computer-organization-visualization
Use this skill whenever the user wants visual learning for computer organization, data representation, two's complement, floating point, cache mapping, memory hierarchy, instruction format, CPU datapath, control signals, pipeline timing, bus, I/O, DMA, interrupts, or 408 computer organization review.
Profile →npx skills add mingchen666/Reviva --skill computer-organization-visualization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Computer Organization Visualization Skill Create precise visual explanations for computer organization. Emphasize bit fields, data movement, component roles, timing, and calculation steps. ## Use For - two's complement range, overflow, fixed/floating-point representation; - IEEE-like floating format explanation when the user's material specifies the format; - Cache address split, direct/associat
What does the computer-organization-visualization skill do?
Use this skill whenever the user wants visual learning for computer organization, data representation, two's complement, floating point, cache mapping, memory hierarchy, instruction format, CPU datapath, control signals, pipeline timing, bus, I/O, DMA, interrupts, or 408 computer organization review.
How do I install it?
Run `npx skills add mingchen666/Reviva --skill computer-organization-visualization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mingchen666/Reviva, a repository with 181 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.