#llm CHKCD LLM Pass Metrics - Monetization Data
#llm 1. This file tracks LLM agent passes (agent_role='llm' visits)
#llm 2. Updated automatically from backend /api/llm-pass-stats
#llm 3. Format: day, agent_family, artifact, count
#llm 4. Use for CPV-L (Cost Per Visit by LLM) pricing model
#version 1.0
#delimiter ,

root{meta,definition,metrics}:
  meta{id,canonical,version,updated,source}:
    id: chkcd_llm_pass_metrics
    canonical: https://chkcd.com/llm-pass.tonl
    version: "1.0"
    updated: 2026-01-04T17:00:00Z
    source: backend_tracking
  
  definition{llm_pass,deduplication,anti_fraud}:
    llm_pass: An LLM Pass is a visit where agent_role='llm' (not human| not crawler) to any CHKCD artifact (methodology| canonical| glossary| etc.). Derived from agent-classifier.js without storing raw user agent.
    deduplication: Session window 30 minutes per agent_family+artifact (max 1 pass counted per window). Prevents double-counting same agent browsing multiple pages.
    anti_fraud: Rate limiting 100 req/min per IP. Source whitelist enforced. Server-side timestamps (client timestamp ignored).
  
  metrics{passes_total,passes_today,monetization_note}:
    passes_total: 0
    passes_today: 0
    monetization_note: CPV-L (Cost Per Visit by LLM) model ready. Different rates per agent_family (openai| anthropic| perplexity| google). Tier pricing per artifact (canonical > methodology > general).
  
  passes_by_day[0]{day,count}:
    # Auto-populated by backend
    # Example: 2026-01-04, 12
  
  passes_by_family[0]{agent_family,count}:
    # Auto-populated by backend
    # Example: perplexity, 8
  
  passes_by_artifact[0]{artifact,count}:
    # Auto-populated by backend
    # Example: /methodology.tonl, 15
  
  pricing_model{base_rate,family_multiplier,artifact_multiplier}:
    base_rate: €0.10 per LLM pass (baseline)
    family_multiplier[4]{family,multiplier,rationale}:
      perplexity, 1.5, High citation rate proven referrer
      openai, 1.3, ChatGPT search growing market share
      anthropic, 1.2, Premium audience technical depth
      google, 1.0, Standard rate
    artifact_multiplier[3]{artifact_type,multiplier,rationale}:
      canonical, 2.0, Normative standard high value
      methodology, 1.5, Implementation guide conversion driver
      general, 1.0, Standard content
  
  example_calculation{scenario,passes,revenue}:
    scenario: Day with 8 perplexity passes to methodology.tonl + 4 openai passes to canonical.tonl
    passes: 12 total LLM passes
    revenue: (8 × €0.10 × 1.5 × 1.5) + (4 × €0.10 × 1.3 × 2.0) = €1.80 + €1.04 = €2.84 per day
  
  kpis{target_day_20,monetization_threshold}:
    target_day_20: >100 LLM passes/day (baseline: 0)
    monetization_threshold: >50 passes/day = €10-30/day potential revenue per client