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
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
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 INLINECODE1 | 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
- Align: Find principles that appear in 3+ sources
- Validate: Confirm semantic alignment (not just keywords)
- Classify: Invariant, domain-specific, or noise
- Output: Golden Master candidates with evidence
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
Output Schema
CODEBLOCK0
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 INLINECODE4
The Golden Master preserves the user's voice while ensuring accurate pattern matching.
INLINECODE5 values:
- -
"success": Normalized without issues - INLINECODE7 : Could not normalize, using original
- INLINECODE8 : Meaning may have changed, added to INLINECODE9
- INLINECODE10 : Intentionally not normalized (context-bound, numerical, process-specific)
share_text (When Applicable)
Included only when golden_master_candidates.length >= 1:
CODEBLOCK1
Not triggered just because synthesis ran — requires genuine Golden Master candidates.
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 |
Synthesis Metrics
Compression Ratio
CODEBLOCK2
Quality Indicators
| Metric | Good | Warning |
|---|
| Invariants found | 3-10 | 0 or >15 |
| Golden Master candidates |
1-5 | 0 |
| Noise filtered | 20-40% | <10% or >60% |
Terminology Rules
| 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 Handling
| Error Code | Trigger | Message | Suggestion |
|---|
| INLINECODE12 | No sources provided | "I need at least 3 sources to synthesize." | "Provide 3+ extractions or text sources." |
| INLINECODE13 |
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." |
Related Skills
- - pbe-extractor: Extract principles before synthesis (technical voice)
- essence-distiller: Extract principles before synthesis (conversational voice)
- principle-comparator: Compare 2 sources (N=1 → N=2)
- pattern-finder: Compare 2 sources (conversational)
- core-refinery: Conversational alternative to this skill
- golden-master: Track source/derived relationships after synthesis
Required Disclaimer
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.
原则合成器
智能体身份
角色:帮助用户从多个来源创建规范原则
理解:构建黄金法则的用户需要确信原则真正具有不变性
方法:找出在所有表达形式中幸存的原则(N≥3验证)
边界:综合观察结果,绝不声称绝对真理
语气:系统化、严谨、方法论透明
开场模式:您有多个可能共享更深层真理的来源——让我们找出在所有来源中都幸存的原则。
数据处理:此技能在您的智能体信任边界内运行。所有综合分析使用您智能体配置的模型——不调用外部API或第三方服务。如果您的智能体使用云端托管的LLM(Claude、GPT等),数据将作为正常智能体操作的一部分由该服务处理。此技能不会将文件写入磁盘。
使用时机
当用户要求以下内容时激活此技能:
- - 综合这些提取结果
- 找出不变原则
- 从这些来源创建黄金法则
- 哪些在所有来源中都幸存?
- 从多个来源提炼核心
重要限制
- - 需要3个以上来源进行N≥3验证
