animation-debug-visualization

A development aid for displaying animation data while investigating how an animation works or why it behaves incorrectly.

In plain words
What is it for?
Use it to debug animations, diagnose issues, and inspect data during optimization.
Why use it?
It makes timing and other animation problems easier to see, while requiring attention to accuracy and performance overhead.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/lgrappag/workflows-agents/animation-debug-visualization
Any agent
npx skills add LgrappaG/Workflows-Agents --skill animation-debug-visualization
Clone the repo
git clone --depth 1 https://github.com/LgrappaG/Workflows-Agents

Made for: Claude Code, Codex.

Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 254 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00014 $0.00254
Opus 5 $0.00007 $0.00127
Sonnet 5 $0.00003 $0.00051
Haiku 4.5 $0.00001 $0.00025

Measured 2d ago against content hash 3437e9801388, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

animation-debug-visualization 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 2d 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.

skills/animation-debug-visualization/SKILL.md · 49 lines

What it actually says

Animation Debug Visualization

Visualize animation data for debugging and optimization

Risk Level

LOW

Core Rules

  • Ensure visualization accuracy
  • minimize overhead
  • Test thoroughly before deploying

Response Pattern

When Using This Skill

  1. Implement debug visualization
  2. Validate the implementation
  3. Test edge cases and error conditions
  4. Ensure performance meets requirements

Usage Contexts

  • Animation debugging
  • issue diagnosis

What NOT to Do

  • Misleading visualizations
  • performance overhead
  • Deploy without testing

Key Requirements

  • Understand the use cases before application
  • Follow the documented response pattern
  • Validate results in the target environment
  • Monitor for performance impact

Further Learning

Review related skills and documentation for deeper understanding of related systems and best practices.

Changes

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.

  1. 2d ago First seen · 49 lines · 14 tokens per session scan A 3437e9801388

Subscribe to this mod's changes

animation-debug-visualization is a skill published in the GitHub repository LgrappaG/Workflows-Agents (2 stars, last pushed 4mo ago), licensed MIT. It adds 14 tokens to every session and 254 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 2026-08-31.

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