Expert blueprint for programmatic animation using Tween for smooth property transitions, UI effects, camera movements, and juice. Covers easing functions, parallel tweens, chaining, and lifecycle management. Use when implementing UI animations OR procedural movement. Keywords Tween, easing, interpolation, EASE_IN_OUT, TRANS_CUBIC, tween_property, tween_callback.
npx skills add https://github.com/thedivergentai/GD-Agentic-Skills --skill godot-tweening
Tween property animation, easing curves, chaining, and lifecycle management define smooth programmatic motion.
| Situation | Choose |
|-----------|--------|
| One-off UI juice, hover, popup, score count, recoil | Tween (create_tween) |
| Authored multi-track clips, scrubbable timelines, blend trees | AnimationPlayer / AnimationTree |
| Camera continuous follow behind a moving target | Camera2D.position_smoothing / spring follow — not a new Tween every _process |
| Menu motion while Engine.time_scale == 0 | Tween with set_ignore_time_scale(true) — MANDATORY time_scale_ignored_ui.gd |
| Retriggerable property (button spam, dodge cancel) | Kill-before-recreate — MANDATORY safe_tween_interruption.gd |
| Physics body / net correction motion | TWEEN_PROCESS_PHYSICS + reset_physics_interpolation |
> MANDATORY: Read the script for the case above before writing tween glue.
MANDATORY for any retriggerable tween — kill active tweens before starting new ones.
MANDATORY for pause-menu / time_scale == 0 UI motion.
MANDATORY for composable cutscene timelines via tween_subtween.
set_parallel(true) + chain() for multi-property UI transitions.
tween_method for non-property values (score strings).
Curve resources for bespoke easing.
Procedural screen shake with looping tweens (offset, not follow).
as_relative() / from_current() for recoil nudges.
Sequential collection entry on one Tween.
Infinite ping-pong ambient juice.
Central juice dispatch / builder helpers when many systems share feel presets.
Tween.new() — Always use create_tween() or get_tree().create_tween() [3, 4].PropertyTweener or CallbackTweener — Only via parent Tween methods [5].kill() the old reference first [11, 12].EASE_OUT + TRANS_QUAD or EASE_IN_OUT + TRANS_CUBIC [22]._process without guards — Creating 60 tweens per second will crash the app.bind_node(self) for non-global tweens — Binding ensures death with the node [13].chain() when returning from set_parallel(true) [15].var _tween: Tween
func animate_to(pos: Vector2) -> void:
if _tween and _tween.is_valid():
_tween.kill()
_tween = create_tween().bind_node(self)
_tween.set_ease(Tween.EASE_OUT).set_trans(Tween.TRANS_QUAD)
_tween.tween_property(self, "position", pos, 0.35)
MANDATORY pattern source: safe_tween_interruption.gd.
Continuous follow is not a Tween job:
extends Camera2D
@export var target: Node2D
func _ready() -> void:
position_smoothing_enabled = true
position_smoothing_speed = 5.0
func _physics_process(_delta: float) -> void:
if target:
global_position = target.global_position
If you must tween a one-shot camera punch/return, keep one Tween reference and kill before recreate — never create_tween() inside unguarded _process.
func apply_physics_tween(target: Node3D, start_pos: Vector3, goal: Vector3) -> void:
target.global_position = start_pos
target.reset_physics_interpolation()
var tween := create_tween().bind_node(target)
tween.set_process_mode(Tween.TWEEN_PROCESS_PHYSICS)
tween.tween_property(target, "global_position", goal, 0.5)
Store duration/trans/ease in a Resource (see juice scripts) so feel is data-driven.
Parallel block → chain() → interval/callback → exit. Prefer nested_subtween_cutscene.gd for nested modules.
Tween PathFollow2D.progress_ratio instead of hand-rolled Bezier math.
> LLM-ignorance rule: if a general agent would not know it before reading, it lives here or in scripts/ — never delete, only move.
| Topic | Reference |
|-------|-----------|
| Chains, kill, gotchas | tween-recipes-and-gotchas.md |
> Progressive disclosure: open Official Documentation links only when researching a specific API; load Related Skills when routing to a peer domain — do not preload the whole lattice.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take thedivergentai/godot-tweening from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.