Fuat Beşer

Skill Recorder captures a real work session on your screen: the clicks, the app and window switches, the pages you visit, commands/output from its optional recorded terminal, and (if you want) your spoken narration. It then uses the GitHub Copilot CLI to reconstruct what you actually did as a clear intent plus an ordered list of steps. From there, one step turns that single run into something an agent can reuse:

a Skill: a SKILL.md procedure an agent runs on demand, or

an Automation: the same procedure on a schedule or trigger.

Both prefer the agent’s native tools (like the gh CLI or web_fetch) over replaying UI clicks, and generalize from your one example, so recording yourself submitting one form can teach the agent to submit all of them.

Coding with AI agents gives you instant results, which floods your brain with quick hits of easy dopamine. While this feels exciting and keeps your energy high in the moment, getting rewards without much effort has a serious hidden downside. When your brain gets used to constant instant gratification, your patience for slow, deep problem-solving starts to disappear. You might find yourself avoiding the tedious work of truly understanding how things work under the hood, jumping from prompt to prompt just to get another quick thrill of seeing code appear. Over time, this continuous stream of easy hits can shorten your attention span, make standard debugging feel unbearably frustrating, and leave you mentally drained—addicted to the speed of the output rather than the depth of genuine mastery.

Feature	SEO (Search Engine Optimization)	GEO (Generative Engine Optimization) / LLMO
Target Platforms	Classic search engines like Google and Bing	AI answer engines like ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews
Primary Goal	High rankings (top 10) to generate direct website clicks and organic traffic	Getting cited, referenced, or recommended as a source inside AI-generated responses
User Interaction	Multi-page click-through experience (“ten blue links”)	Zero-click conversational summaries and synthesized answers
Key Metrics	Keyword rankings, click-through rate (CTR), organic sessions	AI Visibility, brand citation rates, Share of Voice in LLMs, and AI referral traffic

Official page for LTX Video 2.5 — an open-source AI video generation model by Lightricks. Optimized for high-resolution video creation, realistic motion, and fast inference directly from text or image prompts.

Anatomy: the object is the explanation

Most technical diagrams are boxes and arrows. They show that the parts exist, but not what the parts do — you have to imagine the motion yourself.

Anatomy takes the opposite approach. It’s a skill for Claude Code that, given one line naming an idea, invents a real machine whose mechanism maps one-to-one onto that idea, then draws it as a live isometric figure. A queue becomes a marble run. An LRU cache becomes a bookcase with four places on top. A bloom filter becomes a fruit machine whose three reels are three hash functions. The author describes it in a single rule: the object is the explanation.

Each figure is built from 40 to 500+ solid parts, shaded with a single light source and one highlighted “watch this” part. You can drag, hover and use the keyboard; there’s an idle tour, it pauses when off-screen, and it goes still under reduced motion. Every readout is computed from the same model that animates the parts, so the picture and the numbers can’t disagree.

Figures ship as framework-free SVG/TypeScript or React components (anatomy-figures), with five themes, and can also be exported as printable A4 sheets. It’s MIT-licensed.