Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add dylantarre/animation-principles --skill user-feedback-claritygit clone --depth 1 https://github.com/dylantarre/animation-principlesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dylantarre/animation-principles/user-feedback-clarity)<a href="https://agentmods.dev/skills/dylantarre/animation-principles/user-feedback-clarity"><img src="https://agentmods.dev/badge/skills/dylantarre/animation-principles/user-feedback-clarity/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dylantarre/animation-principles/user-feedback-clarity"><img src="https://agentmods.dev/badge/skills/dylantarre/animation-principles/user-feedback-clarity.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00024 | $0.00611 |
| Opus 5 | $0.00012 | $0.00305 |
| Sonnet 5 | $0.00005 | $0.00122 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
user-feedback-clarity scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
User Feedback Clarity
Make feedback animations unmissable using Disney's principles.
Problem Indicators
- Users click multiple times (didn't see feedback)
- "Did it work?" questions
- Users miss success/error states
- Form submission confusion
- State changes go unnoticed
Diagnosis by Principle
Anticipation
Issue: No buildup before action completes Fix: Show loading/processing state immediately. User should know their input was received.
Follow Through
Issue: Feedback appears and vanishes too quickly Fix: Let feedback linger. Success messages need 2-3 seconds minimum. Add settle animation.
Staging
Issue: Feedback appears outside user's focus Fix: Show feedback near the trigger. If button was clicked, feedback should appear on/near button.
Exaggeration
Issue: Feedback is too subtle Fix: Increase contrast, size change, or motion. Feedback must compete with user's task focus.
Secondary Action
Issue: Only one feedback channel Fix: Combine channels: visual + motion + color. Error = red + shake. Success = green + checkmark + pulse.
Quick Fixes
- Immediate acknowledgment - Show something within 100ms
- Animate the trigger - Button should respond visibly
- Use color + motion together - Redundant signals
- Keep feedback in viewport - Near user's focus
- Add haptic feedback - On supported devices
Troubleshooting Checklist
- Does feedback appear within 100ms of action?
- Is feedback in user's current focus area?
- Would feedback be noticed peripherally?
- Are multiple senses engaged (visual, motion)?
- Does feedback last long enough to read?
- Is error feedback more prominent than success?
- Test: Can users tell if action succeeded without reading text?
- Test with users—ask "did that work?"
Code Pattern
/* Button feedback */
.button:active {
transform: scale(0.95);
}
.button.loading {
pointer-events: none;
}
.button.success {
animation: successPulse 300ms ease-out;
}
@keyframes successPulse {
0% { transform: scale(1); }
50% { transform: scale(1.05); background: var(--success); }
100% { transform: scale(1); }
}
/* Error shake */
@keyframes errorShake {
0%, 100% { transform: translateX(0); }
25% { transform: translateX(-4px); }
75% { transform: translateX(4px); }
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 87 lines · 24 tokens per session scan A 737bc0e0414d
user-feedback-clarity is a skill published in the GitHub repository dylantarre/animation-principles (81 stars, last pushed 8mo ago), licensed MIT. It adds 24 tokens to every session and 611 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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