بنقرة واحدة
citation-check
Verify citations, claims, and numbers before answering.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Verify citations, claims, and numbers before answering.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Render study flashcards into an interactive HTML deck.
Create interactive mind maps from notes or outlines.
Deliver interactive practice quizzes from study material.
Run the student exam prep workflow (mindmap → flashcards → quiz).
Run citation-check before delivering factual outputs.
Route requests to the right Open Exam Skills before responding.
| name | citation-check |
| description | Verify citations, claims, and numbers before answering. |
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.
Critical: Always use two separate passes. Never interleave extraction and verification.
[claim_id] | [claim_text] | [claim_type] | [location]This prevents "discovering new claims" mid-verification and ensures consistency.
Extract ONLY these claim types. Apply rules strictly — no judgment calls.
| 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 |
| 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 |
[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
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)
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: [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" |
| 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.
Run ALL applicable templates. Do not stop after first result.
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)
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"
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)
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"
Query 1: "[policy/law name] site:gov"
Query 2: "[statistic] official statistics [country]"
Query 3: "[claim] [agency name] report"
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) |
Rule: If only Rank 5-6 sources found, status = "Unverified" with note "Only secondary sources found"
A claim achieves "Verified" status only if:
| Condition | Sources Required |
|---|---|
| Primary source found | 1 (if authoritative: .gov, peer-reviewed, official) |
| Only secondary sources | ≥2 independent sources agreeing |
| Sources conflict | Status = "Unverified", note the conflict |
When uncertain, apply these rules. No judgment calls.
| Situation | Rule |
|---|---|
| Missing date on claim | Assume refers to most recent year available; flag "needs date" |
| Conflicting sources | Use most recent authoritative source; cite both; note conflict |
| Source not found after all queries | Status = "Unverified" (NOT "Hallucination") |
| Number differs due to currency conversion | Flag as "Needs clarification: currency/units" |
| Same org, multiple reports | Use most recent; cite with date |
| Claim uses "approximately" or "about" | Still verify base number is in valid range (±10% of source) |
| Source is paywalled | Note "Source behind paywall, unable to verify exact text" |
| Source is in different language | Translate and verify; note translation |
For every chart, graph, table, or diagram:
| Visual Element | Extracted Value | Source Value | Status |
|----------------|-----------------|--------------|--------|
| Bar 1 (2022) | 45% | 45.0% | ✓ Verified |
| Bar 2 (2023) | 62% | 58.3% | ✗ Numerical Error |
| Bar 3 (2024) | 78% | Not in source | ✗ Hallucination |
| Check | Issue Type |
|---|---|
| Y-axis starts at non-zero | "Visual Distortion: axis manipulation" |
| 3D effects distort proportions | "Visual Distortion: 3D exaggeration" |
| Missing error bars when source has them | "Misleading: uncertainty omitted" |
| Different time ranges than source | "Misleading: cherry-picked timeframe" |
Trigger phrases:
Build complete index before any verification:
SOURCE INDEX
Document: [filename]
Pages: [count]
Page 1:
- Text: [summary of content]
- Statistics: [list all numbers with context]
- Tables: [Table 1: columns X, Y, Z]
- Figures: [Figure 1: shows X]
Page 2:
...
Same as search mode, but verification uses ONLY the source index.
Claim: [C01] "Model achieves 92% accuracy"
Search index for: "92", "accuracy", "performance"
├─ Found: Section 4.2, p.8 — "Our model achieves 92.1% accuracy"
│ └─ Status: Numerical Error (92% vs 92.1%)
│
OR
│
├─ Not found in index
│ └─ Status: "Not in Source"
│ Issue: "This claim cannot be traced to the provided document"
│ Likely: External knowledge / hallucination
In doc-only mode, ANY claim not traceable to source = problem
### External Knowledge Detected
These claims are NOT in the provided document:
| Claim ID | Claim | Status | Issue |
|----------|-------|--------|-------|
| C07 | "This method is widely adopted in industry" | Not in Source | Appears to be from model training data |
| C12 | "Published in Nature 2024" | Not in Source | Publication venue not mentioned in source |
## Verification Report
**Mode:** [Search / Doc-Only]
**Document:** [filename or description]
**Generated:** [timestamp]
### Summary
| Metric | Count |
|--------|-------|
| Total claims extracted | X |
| Verified | Y |
| Numerical Error | Z |
| Unverified | A |
| Hallucination | B |
| Misleading | C |
| Not in Source (doc-only) | D |
**Overall Status:** [PASS: All verified / FAIL: Issues found]
### ✓ Verified Claims (N)
| ID | Claim | Source | Location | Confidence |
|----|-------|--------|----------|------------|
| C01 | "92.1% accuracy" | Chen et al. 2024 | Table 3, p.8 | exact |
### ✗ Numerical Errors (N)
| ID | Claim | Source Value | Claimed Value | Deviation | Fix |
|----|-------|--------------|---------------|-----------|-----|
| C03 | "97% accuracy" | 96.555% | 97% | +0.445% | Use 96.555% |
### ✗ Hallucinations (N)
| ID | Claim | Issue | Source Says |
|----|-------|-------|-------------|
| C05 | "3x faster" | Contradicts source | Source: 2.1x faster |
### ⚠ Unverified (N)
| ID | Claim | Issue |
|----|-------|-------|
| C08 | "$50B market" | No authoritative source found |
### ⚠ Misleading (N)
| ID | Claim | Issue | Missing Context |
|----|-------|-------|-----------------|
| C10 | "Best performance" | Cherry-picked metric | Only on subset; overall performance lower |
### Sources
| ID | Citation | Type | URL | Used For |
|----|----------|------|-----|----------|
| S1 | Chen et al. (2024) | arxiv | https://arxiv.org/... | C01, C02, C03 |
| S2 | Statista Market Report | report | https://statista.com/... | C08 |
Quick: Summary + critical issues only (Numerical Errors, Hallucinations, Unverified) Full: Complete traceability report with all claims JSON: Machine-readable audit (see references/citation_schema.json)
v2.0 — Consistency update