principle-synthesizerSynthesize invariant principles from 3+ sources — find the core that survives across all expressions.
Install via ClawdBot CLI:
clawdbot install leegitw/principle-synthesizerRole: 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."
Activate this skill when the user asks to:
User provides ONE of:
This skill synthesizes principles across 3+ sources to identify Golden Master candidates.
A Golden Master is a principle that:
| 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 |
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.
| 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 |
| 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 |
A principle achieves N≥3 status when:
{
"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"
]
}When creating Golden Master candidates:
original_variantsThe 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)Included only when golden_master_candidates.length >= 1:
"share_text": "Golden Master identified: 3 principles survived across all 4 sources (N≥3 ✓) obviouslynot.ai/pbd/{source_hash} 💎"Not triggered just because synthesis ran — requires genuine Golden Master candidates.
Note: The URL pattern obviouslynot.ai/pbd/{source_hash} is illustrative. Actual URL structure depends on deployment configuration.
| Level | Criteria |
|-------|----------|
| High | All sources express clearly, no ambiguity |
| Medium | Some sources require inference |
| Low | Pattern exists but evidence is weak |
| Factor | Weight |
|--------|--------|
| N-count | Higher = stronger |
| Confidence | High confidence required |
| Coverage | Present in ALL sources vs most |
| Alignment | Clear semantic match vs inferred |
compression_ratio = (1 - (invariants / total_input_principles)) × 100%| Metric | Good | Warning |
|--------|------|---------|
| Invariants found | 3-10 | 0 or >15 |
| Golden Master candidates | 1-5 | 0 |
| Noise filtered | 20-40% | <10% or >60% |
| Term | Use For | Never Use For |
|------|---------|---------------|
| Invariant | Principle confirmed in N≥3 sources | Any shared principle |
| Golden Master | Invariant serving as canonical source | Unvalidated principles |
| Candidate | Potential Golden Master awaiting human approval | Confirmed truths |
| Synthesis | Multi-source distillation | Two-source comparison |
| Error Code | Trigger | Message | Suggestion |
|------------|---------|---------|------------|
| EMPTY_INPUT | No sources provided | "I need at least 3 sources to synthesize." | "Provide 3+ extractions or text sources." |
| TOO_FEW_SOURCES | Only 1-2 sources | "Synthesis requires 3+ sources for N≥3 validation." | "Add more sources, or use principle-comparator for 2-source comparison." |
| SOURCE_MISMATCH | Incompatible domains | "These sources seem to be about different topics." | "Synthesis works best with sources covering the same domain." |
| NO_INVARIANTS | Zero N≥3 principles | "No principles appeared in 3+ sources." | "Sources may be genuinely independent, or try related sources." |
Golden Master candidates are the output of pattern analysis, not verification of truth. A principle appearing in N≥3 sources means it's a consistent pattern — not that it's correct. Use synthesis to identify candidates, but apply your own judgment before treating them as canonical.
Built by Obviously Not — Tools for thought, not conclusions.
Generated Mar 1, 2026
Analyze 3+ competitor whitepapers or product descriptions to distill core value propositions and invariant customer needs, enabling a unified strategy. This helps identify overlapping principles that survive across the market landscape.
Synthesize principles from 3+ research papers in a field like psychology or education to create a Golden Master of foundational theories, supporting meta-analyses or curriculum development with validated invariant concepts.
Integrate principles from 3+ internal documents, such as codes of conduct or process guidelines, to develop invariant ethical or operational standards, streamlining compliance and training across departments.
Distill core messaging principles from 3+ successful marketing campaigns or brand guidelines to create a unified content framework, ensuring consistent brand voice and invariant themes across channels.
Synthesize invariant legal principles from 3+ case law summaries or regulatory texts to identify Golden Master candidates for compliance frameworks, aiding in risk assessment and policy drafting.
Offer a subscription-based tool for businesses to upload multiple documents and automatically synthesize invariant principles, with tiered pricing based on source volume and advanced analytics. Revenue streams include monthly subscriptions and enterprise licenses.
Provide bespoke synthesis services for clients in industries like research or governance, using the skill to analyze their sources and deliver Golden Master reports, with revenue from project-based fees and retainer agreements.
Integrate the skill into academic or training platforms to help students and professionals synthesize principles from multiple texts, generating revenue through platform licensing, institutional sales, or freemium upgrades.
💬 Integration Tip
Ensure users provide at least 3 sources with clear text or extractions; pre-process raw inputs to normalize forms for accurate N-count validation and avoid domain mismatches.
Apply an approved BOOT upgrade item by number using the guided apply runner.
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