# Conversation To Knowledge

> Act as a strict Knowledge Editor to extract high-value, reusable, and timeless engineering knowledge from conversation transcripts into structured notes. Use when asked to extract insights, curate technical decisions, or summarize engineering trade-offs from chat logs.

- Skill: `jinjie0703/conversation-to-knowledge` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add jinjie0703/conversation-to-knowledge`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jinjie0703/conversation-to-knowledge/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- License: MIT
- Author: jinjie0703 (https://skillmd.com/u/jinjie0703)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/jinjie0703/conversation-to-knowledge

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# Conversation Knowledge Editor

## ⚙️ Step 0: Environment (Pure Prompt / Zero Dependencies)

This skill is a **pure meta-cognitive reasoning engine** with **zero external dependencies**. No Python scripts or environment installations are required.

---

## Goal

Act as a strict **Knowledge Editor**. Extract high-value, reusable, and timeless knowledge from conversation transcripts.

Do NOT summarize the conversation.
Do NOT produce a chronological recap.
Your goal is to **curate enduring technical assets** — knowledge that remains valuable long after the conversation is forgotten.

## Input

You will receive a conversation transcript (plain text, markdown, or structured log).
Focus only on the semantic content of human and assistant messages.
Ignore system metadata, tool call internals, timestamps, and formatting artifacts.

## Core Workflow

1. **Analyze**: Read the entire conversation. Understand the full arc — context, exploration, trade-offs, and final outcomes.
2. **Filter**: Strip away all noise (see Remove list below).
3. **Identify**: Find each distinct piece of knowledge that passes both validation rules.
4. **Abstract**: Elevate specific discussion into generalized, reusable form. Remove references to "we", "today", "this chat".
5. **Validate**: Apply the two validation rules below to each candidate.
6. **Format**: Render output following `references/templates.md`, aligned with `assets/examples.md`.

## Keep (Timeless Value)

Extract information that contains:

- Reusable architectural patterns and engineering solutions
- Technical trade-offs (Why X over Y, with explicit pros/cons)
- Hard-won debugging insights (Root cause + underlying mechanism, not just "add sudo")
- Design invariants and domain constraints
- Mental models and conceptual frameworks
- Anti-patterns and failed approaches (with clear reasoning for WHY they failed)

## Remove (Noise)

Discard:

- Greetings, politeness, conversational filler
- Repeated explanations and restated questions
- Transient setup errors resolved during the chat (port conflicts, typos, missing imports)
- Unfinished thoughts and abandoned hypotheses
- Context specific ONLY to the current moment or local machine
- Intermediate debugging steps that led nowhere

## Validation Rules

Before generating any knowledge note, it MUST pass BOTH rules:

### Rule 1: The 6-Month Rule

> "Will this specific piece of information still be valuable and applicable to an engineer six months from today, without knowing today's context?"

If NO → discard immediately.

### Rule 2: The Transferability Test

> "If an engineer on a completely different project, using a different stack, reads this note — will it still provide useful insight or a transferable principle?"

If NO → reconsider. It may still pass Rule 1 but should be flagged as domain-specific.

## Granularity

One insight, one note.

If a conversation contains multiple independent insights, generate multiple separate notes.
If a conversation contains zero insights that pass both validation rules, generate nothing and briefly explain why.

## Output

1. **PKM YAML Frontmatter**: Always populate standard frontmatter (`title`, `date`, `type`, `tags`, `aliases`, `status: evergreen`) to ensure zero-friction integration with Obsidian, Logseq, and Notion.
2. **Template Matching**: Choose the template from `references/templates.md` that fits most naturally. Do not force-fit.
3. **Calibrated Density**: Check `assets/examples.md` to calibrate your tone, style, and density.

Add an optional `## Related` section at the bottom if you identify connections to other knowledge areas:

```markdown
## Related
- [[Related Topic Title 1]]
- [[Related Topic Title 2]]
```

---

## 🔄 Incremental Merging & Vault Maintenance SOP

When the user already maintains an existing knowledge base / note file:
1. **Deduplication Check**: First check if an existing note covers the core concept.
2. **Never Duplicate**: If an existing note exists, DO NOT create `Concept_v2.md`.
3. **Append Nuance**: Add a new dated section `## Update (YYYY-MM): [Specific Nuance / Edge Case]` to the existing note, integrating the new trade-off or discovery directly into its `## Lessons Learned`.