Claude-skill-registry extract-skill-from-conversation

This skill should be used when the user asks to 'extract a skill from a conversation', 'convert conversation to skill', 'create skill from chat history', 'generate skill from session', or mentions extracting reusable workflows from Claude Code conversations. Uses Fabric AI patterns to distill conversations into actionable skills.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/extract-skill-from-conversation" ~/.claude/skills/majiayu000-claude-skill-registry-extract-skill-from-conversation && rm -rf "$T"

manifest: skills/data/extract-skill-from-conversation/SKILL.md

source content

Extract Skill from Conversation

Transform Claude Code conversations into reusable skills by extracting wisdom, instructions, problems, and solutions using Fabric AI patterns.

Core Concept

Conversations contain valuable workflows buried in noise (trial & error, exploration, backtracking). This skill extracts the reusable essence and converts it into a well-structured SKILL.md.

When to Use

Convert a debugging session into a reusable debugging skill
Extract a feature implementation workflow
Create skills from research/learning conversations
Document tribal knowledge from past sessions

Extraction Pipeline

Step 1: Obtain the Conversation

Conversations are stored as JSONL in Claude Code project directories:

~/.claude/projects/{project-path-encoded}/{session-id}.jsonl

To find conversations:

# List recent conversations for a project
ls -lht ~/.claude/projects/{project-path}/*.jsonl | head -10

# Find today's conversations
find ~/.claude/projects/{project-path} -name "*.jsonl" -newermt "today"

Step 2: Parse Conversation to Text

Convert JSONL to readable text using the parse script:

bash scripts/parse_conversation.sh /path/to/conversation.jsonl > /tmp/conversation.txt

The script extracts:

User messages
Assistant responses (truncated for context)
Tool calls and results
Summaries

Step 3: Extract Value with Fabric Patterns

Apply multiple Fabric patterns in parallel to extract different aspects:

# Extract insights and wisdom
cat /tmp/conversation.txt | fabric -p extract_wisdom --stream > /tmp/wisdom.md

# Extract actionable steps
cat /tmp/conversation.txt | fabric -p extract_instructions --stream > /tmp/instructions.md

# Extract the core problem
cat /tmp/conversation.txt | fabric -p extract_primary_problem --stream > /tmp/problem.md

# Extract the solution that worked
cat /tmp/conversation.txt | fabric -p extract_primary_solution --stream > /tmp/solution.md

Run in parallel for speed:

cat /tmp/conversation.txt | fabric -p extract_wisdom > /tmp/wisdom.md &
cat /tmp/conversation.txt | fabric -p extract_instructions > /tmp/instructions.md &
cat /tmp/conversation.txt | fabric -p extract_primary_problem > /tmp/problem.md &
cat /tmp/conversation.txt | fabric -p extract_primary_solution > /tmp/solution.md &
wait

Step 4: Combine into Skill Structure

Merge extractions into a SKILL.md template. The skill should include:

---
name: [skill-name-from-problem]
description: "[One-line description of what this skill solves]"
---

# [Skill Title]

[Brief summary from extract_primary_problem]

## Problem Pattern

[When to use this skill - extracted from problem analysis]

## Steps

[Numbered steps from extract_instructions - filtered to critical path only]

## Key Insights

[Bullet points from extract_wisdom]

## Common Mistakes

[Gotchas identified during conversation]

## References

[Any URLs or files that were useful]

Step 5: Refine and Validate

After generating the skill:

Remove trial-and-error content
Keep only the "recipe that worked"
Add imperative language (do X, not "I did X")
Verify all referenced files/commands exist
Test the skill on a similar problem

Fabric Patterns Reference

Pattern	Extracts	Use For
`extract_wisdom`	Insights, learnings	Key Insights section
`extract_instructions`	Step-by-step procedures	Steps section
`extract_primary_problem`	Core problem statement	Problem Pattern section
`extract_primary_solution`	What actually worked	Solution summary
`create_recursive_outline`	Hierarchical breakdown	Complex workflows
`summarize`	Brief overview	Skill description

What to Include vs Exclude

Include in Skill

Commands that worked
Decisions and why they were made
Key insights discovered
Pattern recognition (e.g., "this type of error usually means X")
Useful references (URLs, files)
Prerequisites not obvious

Exclude from Skill

Trial and error attempts
Dead ends and backtracking
Exploratory reads that didn't help
Typos and corrections
Social conversation ("thanks", "great")
Verbose explanations (distill to essence)

Quick Reference

Full extraction pipeline:

# 1. Parse conversation
bash scripts/parse_conversation.sh /path/to/session.jsonl > /tmp/conv.txt

# 2. Extract in parallel
cat /tmp/conv.txt | fabric -p extract_wisdom > /tmp/wisdom.md &
cat /tmp/conv.txt | fabric -p extract_instructions > /tmp/steps.md &
cat /tmp/conv.txt | fabric -p extract_primary_problem > /tmp/problem.md &
cat /tmp/conv.txt | fabric -p extract_primary_solution > /tmp/solution.md &
wait

# 3. Review extractions
cat /tmp/problem.md
cat /tmp/solution.md
cat /tmp/steps.md
cat /tmp/wisdom.md

# 4. Generate skill (manually combine or use template)

Additional Resources

Scripts

scripts/parse_conversation.sh
- Convert JSONL to readable text
scripts/extract_skill.sh
- Full extraction pipeline

References

references/fabric_patterns.md
- Detailed guide to Fabric patterns
references/skill_template.md
- SKILL.md template with all sections

Examples

examples/example_extraction.md
- Complete example of extraction process