---
id: "framework-skill-creation"
type: "framework"
source_timestamps: ["00:08:19", "00:09:00", "00:12:30"]
tags: ["prompt-engineering", "process-mapping"]
related: ["concept-dialogue-structure", "entity-k-kumar"]
steps: ["Identify the primary Goal or Intent of the conversation", "Extract the Constraints applied during the dialogue", "Identify the Assumptions made by both user and model", "Map the Sub-goals or intermediate steps", "Encode Goal/Constraints/Assumptions/Sub-goals into a structured markdown 'Skill' file"]
---
# Skill Creation via Dialogue Extraction

## Purpose

A repeatable framework for converting ephemeral chatbot conversations into structured, reusable AI **skills** (markdown files) suitable for use under [[concept-icm]]. It operationalizes the thesis of [[concept-dialogue-structure]].

The framework grew out of the visual decision-tree mapping tool built by [[entity-k-kumar]].

## The Five Steps

### 1. Identify the Goal / Intent

What is the user actually trying to achieve? Example: *'Tighten this paragraph.'* This becomes the top of the decision tree.

### 2. Extract Constraints

What boundaries shaped the response? Examples:

- Reduce wordiness
- Maintain the original rhythm and voice
- Preserve all factual content
- Honour a length budget

### 3. Identify Assumptions

What did the user and the model implicitly assume? Examples:

- Target audience is a general blog readership
- The paragraph is final-draft quality
- No new sources or facts may be introduced

### 4. Map Sub-Goals

What intermediate steps were required? Examples:

- Identify filler phrases
- Collapse redundant clauses
- Re-balance sentence lengths
- Verify meaning preservation

### 5. Encode into a Markdown Skill

Write Goal / Constraints / Assumptions / Sub-goals into a structured markdown file in the skills folder. This artifact becomes a permanent, version-controlled skill consumable by any agent operating inside the [[concept-icm]] vault.

## Why This Works

The framework is consistent with mainstream prompt-engineering practice (deriving system prompts and tools by abstracting successful interactions) and conversational-UX practice (modelling chatbot flows as decision trees). Multi-agent research likewise decomposes tasks into conversational roles with explicit protocols.

## Related Action

[[action-codify-voice]] is a concrete instance of this framework applied to writing voice/tone.