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 agentmods add skills/lanesket/llm.log/animatenpx skills add lanesket/llm.log --skill animategit clone --depth 1 https://github.com/lanesket/llm.logWrote 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/lanesket/llm.log/animate)<a href="https://agentmods.dev/skills/lanesket/llm.log/animate"><img src="https://agentmods.dev/badge/skills/lanesket/llm.log/animate.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00025 | $0.01749 |
| Opus 5 | $0.00013 | $0.00874 |
| Sonnet 5 | $0.00005 | $0.00350 |
| Haiku 4.5 | $0.00003 | $0.00175 |
Grade A, and why
animate 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 5d 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.
This is a copy
91% identical to animate — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze a feature and strategically add animations and micro-interactions that enhance understanding, provide feedback, and create delight.
MANDATORY PREPARATION
Use the frontend-design skill — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run teach-impeccable first. Additionally gather: performance constraints.
Assess Animation Opportunities
Analyze where motion would improve the experience:
-
Identify static areas:
- Missing feedback: Actions without visual acknowledgment (button clicks, form submission, etc.)
- Jarring transitions: Instant state changes that feel abrupt (show/hide, page loads, route changes)
- Unclear relationships: Spatial or hierarchical relationships that aren't obvious
- Lack of delight: Functional but joyless interactions
- Missed guidance: Opportunities to direct attention or explain behavior
-
Understand the context:
- What's the personality? (Playful vs serious, energetic vs calm)
- What's the performance budget? (Mobile-first? Complex page?)
- Who's the audience? (Motion-sensitive users? Power users who want speed?)
- What matters most? (One hero animation vs many micro-interactions?)
If any of these are unclear from the codebase, STOP and call the AskUserQuestion tool to clarify.
CRITICAL: Respect prefers-reduced-motion. Always provide non-animated alternatives for users who need them.
Plan Animation Strategy
Create a purposeful animation plan:
- Hero moment: What's the ONE signature animation? (Page load? Hero section? Key interaction?)
- Feedback layer: Which interactions need acknowledgment?
- Transition layer: Which state changes need smoothing?
- Delight layer: Where can we surprise and delight?
IMPORTANT: One well-orchestrated experience beats scattered animations everywhere. Focus on high-impact moments.
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.
- 5d ago First seen · 177 lines · 25 tokens per session scan A 2b4d91b01111
animate is a skill published in the GitHub repository lanesket/llm.log (22 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 1,749 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to animate, differing in 14 lines, and is treated as a copy.
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