Notebook Knowledge Distillation

Language / 语言: English primary · 中文概览如下。

中文概览

将外部资料转化为经过验证、可复用的 Agent 能力，避免把未经核验的笔记直接写入组织记忆。

A source-to-skill workflow for converting external knowledge into validated reusable agent capability.

NATIVE AI · INTERNAL UTILITY · INTERNAL EVALUATION

Native AI question: How can a collection of external sources become a reusable skill without simply copying unverified notes into agent memory?

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Why this exists

Reading sources is not the same as building organizational capability.

External sources
      ↓
Notebook / source collection
      ↓
density / relevance scan
      ↓
multi-source distillation
      ↓
cross-source validation
      ↓
skill candidate
      ↓
skill-tester
      ↓
reusable organizational skill

NotebookLM is the current source workspace. It should not define the long-term identity of the project.

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Core mechanisms

• source-density scan;
• multi-source validation;
• iterative distillation;
• gap detection;
• test-case generation;
• skill packaging;
• internal quality evaluation.

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Evidence

The repository currently reports a TopPrism internal skill-tester score of 8.75/10.

Label it exactly that way. It is not an industry benchmark.

The stronger evidence to add next is an end-to-end example:

Source set
   ↓
Distilled skill
   ↓
Test cases
   ↓
Later task where the skill triggers
   ↓
Measured improvement

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Boundaries

• source agreement can still be collectively wrong;
• source quality matters more than source count;
• distillation can remove nuance;
• skill trigger design can cause false activation;
• copyrighted / confidential source material must not be republished improperly;
• source-handling and IP rules must be reviewed before ingesting external documents.

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TopPrism metadata

topprism:
  purpose: native-ai
  capability: source-to-skill-distillation
  platform_layer: organizational-intelligence
  maturity: internal-utility
  evidence:
    type: internal-evaluation
  related:
    - skill-tester
    - agent-nurture-framework