Rules (Exhaustive)

Extract ONLY these claim types. Apply rules strictly — no judgment calls.

EXTRACT as claims:

Type	Pattern	Example
Statistic	Any number with unit/context (%, $, count, ratio, decimal)	"92.3% accuracy", "$4.7B market"
Comparative	X is [comparative] than Y	"3x faster than baseline"
Temporal	Time-bound assertion	"In 2024, adoption reached..."
Attribution	Claim tied to source	"According to WHO...", "Smith et al. found..."
Causal	X causes/leads to/results in Y	"This reduces latency by..."
Existence	Asserts something exists/is true	"There are 500M users", "The model supports..."
Ranking	Position claims	"largest", "first", "top 3"
Quote	Direct quotation	Any text in quotation marks attributed to source

DO NOT extract as claims:

Type	Example	Reason
Definitions	"Machine learning is a subset of AI"	Definitional, not factual claim
Opinions marked as such	"We believe...", "In our view..."	Explicitly subjective
Hypotheticals	"If adoption continues...", "Could potentially..."	Speculative
Questions	"What drives growth?"	Not an assertion
Future predictions without source	"Will reach $10B by 2030"	Unless citing a forecast report
Methodology descriptions	"We used PyTorch 2.0"	Process, not factual claim
Acknowledgments	"Thanks to our collaborators"	Not verifiable

Extraction Output Format

[C01] | "Model achieves 96.555% accuracy on ImageNet" | Statistic | Slide 3, bullet 2
[C02] | "Outperforms GPT-4 by 12% on reasoning tasks" | Comparative | Slide 3, bullet 3
[C03] | "According to Chen et al. (2024), transformers scale linearly" | Attribution | Slide 5, para 1
[C04] | "Market size reached $4.7B in 2024" | Statistic + Temporal | Slide 7, chart title

Status Decision Tree

Apply this tree to EVERY claim. Follow exactly — no shortcuts.

START
│
├─ Is this a CITATION claim (references a paper/report/source)?
│   ├─ YES → Go to CITATION VALIDATION
│   └─ NO → Go to STATISTIC/FACT VALIDATION
│
│
CITATION VALIDATION
│
├─ Step 1: Does the cited source exist?
│   │   Run ALL mandatory search queries (see Search Templates)
│   │
│   ├─ NO → Status: "Citation Not Found"
│   │        Issue: "Cannot locate [citation] in any database"
│   │        STOP
│   │
│   └─ YES → Step 2: Does source contain the claimed topic?
│             │
│             ├─ NO → Status: "Misquoted"
│             │        Issue: "Source exists but does not discuss [topic]"
│             │        STOP
│             │
│             └─ YES → Step 3: Does source support the exact claim?
│                       │
│                       ├─ YES (exact match) → Status: "Verified"
│                       │                       Confidence: "exact"
│                       │
│                       ├─ YES (paraphrase, same meaning) → Status: "Verified"
│                       │                                   Confidence: "paraphrase"
│                       │
│                       ├─ PARTIALLY (missing context) → Status: "Misleading"
│                       │   Issue: "Claim omits critical context: [what's missing]"
│                       │
│                       └─ NO (contradicts) → Status: "Hallucination"
│                           Issue: "Source says [X], claim says [Y]"
│
│
STATISTIC/FACT VALIDATION
│
├─ Step 1: Can you find an authoritative source?
│   │   Run ALL mandatory search queries (see Search Templates)
│   │
│   ├─ NO (no source found) → Status: "Unverified"
│   │                          Issue: "No authoritative source found"
│   │                          STOP
│   │
│   └─ YES → Step 2: Do values match EXACTLY?
│             │
│             ├─ YES → Status: "Verified"
│             │         Confidence: "exact"
│             │         STOP
│             │
│             └─ NO → Status: "Numerical Error"
│                      Go to NUMERICAL ERROR DETAILS
│
│
NUMERICAL ERROR DETAILS (Academic Precision Mode)
│
├─ Record:
│   • Source value: [exact number from source]
│   • Claimed value: [number in document being checked]
│   • Deviation: [calculate exact difference]
│   • Source location: [page, table, section]
│
├─ Classification:
│   • ANY rounding → Numerical Error
│   • ANY truncation → Numerical Error  
│   • Significant figures mismatch → Numerical Error
│   • Unit mismatch → Numerical Error
│   • Wrong direction (e.g., increase vs decrease) → Hallucination
│
└─ Exception: If source ITSELF provides rounded figure
    • e.g., Source says "96.555% (approximately 97%)"
    • Then claiming "97%" → Verified (cite the approximation)

Numerical Precision Rules (Academic Standard)

Default mode: Strict academic precision. Exact numbers only.

