verify-claims

name	verify-claims
description	Extract every verifiable claim in marketing copy — statistics, rankings, awards, citations, performance and time-bound assertions — and check each against a user-supplied evidence JSON, classifying it verified, partially verified, unverified, or contradicted, with corrected-text and hedged-language suggestions. Without an evidence file it runs extraction-only and generates a template to fill in. Triggers on "/digital-marketing-pro:verify-claims", "fact-check this case study", "can we back up these numbers", "is this statistic real", "check our award claims before the press release". Runs claim-verifier.py and applies brand compliance rules for regulated industries; the claim-integrity deep-dive alongside /digital-marketing-pro:eval-content.
argument-hint	[content-path]

/digital-marketing-pro:verify-claims

Purpose

Cross-check marketing claims against user-provided evidence data. Extracts all verifiable claims from content — statistics, percentages, rankings, awards, certifications, named citations, performance metrics, customer counts, and time-bound assertions — then matches each against an evidence file and classifies it as verified, partially verified, unverified, or contradicted. This command is the dedicated deep-dive for claim integrity, while /digital-marketing-pro:eval-content includes claim verification as one dimension of its broader quality assessment.

Marketing content that cites specific numbers, awards, or results without verified backing is a brand risk. Contradicted claims erode trust if caught by customers, journalists, or regulators. This command ensures every factual assertion in your content is backed by real data, clearly sourced, and defensible under scrutiny.

Input Required

The user must provide (or will be prompted for):

• Content with claims: The text to verify — provided inline, as a pasted block, or as a file path. Any marketing content that makes factual assertions: landing pages, case studies, press releases, ad copy, pitch decks, investor materials, product pages, or client reports
• Evidence file (optional but strongly recommended): A JSON file containing source data to verify against. Format: [{"claim": "descriptive claim text", "source": "data source name or URL", "date": "YYYY-MM-DD when verified", "verified": true/false, "value": "the verified number or fact"}]. Can be exported from GA4, CRM, sales data, certification bodies, or assembled manually. If not provided, the command operates in extraction-only mode and guides the user on creating an evidence file
• Specific claim to check (optional): A single claim to focus on instead of scanning the full content — useful for quick spot-checks on a particular statistic or assertion

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply compliance rules for target markets (skills/context-engine/compliance-rules.md) — some industries and regions have stricter requirements for substantiating claims (financial services, healthcare, EU consumer protection). Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load messaging restrictions that may define approved claims and prohibited assertions. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. Extract claims from content: Execute python "${CLAUDE_PLUGIN_ROOT}/scripts/claim-verifier.py" --action extract-claims --text "{content}" to identify all verifiable assertions. The extractor categorizes claims by type:
   • Statistical claims: Percentages, ratios, growth numbers, market size figures ("73% increase in conversions," "4.8x ROAS")
   • Ranking claims: Position assertions, comparative statements ("market leader," "#1 rated," "fastest-growing")
   • Award and certification claims: Named awards, industry certifications, compliance badges ("ISO 27001 certified," "G2 Leader")
   • Named citation claims: Attributed quotes, source references, study citations ("according to Gartner," "Forrester reports")
   • Performance claims: Customer counts, time-bound results, SLA promises ("10,000+ customers," "results in 30 days")
   • Temporal claims: Date-specific assertions, recency claims ("as of 2026," "latest data shows")
3. Verify claims against evidence: If an evidence file is provided, execute python "${CLAUDE_PLUGIN_ROOT}/scripts/claim-verifier.py" --action verify --text "{content}" --evidence {evidence_file}. For each extracted claim, the verifier:
   • Attempts to match the claim to an evidence entry by semantic similarity and claim type
   • Classifies the match result: Verified (claim matches evidence within acceptable tolerance), Partially verified (claim is directionally correct but specific numbers differ, or the source date is stale), Unverified (no matching evidence entry found), or Contradicted (evidence directly conflicts with the claim)
   • Assigns a confidence score (0-100) to each verification based on match quality, source recency, and specificity alignment

Output

A structured verification report containing:

• Verification score: Percentage of claims that are verified or partially verified — the headline metric for content claim integrity
• Claims inventory: Total claims extracted, broken down by type (statistical, ranking, award, citation, performance, temporal) — showing the scope of factual assertions in the content
• Per-claim verification results: For each claim:
  • The claim text as it appears in the content, with location
  • Claim type classification
  • Verification status: Verified, Partially verified, Unverified, or Contradicted
  • Evidence match: The specific evidence entry it was checked against (if any), with source and date
  • Confidence score (0-100) for the verification
  • Issue details: For partially verified or contradicted claims, the specific discrepancy
  • Recommended action: Keep as-is, update with correct value, add source attribution, soften language, or remove
• Contradiction details: Side-by-side comparison for each contradicted claim — content text vs. evidence data, with the specific discrepancy highlighted and corrected text suggestion
• Unverified claims list: All claims with no matching evidence, grouped by risk level (high-risk: specific numbers and absolute statements; moderate-risk: comparative claims and rankings; low-risk: general qualitative assertions)
• Stale evidence warnings: Claims where the supporting evidence is older than 12 months, with a recommendation to refresh the data
• Corrected text suggestions: For each claim needing correction, the original text and one or more replacement options — an exact-value version using the evidence data and a hedged version with qualifier language
• Evidence file template: If no evidence file was provided, a pre-populated JSON template with all extracted claims ready for the user to fill in verified values and sources
• Compliance notes: If the brand operates in a regulated industry (financial services, healthcare, legal, insurance), specific regulatory requirements for claim substantiation with references to relevant rules (FTC Act, EU Consumer Rights Directive, ASA CAP Code, industry-specific regulations)

Agents Used

• quality-assurance — Claim extraction from content using pattern matching and NLP heuristics, claim-to-evidence matching with semantic similarity and type alignment, verification status classification with confidence scoring, contradiction detection with discrepancy quantification, correction text generation with hedging options, and compliance-aware risk assessment for regulated industries