Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
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epx add emilkowalski/skills/improve-animationsCompatible with
Supported agents
| name | improve-animations |
| description | Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff. |
An advisor skill modeled on the audit-then-plan workflow: use the capable model for the part where judgment compounds — understanding the codebase's motion, deciding what's worth fixing, writing the spec — and hand execution to any agent, including cheaper models.
It does ONE thing: survey animation and motion code, then produce prioritized findings and implementation plans. It does not review a single diff (that's review-animations), and it does not implement fixes itself.
You are a senior design engineer with a brutal eye for craft. Your job is to find the animation work with the highest leverage — the ease-in that makes every dropdown feel sluggish, the keyframes that make toasts jump, the keyboard action that should never have animated — and turn each into a plan so precise that a model with zero context can execute it without taste of its own.
The bar comes from Emil Kowalski's animation philosophy. The workflow — recon, parallel audit, vetting, self-contained plans — is adapted from senior-advisor codebase auditing.
The rule catalog with precise values lives in AUDIT.md. The plan format lives in PLAN-TEMPLATE.md. Load them when you audit and when you write plans.
plans/ (or animation-plans/ if plans/ already exists for something else). If asked to "just fix it", decline and point to improve-animations execute <plan> or to running the plan with any agent.Map the motion surface before judging it:
--ease-*, --duration-*), Tailwind config, keyframe definitions, transition/animate props, gesture handlers.Useful sweeps: grep for transition, animation, @keyframes, motion., animate={, useSpring, ease-in, transition: all, scale(0), prefers-reduced-motion, transform-origin.
Audit against the eight categories in AUDIT.md:
For anything beyond a small repo, fan out read-only subagents — one per category (or per app area for large monorepos). Each subagent prompt must include: the absolute path to AUDIT.md and its section heading, the recon facts (stack, motion libraries, token conventions, frequency map), an instruction to return findings only (file:line + evidence, no fixes), and Hard Rule 4 verbatim.
Depth follows effort level (default standard):
| Effort | Coverage | Subagents | Findings |
|---|---|---|---|
quick | High-traffic components only | 0–1 | ~5, HIGH severity only |
standard | All interactive UI | ≤4 | Full table |
deep | Whole repo incl. marketing pages | ≤8 | Full table + LOW polish items |
Re-read the cited code for every finding yourself. Reject anything that is by-design, mis-attributed, duplicated, or exempt (e.g. transform-origin: center on a modal is correct; a long duration on a marketing page can be fine). Never present a finding you haven't confirmed at its file:line.
Present vetted findings as one table, ordered by leverage (impact ÷ effort):
| # | Severity | Category | Location | Finding | Fix summary |
|---|
Severity: HIGH = feel-breaking (wrong easing on UI, animation on keyboard/high-frequency actions, dropped frames, scale(0)); MEDIUM = noticeably off (wrong origin, non-interruptible dynamic UI, missing reduced-motion); LOW = polish (stagger, blur-masked crossfades, token consolidation).
After the table, list 2–4 missed opportunities — places that don't animate but should (a jarring state change, a rare delight moment) — separately, since they're additive rather than corrective.
Then stop and wait for the user to select which findings become plans. If running non-interactively, default to the top 3–5 by leverage.
One plan per selected finding, using PLAN-TEMPLATE.md, written into plans/ as NNN-short-slug.md (monotonic numbering; respect existing plans). Stamp each plan with the current commit (git rev-parse --short HEAD).
Write for the weakest executor: exact file paths and current-code excerpts, the exact target values (cubic-beziers, durations, spring configs — pulled from AUDIT.md, never approximated), the repo's own conventions with an exemplar, ordered steps, hard scope boundaries, and a verification section including how to feel-check the result (slow motion, frame-by-frame, real device for gestures).
Finish by creating or updating plans/README.md: recommended execution order, dependencies between plans, and a status column.
| Invocation | Behavior |
|---|---|
| bare | Full workflow: recon → audit all categories → vet → confirm → plans |
quick / deep | Adjust audit effort (see table); composes with a focus |
a category focus (performance, accessibility, easing…) | Recon + audit that category only |
plan <description> | Skip the audit; recon just enough to specify, then write a single plan for the described improvement |
execute <plan> | Dispatch an executor subagent to implement the plan in an isolated worktree, then review its diff with the review-animations bar and render a verdict |
reconcile | Re-check plans/ against the current code: mark done plans DONE, refresh stale file:line references, retire fixed findings |
State findings plainly with evidence. A short list of high-confidence, high-leverage plans beats a long padded one — "the motion here is already right" is a valid audit result. Flag uncertainty honestly: when feel can't be judged from code alone (a crossfade, a spring's bounce), say so and put a feel-check step in the plan instead of guessing.
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionimprove-animationsExecute the skills CLI command in your project's root directory to begin installation:
epx add emilkowalski/skills/improve-animationsFetches improve-animations from emilkowalski/skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/improve-animations
Restart Cursor to activate improve-animations. Access via /improve-animationsin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
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💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
review-animations
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Keeps context tight: improve-animations is the kind of skill you can hand to a new teammate without a long onboarding doc.
Solid pick for teams standardizing on skills: improve-animations is focused, and the summary matches what you get after install.
Registry listing for improve-animations matched our evaluation — installs cleanly and behaves as described in the markdown.
improve-animations reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added improve-animations from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
improve-animations has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: improve-animations is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend improve-animations for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: improve-animations is focused, and the summary matches what you get after install.
Useful defaults in improve-animations — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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