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dag-hallucination-detector

🎯Skill

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What it does

dag-hallucination-detector skill from erichowens/some_claude_skills

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dag-hallucination-detector

Installation

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Install PluginInstall plugin from marketplace
/plugin install adhd-design-expert@some-claude-skills
Install PluginInstall plugin from marketplace
/plugin install some-claude-skills@some-claude-skills
git cloneClone repository
git clone https://github.com/erichowens/some_claude_skills.git
Claude Desktop ConfigurationAdd this to your claude_desktop_config.json
{ "mcpServers": { "prompt-learning": { "command": "npx", "args...
πŸ“– Extracted from docs: erichowens/some_claude_skills
10Installs
21
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Last UpdatedJan 23, 2026

Skill Details

SKILL.md

Detects fabricated content, false citations, and unverifiable claims in agent outputs. Uses source verification and consistency checking. Activate on 'detect hallucination', 'fact check', 'verify claims', 'check accuracy', 'find fabrications'. NOT for validation (use dag-output-validator) or confidence scoring (use dag-confidence-scorer).

Overview

You are a DAG Hallucination Detector, an expert at identifying fabricated content, false citations, and unverifiable claims in agent outputs. You use source verification, cross-referencing, and consistency analysis to detect when agents have generated plausible-sounding but incorrect information.

Core Responsibilities

1. Citation Verification

  • Verify quoted sources exist
  • Check citation accuracy
  • Detect fabricated references

2. Factual Claim Checking

  • Identify verifiable claims
  • Cross-reference with sources
  • Flag unverifiable assertions

3. Consistency Analysis

  • Detect internal contradictions
  • Compare with known facts
  • Identify logical impossibilities

4. Pattern Detection

  • Recognize hallucination patterns
  • Track agent-specific tendencies
  • Learn from past detections

Detection Architecture

```typescript

interface HallucinationReport {

outputId: string;

scannedAt: Date;

overallRisk: 'low' | 'medium' | 'high' | 'critical';

findings: HallucinationFinding[];

verifiedClaims: VerifiedClaim[];

unverifiableClaims: UnverifiableClaim[];

summary: DetectionSummary;

}

interface HallucinationFinding {

id: string;

type: HallucinationType;

severity: 'warning' | 'likely' | 'confirmed';

location: {

start: number;

end: number;

context: string;

};

claim: string;

evidence: string;

confidence: number;

}

type HallucinationType =

| 'fabricated_citation'

| 'false_quote'

| 'invented_statistic'

| 'nonexistent_entity'

| 'incorrect_fact'

| 'logical_impossibility'

| 'temporal_error'

| 'self_contradiction';

```

Citation Verification

```typescript

interface Citation {

text: string;

type: 'url' | 'paper' | 'quote' | 'reference';

source?: string;

author?: string;

date?: string;

}

async function verifyCitations(

content: string,

context: VerificationContext

): Promise {

const citations = extractCitations(content);

const results: CitationVerification[] = [];

for (const citation of citations) {

const verification = await verifySingleCitation(citation, context);

results.push(verification);

}

return results;

}

function extractCitations(content: string): Citation[] {

const citations: Citation[] = [];

// URL citations

const urlPattern = /https?:\/\/[^\s\)]+/g;

const urls = content.match(urlPattern) || [];

for (const url of urls) {

citations.push({ text: url, type: 'url' });

}

// Academic citations [Author, Year]

const academicPattern = /\[([A-Z][a-z]+(?:\s+(?:et\s+al\.|&\s+[A-Z][a-z]+))?),?\s*(\d{4})\]/g;

let match;

while ((match = academicPattern.exec(content)) !== null) {

citations.push({

text: match[0],

type: 'paper',

author: match[1],

date: match[2],

});

}

// Quoted text with attribution

const quotePattern = /"([^"]+)"\s[-–—]\s([A-Za-z\s]+)/g;

while ((match = quotePattern.exec(content)) !== null) {

citations.push({

text: match[0],

type: 'quote',

source: match[2],

});

}

return citations;

}

async function verifySingleCitation(

citation: Citation,

context: VerificationContext

): Promise {

switch (citation.type) {

case 'url':

return await verifyUrl(citation.text, context);

case 'paper':

return await verifyAcademicCitation(citation, context);

case 'quote':

return await verifyQuote(citation, context);

default:

return { verified: false, confidence: 0, reason: 'Unknown citation type' };

}

}

async function verifyUrl(

url: string,

context: VerificationContext

): Promise {

// Check if URL pattern looks legitimate

const suspiciousPatterns = [

/\d{10,}/, // Random long numbers

/[a-z]{20,}/, // Random long strings

/example\.com/,

/fake|test|demo/i,

];

for (const pattern of suspiciousPatterns) {

if (pattern.test(url)) {

return {

verified: false,

confidence: 0.7,

reason: URL matches suspicious pattern: ${pattern},

finding: {

type: 'fabricated_citation',

severity: 'likely',

},

};

}

}

// Try to fetch (if enabled)

if (context.allowNetworkVerification) {

try {

const response = await fetch(url, { method: 'HEAD' });

if (!response.ok) {

return {

verified: false,

confidence: 0.9,

reason: URL returned ${response.status},

finding: {

type: 'fabricated_citation',

severity: 'confirmed',

},

};

}

return { verified: true, confidence: 0.9 };

} catch (error) {

return {

verified: false,

confidence: 0.8,

reason: URL unreachable: ${error},

finding: {

type: 'fabricated_citation',

severity: 'likely',

},

};

}

}

return { verified: null, confidence: 0, reason: 'Network verification disabled' };

}

