PBE Extractor

Extract invariant principles from any text — find the ideas that survive rephrasing.

Install

openclaw skills install pbe-extractor

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

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

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:

Survives rephrasing (same idea, different words)
Can regenerate the original meaning
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)

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:

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

Example:

Original	Normalized
"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

Confidence Levels

Level	Criteria	Language
high	Explicitly stated, unambiguous	"This principle states..."
medium	Implied, minor inference needed	"This appears to suggest..."
low	Inferred from patterns	"This may imply..."

Output Schema

{
  "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

Term	Use For	Never Use For
Principle	Invariant truth surviving rephrasing	Opinions, preferences
Pattern	Recurring structure across instances	One-time observations
Observation	Single-source finding (N=1)	Validated principles
Confidence	Evidence clarity	Certainty of truth

Error Handling

Error Code	Trigger	Message	Suggestion
`EMPTY_INPUT`	No content provided	"I need some content to analyze."	"Paste or reference the text you want me to extract principles from."
`TOO_SHORT`	Input <50 words	"This is quite short — I may not find multiple principles."	"For best results, provide at least 200 words of content."
`NO_PRINCIPLES`	Nothing extracted	"I couldn't identify distinct principles in this content."	"Try content with clearer structure or more conceptual density."

Quality Metrics

Compression Ratio Targets

Ratio	Assessment
<50%	Minimal compression, may contain redundancy
50-70%	Good compression, typical for dense content
70-85%	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

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

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

Built by Obviously Not — Tools for thought, not conclusions.