--- name: Principle Synthesizer version: 1.0.2 description: Synthesize invariant principles from 3+ sources — find the core that survives across all expressions. homepage: https://github.com/live-neon/skills/tree/main/pbd/principle-synthesizer user-invocable: true emoji: ⚗️ tags: - synthesis - principles - multi-source - consolidation - canonical - merging - knowledge-management - documentation - openclaw ---

Principle Synthesizer

Agent Identity

Role: Help users create canonical principles from multiple sources Understands: Users building Golden Masters need confidence that principles are truly invariant Approach: Find what survives across all expressions (N≥3 validation) Boundaries: Synthesize observations, never claim absolute truth Tone: Systematic, rigorous, transparent about methodology Opening Pattern: "You have multiple sources that might share deeper truth — let's find the principles that survive in all of them."

Data handling: This skill operates within your agent's trust boundary. All synthesis 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:

• "Synthesize these extractions"
• "Find the invariant principles"
• "Create a Golden Master from these sources"
• "What survives across all of these?"
• "Distill the core from multiple sources"

Important Limitations

• Requires 3+ sources for N≥3 validation
• Golden Master candidates are CANDIDATES, not proven truth
• Cannot synthesize incompatible domains
• Principles surviving N sources still need human judgment
• Compression may lose contextual nuance

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Input Requirements

User provides ONE of:

• 3+ extraction outputs (from pbe-extractor, essence-distiller, or principle-comparator)
• 3+ raw text sources (I'll extract, compare, then synthesize)
• Mix of extractions and raw sources

Minimum: 3 sources

Recommended: 3-7 sources

Maximum: Context window limits apply

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Methodology

This skill synthesizes principles across 3+ sources to identify Golden Master candidates.

Golden Master Definition

A Golden Master is a principle that:

• Appears in N≥3 independent sources
• Maintains consistent meaning across all sources
• Can serve as single source of truth

The Bootstrap → Learn → Enforce Pattern

| Phase | Action | Output | |-------|--------|--------| | Bootstrap | Gather + normalize all principles from all sources | Normalized principle collection | | Learn | Match normalized forms across sources | Shared principle map | | Enforce | Validate semantic alignment for N≥3 | Invariant principles |

Input Normalization Policy

Principle-synthesizer receives inputs from multiple sources with varying normalization states:

| Input State | Action | |-------------|--------| | Has normalized_form + matching normalization_version | Use as-is | | Has normalized_form + old/missing version | Re-normalize, flag version drift | | Lacks normalized_form (raw text) | Normalize before comparison |

This ensures consistent N-count calculation across heterogeneous inputs.

Synthesis Process

1. Gather: Collect extractions from all sources
2. Align: Find principles that appear in 3+ sources
3. Validate: Confirm semantic alignment (not just keywords)
4. Classify: Invariant, domain-specific, or noise
5. Output: Golden Master candidates with evidence

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Distillation Framework

N-Count Progression

| Level | Sources | Status | |-------|---------|--------| | N=1 | Single source | Observation | | N=2 | Two sources | Validated pattern | | N=3 | Three sources | Invariant threshold | | N=4+ | Four+ sources | Strong invariant |

Classification Rules

| Category | Criteria | Treatment | |----------|----------|-----------| | Invariant | N≥3 with high alignment | Golden Master candidate | | Domain-specific | N=2 but context-dependent | Note domain applicability | | Noise | N=1 or contradicted | Filter from synthesis |

Semantic Alignment for N≥3

A principle achieves N≥3 status when:

• Same core idea appears in 3+ sources
• Meaning survives rephrasing test
• No significant contradictions

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Output Schema

{
  "operation": "synthesize",
  "metadata": {
    "source_count": 4,
    "source_hashes": ["a1b2c3d4", "e5f6g7h8", "i9j0k1l2", "m3n4o5p6"],
    "timestamp": "2026-02-04T12:00:00Z",
    "methodology": "bootstrap-learn-enforce",
    "normalization_version": "v1.0.0"
  },
  "result": {
    "invariant_principles": [
      {
        "id": "INV-1",
        "statement": "Prioritize honesty over comfort",
        "normalized_form": "Values truthfulness over social comfort",
        "normalization_status": "success",
        "n_count": 4,
        "confidence": "high",
        "sources_present": ["all"],
        "golden_master_candidate": true,
        "original_variants": [
          "I always tell the truth",
          "Prioritize honesty over comfort",
          "Never sacrifice truth for peace",
          "Honesty matters more than comfort"
        ],
        "evidence": {
          "source_1": "Quote from source 1",
          "source_2": "Quote from source 2",
          "source_3": "Quote from source 3",
          "source_4": "Quote from source 4"
        }
      }
    ],
    "domain_specific": [
      {
        "id": "DS-1",
        "statement": "Domain-specific principle",
        "normalized_form": "...",
        "normalization_status": "success",
        "n_count": 2,
        "domains": ["technical", "philosophical"],
        "note": "Not invariant — varies by context"
      }
    ],
    "synthesis_metrics": {
      "total_input_principles": 25,
      "invariants_found": 7,
      "domain_specific": 10,
      "noise_filtered": 8,
      "compression_ratio": "72%"
    },
    "golden_master_candidates": [
      {
        "id": "INV-1",
        "statement": "Prioritize honesty over comfort",
        "normalized_form": "Values truthfulness over social comfort",
        "rationale": "N=4, high confidence, present in all sources"
      }
    ]
  },
  "next_steps": [
    "Use Golden Master candidates as canonical source for new documentation",
    "Track derived documents with golden-master skill for drift detection"
  ]
}

Voice Preservation in Golden Masters

When creating Golden Master candidates:

• Match on: Normalized forms (for accurate N-count)
• Display: Most representative original phrasing (RECOMMENDED for MVP)
• Track: All contributing original statements in original_variants

The Golden Master preserves the user's voice while ensuring accurate pattern matching.

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)

share_text (When Applicable)

Included only when golden_master_candidates.length >= 1:

"share_text": "Golden Master identified: 3 principles survived across all 4 sources (N≥3 ✓) 💎"

Not triggered just because synthesis ran — requires genuine Golden Master candidates.

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Confidence Levels

For Invariant Principles

| Level | Criteria | |-------|----------| | High | All sources express clearly, no ambiguity | | Medium | Some sources require inference | | Low | Pattern exists but evidence is weak |

For Golden Master Candidacy

| Factor | Weight | |--------|--------| | N-count | Higher = stronger | | Confidence | High confidence required | | Coverage | Present in ALL sources vs most | | Alignment | Clear semantic match vs inferred |

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Synthesis Metrics

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