Pretext Measurement

Category role · Version 5.3.1

Equation term Pretext = measure(text) where DOM ⊥ measurement

DOM-free text measurement technique for privacy-preserving browser interactions. Activates when discussing pretext discovery, shadow measurement, orb mechanics, text property extraction without DOM access, or browser-layer privacy.

Source: Privacy Value Model V5.2 + First Person Spellbook Act XXVIII (The Ceremony Engine)
Target context: Browser extension developers, privacy engineers, measurement specialists
Architecture: agentprivacy.ai · Sync: sync.soulbis.com · Contact: mage@agentprivacy.ai

What this is

Pretext measurement is a technique for extracting text properties without accessing DOM content. The measurement touches the shadow, not the surface. It knows the weight without reading the words.

The shadow tells you the height without climbing the tower. The orb measures the text without reading the words.

This enables privacy-preserving interactions where:

Text properties are needed (length, structure, patterns)
Text content must remain private
DOM access would leak information

The Measurement Problem

Traditional Approach (Privacy-Leaking)

// This reads the content
const text = document.getElementById('content').innerText;
const length = text.length;
const wordCount = text.split(' ').length;
// Content is now in memory, accessible, loggable

Pretext Approach (Privacy-Preserving)

// This measures without reading
const pretext = measurePretext(element);
// pretext.length exists
// pretext.wordCount exists
// pretext.content does NOT exist

Shadow Measurement Technique

Principle

The DOM casts shadows through:

Computed styles (width, height)
Scroll dimensions
Selection ranges
Intersection observations

These shadows reveal properties without exposing content.

Implementation Sketch

function measurePretext(element) {
  // Create measurement probe
  const probe = document.createElement('div');
  probe.style.cssText = `
    position: absolute;
    visibility: hidden;
    white-space: pre-wrap;
    font: inherit;
  `;

  // Mirror element's text styling
  const computed = getComputedStyle(element);
  probe.style.width = computed.width;
  probe.style.font = computed.font;
  probe.style.letterSpacing = computed.letterSpacing;

  // Measure shadow (scroll dimensions indicate content size)
  // WITHOUT reading innerText
  const metrics = {
    scrollHeight: element.scrollHeight,
    scrollWidth: element.scrollWidth,
    lineHeight: parseFloat(computed.lineHeight),
    charWidth: measureCharWidth(computed.font)
  };

  // Derive properties from shadow
  return {
    estimatedLines: Math.ceil(metrics.scrollHeight / metrics.lineHeight),
    estimatedChars: Math.ceil(metrics.scrollWidth / metrics.charWidth),
    hasOverflow: element.scrollHeight > element.clientHeight,
    // Content is NEVER accessed
  };
}

The Pretext Orb

The orb is the visual representation of pretext measurement:

     ╭──────────╮
    ╱   📊      ╲
   │  Metrics   │
   │  without   │
   │  Content   │
    ╲   🔮      ╱
     ╰──────────╯

Orb Properties

Property	Derived From	Content Access
`estimatedLength`	Scroll dimensions	No
`lineCount`	Height / line-height	No
`hasStructure`	DOM child count	No
`complexityScore`	Tag variety	No
`languageHint`	Direction, font features	No

Orb Limitations

The orb cannot determine:

Actual text content
Specific words
Semantic meaning
Named entities

This is by design. The limitation IS the feature.

Use Cases

1. Progressive Disclosure Check

Before revealing content, check if the reader has spent enough time:

const pretext = measurePretext(article);
const estimatedReadTime = pretext.estimatedChars / 200; // 200 wpm
const actualTimeSpent = getTimeOnPage();

if (actualTimeSpent >= estimatedReadTime * 0.8) {
  // Reader likely read the content
  unlockNextSection();
}

2. Mana Calculation

Mana earned depends on content engagement without reading content:

const pretext = measurePretext(spellbookPage);
const engagementScore = calculateEngagement(pretext, scrollDepth, timeSpent);
const manaEarned = engagementScore * MANA_RATE;

3. Ceremony Verification

Verify understanding ceremony without accessing submission:

const originalPretext = measurePretext(originalContent);
const responsePretext = measurePretext(userResponse);

// Check structural similarity without reading either
if (responsePretext.complexity >= originalPretext.complexity * 0.7) {
  // Response demonstrates engagement
  proceedToCeremony();
}

Security Properties

What Pretext Protects Against

Content logging by measurement code
Content exfiltration through metrics API
Side-channel content reconstruction (within limits)

What Pretext Does NOT Protect Against

Timing attacks on specific content patterns
High-precision scroll position analysis
Intersection observer abuse with fine targets

Threat Model

Pretext assumes:

Measurement code is potentially untrusted
Content must remain in DOM (visible to user)
Approximate properties are acceptable

Mapping to PVM-V5.2

Pretext Concept	PVM Term
Shadow measurement	Bounded leakage
Content isolation	Three-axis separation
Orb properties	Compressed representation
DOM perpendicularity	Φ_data separation
Engagement scoring	T_∫(π) contribution

Proverb

"The shadow tells you the height without climbing the tower. The orb measures the text without reading the words."

Emoji Spell

📐⊥DOM → 📏(text) → weight(shadow) → measure·¬touch → 🔮(orb)

Open Problems

Measurement Precision: How precise can shadow measurement get before it leaks content?
Cross-Browser Consistency: Do shadow measurements work consistently across browsers?
Dynamic Content: How to handle content that changes during measurement?
Adversarial Content: Can content be crafted to fool pretext measurement?
Formal Guarantees: Can we prove information-theoretic bounds on pretext leakage?

Verify: agentprivacy.ai · spellweb.ai · github.com/mitchuski/agentprivacy-docs

Provenance

Forkable skill page migrated from the agentprivacy skills repo (persona/agentprivacy-pretext-measurement, v5.3.1).
Source of truth: agentprivacy-skills. Fork this page to materialize a local SKILL.md via fedwiki-to-skill.

origin: 0xagentprivacy · author: Mitchell Travers

Assets

📎 pretext-measurementSKILL.md

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