Rule	Source	Claim	Status
Exact match required	96.555%	96.555%	✓ Verified
Any rounding = error	96.555%	97%	✗ Numerical Error
Any rounding = error	96.555%	96.6%	✗ Numerical Error
Truncation = error	96.555%	96.5%	✗ Numerical Error
Sig figs must match	0.834	0.83	✗ Numerical Error
Units must match	96.555%	0.96555	✗ Numerical Error
Direction matters	+12% growth	+15% growth	✗ Hallucination
Order of magnitude	$4.7B	$47B	✗ Hallucination

Numerical Error Output Format

markdown

### Numerical Error: [Claim ID]
 
| Field | Value |
|-------|-------|
| Claim | "Model achieves 97% accuracy" |
| Location | Slide 4, bullet 2 |
| Source | Chen et al. (2024), Table 3, p.8 |
| Source value | 96.555% |
| Claimed value | 97% |
| Deviation | +0.445% (rounded up) |
| Status | Numerical Error |
| Fix | Replace with: "Model achieves 96.555% accuracy" |

Confidence Classification

Level	Criteria	Use when
exact	≥95% word overlap OR identical number with identical units	Direct quote, exact statistic
paraphrase	Same fact, different words, no interpretation added	Restated finding
interpretation	Inference drawn from source data	Calculated from source, synthesized

Rule: When uncertain between levels, use the MORE CONSERVATIVE option and flag for review.

Mandatory Search Templates

Run ALL applicable templates. Do not stop after first result.

For Academic Citations

Query 1: "[first author last name] [year] [first 3 words of title]"
Query 2: "[full paper title]" site:semanticscholar.org OR site:arxiv.org
Query 3: "[first author] [year] [venue/journal name]"
Query 4: "doi:[DOI]" (if DOI provided)
Query 5: "arxiv:[arxiv_id]" (if arXiv ID provided)

For Statistics (Market size, usage numbers, etc.)

Query 1: "[exact number with unit] [topic] [year]"
Query 2: "[topic] [year] statistics report site:statista.com"
Query 3: "[topic] [year] report site:mckinsey.com OR site:gartner.com"
Query 4: "[topic] market size [year] site:gov OR site:edu"
Query 5: "[topic] [number] original source"

For Company/Product Claims

Query 1: "[company name] [claim topic] press release [year]"
Query 2: site:[company domain] [claim topic]
Query 3: "[company name] [metric] official announcement"
Query 4: "[company name] [claim] SEC filing" (for public companies)

For Health/Medical Claims

Query 1: "[claim topic] site:who.int OR site:cdc.gov OR site:nih.gov"
Query 2: "[claim] systematic review site:cochrane.org"
Query 3: "[claim] meta-analysis pubmed"

For Government/Policy Claims

Query 1: "[policy/law name] site:gov"
Query 2: "[statistic] official statistics [country]"
Query 3: "[claim] [agency name] report"

Source Authority Hierarchy

When multiple sources found, prefer in this order:

Rank	Source Type	Examples
1	Primary source	Original study, official report, raw data
2	Government/institutional	WHO, CDC, World Bank, national statistics offices
3	Peer-reviewed publication	Nature, Science, IEEE, ACM
4	Industry reports (named)	Gartner, McKinsey, Statista (with methodology)
5	Reputable news citing primary	NYT, Reuters citing original source
6	Secondary compilations	Wikipedia (check their sources)

This is the start of the file. Read the full SKILL.md on GitHub.

Security

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Source

SKILL.md in the repositoryCheckingskills/citation-check/SKILL.md

Popularity

Repository stars: 139
Repository forks: 21
7-day trend score: 25

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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