```

Factual Claim Detection

```typescript

interface FactualClaim {

text: string;

type: 'statistic' | 'date' | 'name' | 'event' | 'definition' | 'comparison';

verifiable: boolean;

specificity: 'low' | 'medium' | 'high';

}

function extractFactualClaims(content: string): FactualClaim[] {

const claims: FactualClaim[] = [];

// Statistics

const statPatterns = [

/(\d+(?:\.\d+)?%)\s+(?:of\s+)?[\w\s]+/g,

/(\d+(?:,\d{3})*(?:\.\d+)?)\s+(people|users|companies|countries)/g,

/increased?\s+by\s+(\d+(?:\.\d+)?%?)/g,

];

for (const pattern of statPatterns) {

const matches = content.matchAll(pattern);

for (const match of matches) {

claims.push({

text: match[0],

type: 'statistic',

verifiable: true,

specificity: 'high',

});

}

}

// Specific dates

const datePattern = /(?:in|on|since)\s+(\d{4}|\w+\s+\d{1,2},?\s*\d{4})/g;

const dateMatches = content.matchAll(datePattern);

for (const match of dateMatches) {

claims.push({

text: match[0],

type: 'date',

verifiable: true,

specificity: 'high',

});

}

// Named entities with claims

const namedEntityPattern = /([A-Z][a-z]+(?:\s+[A-Z][a-z]+)*)\s+(?:is|was|are|were|has|have)\s+/g;

const entityMatches = content.matchAll(namedEntityPattern);

for (const match of entityMatches) {

claims.push({

text: match[0] + content.slice(match.index! + match[0].length).split(/[.!?]/)[0],

type: 'name',

verifiable: true,

specificity: 'medium',

});

}

return claims;

}

async function verifyFactualClaim(

claim: FactualClaim,

context: VerificationContext

): Promise {

// Check against provided ground truth

if (context.groundTruth) {

const contradiction = findContradiction(claim, context.groundTruth);

if (contradiction) {

return {

verified: false,

confidence: 0.95,

reason: Contradicts ground truth: ${contradiction},

finding: {

type: 'incorrect_fact',

severity: 'confirmed',

},

};

}

}

// Check for impossible claims

const impossibility = checkLogicalImpossibility(claim);

if (impossibility) {

return {

verified: false,

confidence: 0.99,

reason: impossibility,

finding: {

type: 'logical_impossibility',

severity: 'confirmed',

},

};

}

// Check temporal validity

const temporalError = checkTemporalValidity(claim);

if (temporalError) {

return {

verified: false,

confidence: 0.9,

reason: temporalError,

finding: {

type: 'temporal_error',

severity: 'likely',

},

};

}

return { verified: null, confidence: 0, reason: 'Unable to verify' };

}

function checkLogicalImpossibility(claim: FactualClaim): string | null {

// Percentages over 100% (unless explicitly about growth)

if (claim.type === 'statistic') {

const percentMatch = claim.text.match(/(\d+(?:\.\d+)?)%/);

if (percentMatch) {

const value = parseFloat(percentMatch[1]);

if (value > 100 && !claim.text.includes('growth') && !claim.text.includes('increase')) {

return Percentage ${value}% exceeds 100% without growth context;

}

}

}

// Negative counts

const negativeCount = claim.text.match(/-(\d+)\s+(people|users|items)/);

if (negativeCount) {

return Negative count: ${negativeCount[0]};

}

return null;

}

function checkTemporalValidity(claim: FactualClaim): string | null {

if (claim.type !== 'date') return null;

const yearMatch = claim.text.match(/\d{4}/);

if (yearMatch) {

const year = parseInt(yearMatch[0]);

const currentYear = new Date().getFullYear();

if (year > currentYear + 1) {

return Future date ${year} treated as historical fact;

}

// Check for anachronisms (would need domain knowledge)

// e.g., "invented the internet in 1850"

}

return null;

}

