takeaway-skill

Distill references into reusable mechanisms with take and avoid decisions.

58|9|Updated Apr 6, 2026
One-click install
npx skills add https://github.com/julilaoshi/takeaway-skill --skill takeaway-skill
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: takeaway-skill
Source: https://github.com/julilaoshi/takeaway-skill/tree/main/skill
Command: npx skills add https://github.com/julilaoshi/takeaway-skill --skill takeaway-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Prevents copy-pasting by helping you distill a reference (site, effect, layout, screenshot, or visual system) into the mechanisms worth taking, clearly separating what must be redesigned from what can be reused.

Core Features & Use Cases

  • Layered distillation: Breaks the reference into structure, mechanism, visual, and rhythm layers to judge transferability.
  • Four-part take/avoid decisions: Produces direct take, cannot-take, take-after-adaptation, and evidence-insufficient outcomes to avoid guesswork.
  • Evidence-first grounding: Prioritizes source code and user recordings/screenshots, and stops when evidence is insufficient.
  • Implementation handoff guidance: Suggests the smallest valid output and the next practical workflow destination (web, SVG/vector, media extraction, or refinement).

Quick Start

Ask the AI to distill the provided reference into mechanisms, list what can be reused vs must be redesigned, and produce an implementation handoff that updates the working output entry in takeaway_is_here/distilled_entries/.

Frequently Asked Questions about takeaway-skill

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

FAQPage Schema
How do I extract reusable interaction patterns from a reference website without copying the visual style?

Extracting reusable interaction patterns requires layered distillation to separate underlying mechanisms from surface styling. You break the reference into structure, mechanism, visual, and rhythm layers, judging transferability to isolate what can be reused from what must be redesigned.

What is the best way to analyze UI layout screenshots for implementation handoff?

Analyzing UI layout screenshots for implementation handoff involves evidence-first grounding. You prioritize source code and user recordings, producing four-way take decisions—direct take, cannot-take, take-after-adaptation, and evidence-insufficient—to guide the smallest valid output and next workflow destination.

How does mechanism design distillation work when studying reference videos?

Mechanism design distillation works by breaking reference videos into structural layers to evaluate transferability. It applies evidence-prioritized judgments to determine exactly what interaction effects can be directly reused versus what requires adaptation, stopping when evidence is insufficient.

Can I use this approach to study landing pages and visual systems for adaptation goals?

You can use this approach to study landing pages and visual systems by applying four-part take/avoid decisions. It clearly separates reusable mechanisms from surface styles that must be redesigned, ensuring your adaptation goals are grounded in structural evidence rather than guesswork.

What are the limitations of distilling references into reusable mechanisms?

A key limitation is the dependency on evidence availability; the process stops when evidence is insufficient to judge transferability. It also requires distinguishing between direct takes and adaptations, meaning you cannot simply copy surface styles without layered structural analysis.

How to convert reference study results into a reusable entry template?

To convert reference study results into a reusable entry template, you apply implementation handoff guidance that suggests the smallest valid output. This updates your working output entry by directing the distilled mechanisms to practical workflow destinations like web or vector formats.