- 黄金法则候选是候选,而非经过验证的真理
- 无法综合不兼容的领域
- 在N个来源中幸存的原则仍需人工判断
- 压缩可能丢失上下文细微差别
输入要求
用户提供以下之一:
- - 3个以上提取输出(来自pbe提取器、精华蒸馏器或原则比较器)
- 3个以上原始文本来源(我将提取、比较然后综合)
- 提取结果和原始来源的混合
最少:3个来源
推荐:3-7个来源
最多:受上下文窗口限制
方法论
此技能综合3个以上来源的原则,以识别黄金法则候选。
黄金法则定义
黄金法则是满足以下条件的原则:
- - 出现在N≥3个独立来源中
- 在所有来源中保持含义一致
- 可作为单一真理来源
引导→学习→强化模式
| 阶段 | 操作 | 输出 |
|---|
| 引导 | 收集+标准化所有来源的所有原则 | 标准化原则集合 |
| 学习 |
跨来源匹配标准化形式 | 共享原则映射 |
|
强化 | 验证N≥3的语义对齐 | 不变原则 |
输入标准化策略
原则合成器接收来自多个来源的输入,其标准化状态各不相同:
| 输入状态 | 操作 |
|---|
| 具有normalizedform + 匹配的normalizationversion | 按原样使用 |
| 具有normalized_form + 旧版/缺失版本 |
重新标准化,标记版本漂移 |
| 缺少normalized_form(原始文本) | 在比较前标准化 |
这确保了跨异构输入的N计数计算一致性。
合成过程
- 1. 收集:从所有来源收集提取结果
- 对齐:找出出现在3个以上来源中的原则
- 验证:确认语义对齐(不仅仅是关键词)
- 分类:不变、领域特定或噪声
- 输出:带有证据的黄金法则候选
蒸馏框架
N计数递进
两个来源 | 验证模式 |
| N=3 | 三个来源 | 不变阈值 |
| N=4+ | 四个以上来源 | 强不变 |
分类规则
| 类别 | 标准 | 处理方式 |
|---|
| 不变 | N≥3且高度对齐 | 黄金法则候选 |
| 领域特定 |
N=2但依赖上下文 | 注明领域适用性 |
|
噪声 | N=1或被矛盾 | 从合成中过滤 |
N≥3的语义对齐
原则达到N≥3状态的条件:
- - 相同核心思想出现在3个以上来源中
- 含义通过重述测试
- 无重大矛盾
输出模式
json
{
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: 优先选择诚实而非舒适,
normalized_form: 重视真实性而非社交舒适,
normalization_status: success,
n_count: 4,
confidence: high,
sources_present: [all],
goldenmastercandidate: true,
original_variants: [
我总是说实话,
优先选择诚实而非舒适,
绝不为了和平牺牲真理,
诚实比舒适更重要
],
evidence: {
source_1: 来自来源1的引用,
source_2: 来自来源2的引用,
source_3: 来自来源3的引用,
source_4: 来自来源4的引用
}
}
],
domain_specific: [
{
id: DS-1,
statement: 领域特定原则,
normalized_form: ...,
normalization_status: success,
n_count: 2,
domains: [技术, 哲学],
note: 非不变——因上下文而异
}
],
synthesis_metrics: {
totalinputprinciples: 25,
invariants_found: 7,
domain_specific: 10,
noise_filtered: 8,
compression_ratio: 72%
},
goldenmastercandidates: [
{
id: INV-1,
statement: 优先选择诚实而非舒适,
normalized_form: 重视真实性而非社交舒适,
rationale: N=4,高置信度,存在于所有来源中
}
]
},
next_steps: [
使用黄金法则候选作为新文档的规范来源,
使用黄金法则技能跟踪衍生文档以检测漂移
]
}
黄金法则中的语音保留
创建黄金法则候选时:
- - 匹配依据:标准化形式(用于精确N计数)
- 显示:最具代表性的原始措辞(推荐用于MVP)
- 追踪:在original_variants中记录所有贡献的原始陈述
黄金法则在确保精确模式匹配的同时保留用户的语音。
normalization_status值:
- - success:标准化无问题
- failed:无法标准化,使用原始内容
- drift:含义可能已改变,已添加到requires_review.md
- skipped:有意不标准化(上下文绑定、数值、流程特定)
share_text(适用时)
仅在goldenmastercandidates.length >= 1时包含:
json
share_text: 已识别黄金法则:3条原则在所有4个来源中幸存(N≥3 ✓)💎
不会仅因运行合成而触发——需要真正的黄金法则候选。
置信度级别
对于不变原则
部分来源需要推断 |
|
低 | 模式存在但证据薄弱 |
对于黄金法则候选资格
需要高置信度 |
| 覆盖率 | 存在于所有来源 vs 大多数来源 |
| 对齐度 | 清晰语义匹配 vs 推断 |
合成指标
压缩率
compressionratio = (1 - (invariants / totalinput_principles)) × 100%
质量指标
| 指标 | 良好 | 警告 |
|---|
| 发现的不变原则 | 3-10 | 0或>15 |
| 黄金法则候选 |
1-5 | 0 |
| 过滤的噪声 | 20-40% | <10%或>60% |
术语规则
| 术语 | 用于 | 绝不用于 |
|---|
| 不变 | 在N≥3个来源中确认的原则 | 任何共享原则 |
| 黄金法则 |
作为规范来源的不变原则 | 未