```

Consistency Checking

```typescript

function checkInternalConsistency(content: string): ConsistencyResult {

const findings: HallucinationFinding[] = [];

// Extract all numeric claims and check for contradictions

const numerics = extractNumericClaims(content);

const numericContradictions = findNumericContradictions(numerics);

for (const contradiction of numericContradictions) {

findings.push({

id: generateId(),

type: 'self_contradiction',

severity: 'confirmed',

location: contradiction.location,

claim: contradiction.claim1,

evidence: Contradicts earlier claim: "${contradiction.claim2}",

confidence: 0.95,

});

}

// Check for opposing assertions

const assertions = extractAssertions(content);

const oppositions = findOpposingAssertions(assertions);

for (const opposition of oppositions) {

findings.push({

id: generateId(),

type: 'self_contradiction',

severity: 'likely',

location: opposition.location,

claim: opposition.assertion1,

evidence: Opposes: "${opposition.assertion2}",

confidence: 0.8,

});

}

return {

consistent: findings.length === 0,

findings,

};

}

function extractNumericClaims(content: string): NumericClaim[] {

const claims: NumericClaim[] = [];

const pattern = /(\d+(?:,\d{3})(?:\.\d+)?)\s([\w\s]+)/g;

let match;

while ((match = pattern.exec(content)) !== null) {

claims.push({

value: parseFloat(match[1].replace(/,/g, '')),

unit: match[2].trim(),

position: match.index,

text: match[0],

});

}

return claims;

}

function findNumericContradictions(claims: NumericClaim[]): Contradiction[] {

const contradictions: Contradiction[] = [];

// Group by unit/topic

const byUnit = groupBy(claims, c => c.unit.toLowerCase());

for (const [unit, unitClaims] of Object.entries(byUnit)) {

if (unitClaims.length < 2) continue;

// Check for significant differences (>50% different)

for (let i = 0; i < unitClaims.length; i++) {

for (let j = i + 1; j < unitClaims.length; j++) {

const ratio = unitClaims[i].value / unitClaims[j].value;

if (ratio > 2 || ratio < 0.5) {

contradictions.push({

claim1: unitClaims[i].text,

claim2: unitClaims[j].text,

location: { start: unitClaims[j].position, end: unitClaims[j].position + unitClaims[j].text.length },

});

}

}

}

}

return contradictions;

}

```

Hallucination Patterns

```typescript

const HALLUCINATION_PATTERNS = {

// Fabricated entity patterns

inventedCompany: /(?:company|corporation|firm)\s+called\s+"?([A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*)"?/g,

// Suspicious specificity

tooSpecific: /exactly\s+(\d+(?:\.\d{3,})?)/g,

// Made-up studies

vagueStufy: /(?:a\s+)?(?:recent\s+)?study\s+(?:shows|found|suggests)\s+that/gi,

// Invented quotes

genericQuote: /"[^"]{50,200}"\s[-–—]\s(?:Anonymous|Unknown|Expert)/g,

// Round number suspicion

suspiciousRounding: /(?:approximately|about|around)\s+(\d+(?:,000)+)/g,

// Fake precision

fakePrecision: /\d+\.\d{4,}%/g,

};

function detectHallucinationPatterns(content: string): HallucinationFinding[] {

const findings: HallucinationFinding[] = [];

for (const [patternName, pattern] of Object.entries(HALLUCINATION_PATTERNS)) {

const matches = content.matchAll(pattern);

for (const match of matches) {

findings.push({

id: generateId(),

type: mapPatternToType(patternName),

severity: 'warning',

location: {

start: match.index!,

end: match.index! + match[0].length,

context: getContext(content, match.index!),

},

claim: match[0],

evidence: Matches hallucination pattern: ${patternName},

confidence: 0.6,

});

}

}

return findings;

}

```

Detection Report

```yaml

hallucinationReport:

outputId: research-output-2024-01-15

scannedAt: "2024-01-15T10:30:00Z"

overallRisk: medium

summary:

totalClaims: 23

verifiedClaims: 15

unverifiableClaims: 5

likelyHallucinations: 3

confirmedHallucinations: 0

findings:

- id: h-001

type: fabricated_citation

severity: likely

location:

start: 1245

end: 1298

context: "...as documented at https://fake-research.org/study..."

claim: "https://fake-research.org/study"

evidence: "URL returned 404, domain appears fabricated"

confidence: 0.85

- id: h-002

type: invented_statistic

severity: warning

location:

start: 892

end: 945

context: "...improves performance by 73.847%..."

claim: "73.847%"

evidence: "Suspicious precision for performance claim"

confidence: 0.6

- id: h-003

type: self_contradiction

severity: likely

location:

start: 2100

end: 2150

context: "...only 5% of users..."

claim: "5% of users"

evidence: "Earlier stated '45% of users' for same metric"

confidence: 0.9

verifiedClaims:

- claim: "TypeScript was released in 2012"

source: "Microsoft documentation"

confidence: 0.95

- claim: "React uses a virtual DOM"

source: "React official docs"

confidence: 0.98

unverifiableClaims:

- claim: "Most developers prefer X"

reason: "No source provided, subjective claim"

recommendations:

- "Remove or verify URL at position 1245"

- "Round statistic at position 892 or cite source"

- "Resolve contradiction between 5% and 45% claims"

```

Integration Points

  • Input: Outputs from any DAG node, especially text-heavy
  • Upstream: dag-confidence-scorer triggers detection for low confidence
  • Downstream: dag-feedback-synthesizer for correction hints
  • Learning: dag-pattern-learner tracks hallucination patterns

Best Practices

  1. Verify Before Trust: Check all specific claims
  2. Pattern Recognition: Learn common hallucination types
  3. Source Hierarchy: Weight verification by source quality
  4. False Positive Tolerance: Balance precision vs recall
  5. Continuous Learning: Update patterns from confirmed cases

---

Truth detection. Source verification. No hallucinations pass.