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Skill evidence / gsap-animation
Skill profile

gsap-animation

GSAP + Remotion integration for professional motion graphics video production.

What this skill does for you

GSAP + Remotion integration for professional motion graphics video production. Timeline orchestration, text splitting, SVG morphing, advanced easing, and reusable effect presets.

Quoted from the skill description in the pinned source

Who made it

Publishernotedit
Repositoryhappy-skills
Installs287as of Sep 27, 2026

Use it with Edge

Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the gsap-animation skill from notedit/happy-skills.

Paste it into Claude, ChatGPT, Codex or Cursor.

Or read the skill source first.

Security evidence

Security state at 2026-09-30: reviewed. State definitions. This records static evidence for this revision; live load eligibility is checked again.

3 static scans, 0 counted findings. The listed scanners recorded the results shown for this package revision.

ScannerDateResultFindings
Edge static checks2026-09-27Pass0
Cisco skill-scanner2026-09-27Pass0
Semgrep Edge rules2026-09-27Pass0

Static scans check the code, not how well the skill works.

Scanner scope, raw findings and mirrored provider records

Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: 9a2d593b620776eb5a8a5847efafb8a32e7c3875. Raw findings: . Counted findings: .

Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: 9a2d593b620776eb5a8a5847efafb8a32e7c3875. Raw findings: 1. Counted findings: .

Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: 9a2d593b620776eb5a8a5847efafb8a32e7c3875. Raw findings: . Counted findings: .

Source and licence

Repository
https://github.com/notedit/happy-skills
Pinned skill file
View source at revision
Revision
9a2d593b620776eb5a8a5847efafb8a32e7c3875
Package hash
sha256:926f981a92e5a73d8ffe57ad1bc25231118d199758baa490f019422c59acd0fd
Licence
MIT