---

name: factcheck description: Adversarial fact-checking and verification of LLM output against uploaded attachments using DeepCitation. Use when you need to verify claims, find contradictions, or perform devil's advocate analysis on AI-generated content. allowed-tools:

• Read
• Grep
• Glob
• Bash(node:, bun:, npx:*)

---

Fact-Check Skill

Perform adversarial verification of LLM-generated content against uploaded attachments using DeepCitation.

When to Use

• Verify claims in an AI response against attachments
• Find potential contradictions or unsupported statements
• Perform devil's advocate analysis
• Generate counter-arguments and check their evidence

Arguments

• --output <text|file> - The LLM output text to fact-check, OR a file path containing the output
• --sources <files...> - Source files to verify against (PDF, images, documents)
• --attachment-id <id> - (Optional) Use existing DeepCitation attachment ID instead of uploading files

Prerequisites

1. DeepCitation API key in environment (DEEPCITATION_API_KEY)

Workflow

When executing this skill, follow these steps:

Step 0: Upload Source Files

Read the source files and upload them to DeepCitation:

Copyconst dc = new DeepCitation({ apiKey: process.env.DEEPCITATION_API_KEY });
const { fileDataParts } = await deepcitation.prepareFiles([
  { file: readFileSync(sourcePath), filename: basename(sourcePath) }
]);
const attachmentId = fileDataParts[0].attachmentId;

Step 1: Extract Claims (You do this)

Analyze the LLM output and identify distinct factual claims:

• QUANTITATIVE claims (numbers, statistics)
• QUALITATIVE claims (descriptions, assessments)
• CAUSAL claims (cause-effect relationships)
• TEMPORAL claims (time-related assertions)

For each claim, note the exact text and extract an anchor text (1-3 important words).

Step 2: Verify Claims

For each claim, use DeepCitation to verify against the sources:

Copyconst result = await deepcitation.verify(attachmentId, {
  "claim-1": { fullPhrase: "exact claim text", anchorText: "key words" }
});
const status = getCitationStatus(result.verifications["claim-1"]);

Step 3: Generate Counter-Arguments (You do this)

For each claim, think adversarially and generate 2-3 counter-arguments:

• Alternative explanations for the same data
• Potential confounding factors
• Missing context that could change interpretation
• What search phrases would find evidence for these counter-arguments?

Step 4: Search for Counter-Evidence

For each counter-argument search phrase, verify against sources:

Copyconst counterResult = await deepcitation.verify(attachmentId, {
  "counter-1": { fullPhrase: searchPhrase, anchorText: searchPhrase.split(" ").slice(0,2).join(" ") }
});

Step 5: Calculate Confidence & Report

Score each claim based on verification status and counter-evidence found.

Output Format

Copy=== FACT-CHECK REPORT ===

CLAIM 1: "Revenue grew 45% year-over-year"
├── Verification: VERIFIED (found on page 2, lines 12-14)
├── Counter-Arguments:
│   ├── "Growth could be one-time acquisition effect"
│   │   └── Evidence: NOT FOUND
│   └── "Prior year may have been unusually low"
│       └── Evidence: PARTIAL MATCH (page 5)
└── Confidence: HIGH (85/100)

CLAIM 2: "Market share increased to 35%"
├── Verification: NOT FOUND
├── Counter-Arguments:
│   └── "Market definition may be narrow"
│       └── Evidence: FOUND (page 8, line 42)
└── Confidence: LOW (25/100)

=== SUMMARY ===
Total Claims: 5
Verified: 3 (60%)
Partially Verified: 1 (20%)
Unsupported: 1 (20%)
Counter-Evidence Found: 2 claims

Confidence Scoring

Confidence Levels:

• HIGH: 70-100 points
• MEDIUM: 40-69 points
• LOW: 0-39 points

Quick Start

Copy# Fact-check with source files (most common)
/factcheck --output ./llm-response.txt --sources ./report.pdf ./data.xlsx

# Fact-check inline text with source files
/factcheck --output "The company reported 45% revenue growth..." --sources ./annual-report.pdf

# With existing attachment ID (if already uploaded)
/factcheck --output ./response.txt --attachment-id abc123xyz

# Multiple source files
/factcheck --output ./summary.txt --sources ./doc1.pdf ./doc2.pdf ./chart.png

Integration Details

Uses DeepCitation library exports:

• DeepCitation.prepareFiles(files) - Upload source files, returns attachmentIds
• DeepCitation.verify(attachmentId, citations) - Verify claims against sources
• getAllCitationsFromLlmOutput(text) - Parse existing citations from LLM output
• getCitationStatus(verification) - Interpret verification results

For detailed workflow and prompt templates, see references/WORKFLOW.md.