lookdev-auto
Automated visual tuning: a vision or video model rates rendered variants in a loop. Render several labeled variants into one artifact, ask the model to rate them and suggest better values, render the suggestions, ask it to pick the best, repeat until good — the model is the eye, you run the loop.
Profile →npx skills add sickn33/agentic-awesome-skills --skill lookdev-auto --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.
## When to Use Use whenever "looks/feels right" is the success criterion and there's no cheap numeric metric — animation easing/timing, zoom/camera feel, color grade, layout/spacing, design params, render/encoder settings, prompt params. Use the automated counterpart to lookdev when there's no human to sit the loop. _Source: [connerkward/lookdev-auto-skill](https://github.com/connerkward/lookdev-auto-skill) (MIT)._ # Visual eval loop — let a vision/video model tune what only an eye can judge When the target is "does this LOOK/FEEL right" (not a number you can minimize), a vision model (image) or video-understanding model (motion/timing) can be the judge in a tight optimize loop. Worked reference: the `screenstudio-alternative` skill (`iteration.py`) (tuned zoom-animation feel via `fal-ai/video-understanding`). ## The loop 1. **Render N labeled variants into ONE artifact.** Vary the parameter(s) across a small spread. **Annotate each variant's params ON the artifact** (burn the label in: "A · 2.2Hz · ζ0.5"). Images → a labeled grid/contact sheet. Video/motion → a labeled *sequence* (label card or burned-in overlay before/over each clip) so the model can compare temporally. 2. **One
- When to Use
- The loop
- Token / quality / step reductions (do these)
- When NOT to use it
- Caveats (learned)
- Limitations
What does the lookdev-auto skill do?
Automated visual tuning: a vision or video model rates rendered variants in a loop. Render several labeled variants into one artifact, ask the model to rate them and suggest better values, render the suggestions, ask it to pick the best, repeat until good — the model is the eye, you run the loop.
How do I install it?
Run `npx skills add sickn33/agentic-awesome-skills --skill lookdev-auto --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 sickn33/agentic-awesome-skills, a repository with 44,414 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.