distill-discussion

Distill discussion files into structured crystals and lessons with traceable records.

1|Updated Feb 15, 2026
One-click install
npx skills add https://github.com/n0rvyn/indie-toolkit --skill distill-discussion
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: distill-discussion
Source: https://github.com/n0rvyn/indie-toolkit/tree/main/dev-workflow/skills/distill-discussion
Command: npx skills add https://github.com/n0rvyn/indie-toolkit --skill distill-discussion

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Convert raw discussion exports (AI conversations, exploratory notes) into structured knowledge artifacts by distilling deliberations into decisions and lessons.

Core Features & Use Cases

  • Crystal extraction: identify and capture decisive conclusions as structured crystals.
  • Lesson extraction: surface pitfalls and lessons learned to prevent repeat mistakes.
  • File-based processing: accepts one or more files or directories of discussion exports and routes each to crystal, lesson, or skip.
  • Traceability & context: links outputs back to their source discussions for auditing and review.

Quick Start

Provide one or more discussion files to process and let the skill classify and distill them into crystals and lessons.

Frequently Asked Questions about distill-discussion

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I distill discussions into structured knowledge artifacts?

Distill discussions by providing raw export files for the skill to classify and process. It identifies and captures decisive conclusions as structured crystals and surfaces lessons learned, linking each output back to its source discussion for traceability.

What is the best way to extract lessons learned from AI conversation logs?

Extract lessons learned by routing conversation logs through a file-based processing workflow that surfaces pitfalls to prevent repeat mistakes. The skill outputs machine-readable lesson records linked directly to their source discussions for auditing.

How does knowledge management work for large discussion files?

Knowledge management for large discussion files works via agent-based reading to handle extensive content. The skill processes large files by routing them through agents to extract structured crystals and lessons without manual intervention.

Can I process a directory of exploratory notes to capture decisions?

Yes, you can process a directory of exploratory notes by providing the folder path to the skill. It accepts one or more files or directories, routing each item to crystal, lesson, or skip based on its content.

When should I skip files during discussion distillation?

You should skip files during discussion distillation when the content lacks decisive conclusions or pitfalls worth capturing. The skill automatically routes files to skip if they do not contain actionable crystals or lessons.