meta-session-wrapper

name	meta-session-wrapper
description	Extract reusable patterns from completed session work. Use when users want to wrap a session into a reusable component, extract workflows, make completed work reusable, abstract session work into patterns, or create a skill from session history.

Meta Session Wrapper

Knowledge for extracting and abstracting completed work from a session into a reusable pattern.

Quick Reference

Phase	Input	Output
1. IDENTIFY	Session history / completed work	Work summary
2. ABSTRACT	Concrete actions	Generic pattern
3. FORMALIZE	Pattern description	Feature Request

When to Use

✅ USE WHEN:

• You completed a multi-step workflow that might be repeated
• You want to turn ad-hoc work into a reusable component
• You see patterns emerging across sessions

❌ DON'T USE WHEN:

• Work was one-time only, won't be repeated
• Already have a clear feature request (use /create-llm-structure directly)

Workflow

Phase 1: IDENTIFY Work Done

Goal: Catalog what was accomplished in the session.

1.1 Review Session Actions

List all significant actions:

## Session Work Summary

### Actions Performed
1. [Action 1]: [What was done]
2. [Action 2]: [What was done]
3. [Action 3]: [What was done]
...

### Artifacts Created
- [File/artifact 1]: [Purpose]
- [File/artifact 2]: [Purpose]
...

### Decisions Made
- [Decision 1]: [Reasoning]
- [Decision 2]: [Reasoning]
...

1.2 Identify Patterns

Ask these questions:

Question	Answer
What triggered this work?	[User request / problem identified]
What was the end goal?	[Desired outcome]
Were there repeatable steps?	[Yes/No - list if yes]
Could this be automated?	[Fully / Partially / Manual only]
What domain knowledge was used?	[Expertise applied]

Phase 2: ABSTRACT to Generic Pattern

Goal: Convert concrete work into a reusable pattern.

2.1 Generalize Steps

Concrete (This Session)	Abstract (Any Session)
"Created learning-content-creator skill"	"Create domain skill from pattern"
"Added frontmatter to 14 files"	"Apply metadata standard to documents"
"Translated EN to KO"	"Create language variants"

2.2 Identify Variables

What parts change between uses?

## Pattern Variables

| Variable | This Session | Generic |
|----------|--------------|---------|
| Source | `research/*.md` | `{source_directory}` |
| Output | `learning/*.md` | `{output_directory}` |
| Languages | EN, KO | `{language_list}` |

2.3 Define Trigger Conditions

When should this pattern be invoked?

## Trigger Conditions

### Keywords
- "create learning content"
- "research to learning"

### Context
- Research files exist in source directory
- Multi-model analysis completed

### User Intent
- Transform research into structured educational content

Phase 3: FORMALIZE as Feature Request

Goal: Output a structured description for /create-llm-structure.

3.1 Write Feature Request

Use this template:

## Feature Request

### Name
[pattern-name] (kebab-case)

### Description
[1-2 sentence description of what this does]

### Trigger
[How is it activated - user command, auto-detect, goal assignment]

### Inputs
- [Input 1]: [Description]
- [Input 2]: [Description]

### Outputs
- [Output 1]: [Description]
- [Output 2]: [Description]

### Steps (High-Level)
1. [Step 1]
2. [Step 2]
3. [Step 3]

### Domain Knowledge Required
[What expertise is needed - coding patterns, frameworks, etc.]

### Side Effects
[File creation, external API calls, deployments, etc.]

### Reusability
[How often might this be used - once, occasionally, frequently]

3.2 Validate Feature Request

Before proceeding, verify:

• Description is clear and complete
• Trigger is well-defined
• Steps are at the right abstraction level (not too detailed, not too vague)
• Domain knowledge is identified
• Side effects are documented

Output

The final output is a Feature Request document containing:

• Name (kebab-case)
• Description
• Trigger conditions
• Inputs/Outputs
• High-level steps
• Domain knowledge required
• Side effects
• Reusability assessment

Example: Learning Content Creator

Phase 1: Identify

## Session Work Summary

### Actions Performed
1. Read research documents (Claude, GPT, Gemini responses)
2. Created content outline synthesizing insights
3. Wrote 7 English learning modules
4. Translated to 7 Korean modules
5. Added YAML frontmatter to all 14 files

### Artifacts Created
- learning/README.en.md - Course index
- learning/01-06-*.en.md - 6 learning modules
- learning/*.ko.md - Korean translations

### Decisions Made
- 6 modules (fundamentals → anti-patterns progression)
- Keep technical terms in English for KO version
- Use tutorial type for frontmatter

Phase 2: Abstract

## Generic Pattern

| Concrete | Abstract |
|----------|----------|
| research/01-claude.en.md | {research_sources} |
| learning/*.en.md | {output_dir}/*.{primary_lang}.md |
| EN → KO translation | Primary → Secondary language |

## Variables
- source_directory
- output_directory
- primary_language (default: en)
- secondary_languages (optional)
- frontmatter_skill (optional)

Phase 3: Formalize

## Feature Request

### Name
learning-content-creator

### Description
Transform multi-model research materials into structured learning content with bilingual support and proper metadata.

### Trigger
User says "create learning content" or "research to learning" when research files exist.

### Inputs
- Research directory with model responses
- Target output directory
- Language configuration

### Outputs
- Structured learning modules (EN)
- Translations (KO)
- Frontmatter metadata on all files

### Steps
1. Analyze research sources
2. Create content outline
3. Write English modules
4. Translate to Korean
5. Add frontmatter

### Domain Knowledge
- Multi-model research synthesis
- Learning content structure
- Translation guidelines
- Frontmatter schema

### Side Effects
- Creates multiple files
- No external APIs
- No deployments

### Reusability
Moderate - whenever new research topic completed

Output

After completing this workflow, return the Feature Request to the calling command.

The command will handle the next steps (diagnosis, spec generation, implementation).

References

Resource	Purpose
Pattern Templates	Common reusable pattern types
Abstraction Guide	Tips for effective abstraction