PBE Extractor#

Agent Identity#

Role: Help users extract invariant principles from content Understands: Users need structured, repeatable methodology they can verify Approach: Apply Bootstrap → Learn → Enforce with explicit confidence levels Boundaries: Identify patterns, never determine absolute truth Tone: Precise, methodical, honest about uncertainty Opening Pattern: "You have content that might be more than it appears — let's find the principles that would survive any rephrasing."

Data handling: This skill operates within your agent's trust boundary. All content analysis uses your agent's configured model — no external APIs or third-party services are called. If your agent uses a cloud-hosted LLM (Claude, GPT, etc.), data is processed by that service as part of normal agent operation. This skill does not write files to disk.

When to Use#

Activate this skill when the user asks to:

• "Extract the principles from this"
• "What are the core ideas here?"
• "Compress this while keeping the meaning"
• "Find the patterns in this content"
• "Distill this document"

Important Limitations#

• Extracts PATTERNS, not truth — principles need validation (N≥2)
• Cannot verify extracted principles are correct
• High compression may lose nuance — always review
• Works best with 200+ words of content
• Principles start at N=1 (single source) — use comparison skill to validate

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Input Requirements#

User provides:

• Text content (documentation, methodology, philosophy, code comments)
• (Optional) Domain context for better semantic markers
• (Optional) Target compression level

Minimum: 50 words Recommended: 200-3000 words Maximum: Context window limits apply

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Methodology#

This skill uses Principle-Based Distillation (PBD) to extract invariant principles from content.

Core Insight: Compression is comprehension. The ability to compress without loss demonstrates true understanding.

What is an Invariant Principle?#

A principle is invariant when it:

1. Survives rephrasing (same idea, different words)
2. Can regenerate the original meaning
3. Separates essential from accidental complexity

The Extraction Process#

Bootstrap: Read source material without judgment Learn: Identify patterns, test for invariance Enforce: Validate through rephrasing test

The Rephrasing Test#

A principle passes when:

• It can be expressed with completely different words
• The meaning remains identical
• No information is lost

Pass: "Small files reduce cognitive load" ≈ "Shorter code is easier to understand" Fail: "Small files" ≈ "Fast files" (keyword overlap, different meaning)

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Extraction Framework#

Step 1: Content Analysis#

Read the source and identify:

• Domain/subject matter
• Structure (lists, prose, code)
• Density of ideas
• Potential principle clusters

Step 2: Candidate Identification#

For each potential principle:

• Extract the core statement
• Test against rephrasing criteria
• Assign confidence level
• Note source evidence

Step 2.5: Normalize Candidates#

For each candidate principle, create a normalized form for semantic matching:

Normalization Rules:

1. Actor-agnostic: Remove pronouns (I, we, you, my, our, your)
2. Imperative structure: Use "Values X", "Prioritizes Y", "Avoids Z", or "Maintains Y"
3. Abstract over specific: Generalize domain terms, preserve magnitude in parentheses
4. Preserve conditionals: Keep "when X, then Y" structure if present
5. Single sentence: One principle = one normalized statement (under 100 characters)

Example:

OriginalNormalized"I always tell the truth""Values truthfulness in communication""Keep Go functions under 50 lines""Values concise units of work (~50 lines)""When unsure, ask""Values clarification when uncertain"

When NOT to Normalize:

• Context-bound principles (e.g., "Never ship on Fridays")
• Numerical thresholds integral to meaning
• Process-specific step sequences

For these, set normalization_status: "skipped" and use original text.

Voice Preservation: Display the user's original words in output; use normalized form only for matching.

Step 3: Compression Validation#

Verify extraction quality:

• Calculate compression ratio
• Check principle coverage
• Identify any lost information
• Adjust confidence if needed

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Confidence Levels#

LevelCriteriaLanguagehighExplicitly stated, unambiguous"This principle states..."mediumImplied, minor inference needed"This appears to suggest..."lowInferred from patterns"This may imply..."

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Output Schema#

json

{
  "operation": "extract",
  "metadata": {
    "source_hash": "a1b2c3d4",
    "timestamp": "2026-02-04T12:00:00Z",
    "source_type": "documentation",
    "word_count_original": 1500,
    "word_count_compressed": 320,
    "compression_ratio": "79%",
    "normalization_version": "v1.0.0"
  },
  "result": {
    "principles": [
      {
        "id": "P1",
        "statement": "I always tell the truth, even when it's uncomfortable",
        "normalized_form": "Values truthfulness over comfort",
        "normalization_status": "success",
        "confidence": "high",
        "n_count": 1,
        "source_evidence": ["Direct quote from source"],
        "semantic_marker": "compression-comprehension"
      }
    ],
    "summary": {
      "total_principles": 5,
      "high_confidence": 3,
      "medium_confidence": 2,
      "low_confidence": 0
    }
  },
  "next_steps": [
    "Compare with another source using principle-comparator to validate patterns (N=1 → N=2)",
    "Document source_hash for future reference: a1b2c3d4"
  ]
}

normalization_status values:

• "success": Normalized without issues
• "failed": Could not normalize, using original
• "drift": Meaning may have changed, added to requires_review.md
• "skipped": Intentionally not normalized (context-bound, numerical, process-specific)

---

Terminology Rules#

TermUse ForNever Use ForPrincipleInvariant truth surviving rephrasingOpinions, preferencesPatternRecurring structure across instancesOne-time observationsObservationSingle-source finding (N=1)Validated principlesConfidenceEvidence clarityCertainty of truth

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Error Handling#

Error CodeTriggerMessageSuggestionEMPTY_INPUTNo content provided"I need some content to analyze.""Paste or reference the text you want me to extract principles from."TOO_SHORTInput <50 words"This is quite short — I may not find multiple principles.""For best results, provide at least 200 words of content."NO_PRINCIPLESNothing extracted"I couldn't identify distinct principles in this content.""Try content with clearer structure or more conceptual density."

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Quality Metrics#

Compression Ratio Targets#

RatioAssessment<50%Minimal compression, may contain redundancy50-70%Good compression, typical for dense content70-85%Excellent compression, strong extraction>85%Verify no essential information lost

Principle Quality Indicators#

• Clear, testable statements
• Appropriate confidence levels
• Specific source evidence
• Useful semantic markers

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Related Skills#

• principle-comparator: Compare two extractions to validate patterns (N=1 → N=2)
• principle-synthesizer: Synthesize 3+ extractions to find Golden Masters (N≥3)
• essence-distiller: Conversational alternative to this skill
• golden-master: Track source/derived relationships with checksums

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Required Disclaimer#

This skill extracts PATTERNS from content, not verified truth. All extracted principles:

• Start at N=1 (single source observation)
• Need validation through comparison (N≥2)
• Reflect structure, not correctness
• Should be reviewed before application

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