t1k:designer:base:feel-foundations

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Field	Value
Module	base
Version	1.13.2
Effort	high
Tools	—

Keywords: ADSR envelope, context metric, floaty, frame rate floor, game feel principles, game-feel theory, input metric, loose, metaphor metric, perception threshold, real-time control, response metric, response time, responsive, rules metric, simulated space, sluggish, swink, tight, twitchy, virtual sensation

How to invoke

/t1k:designer:base:feel-foundations

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Game Feel Foundations

When This Skill Triggers

• Diagnosing why a game feels floaty, twitchy, sluggish, mushy, or autopilot
• Establishing perception-threshold targets (frame budget, input-to-response latency)
• Measuring an existing prototype against the 6-metric framework
• Reverse-engineering the feel of a reference game into measurable parameters
• Auditing a build against the 8 principles of good game feel
• Defining what “feels right” before tuning numbers in a tactical pass
• Picking between theory (this skill) and tactics (game-feel-juice) when unsure

Decision Tree

User intent	Route to
”Make it feel better” / general / no diagnosis yet	6-metric framework + 8 principles checklist (this SKILL.md)
“Why is X floaty / twitchy / sluggish / mushy?”	references/metric-response.md (ADSR envelope diagnosis)
“What numbers should I target?”	references/definition-and-perception.md (perception thresholds)
“How do I reverse-engineer game X’s feel?”	references/case-study-methodology.md (12-step checklist)
“Audit my build against good-feel principles”	references/principles-checklist.md (8 yes/no audit tests)
“Polish, particles, screen shake, hit-stop, easing curves”	Defer to game-feel-juice (sibling skill, tactical toolbox)

The 3-Part Definition (Swink, p.6)

“Real-time control of virtual objects in a simulated space, with interactions emphasized by polish.”

A game has feel only when all three are present:

1. Real-time control — input maps to state every frame, not turn-by-turn
2. Virtual objects in a simulated space — the controlled thing exists in a spatial frame with consistent physics-or-pseudo-physics rules
3. Polish — sensory cues (visual + audio + haptic) emphasize each interaction

Missing any one of the three → not “game feel,” it’s something else (turn-based play, abstract simulation, or spreadsheet UI).

Perception Thresholds (hard numbers)

Threshold	Value	Source
Frame rate floor (motion illusion holds)	10 fps	Swink, p.43–44
Modern smoothness baseline	30+ fps	Swink, p.43–44
Response feels instant	≤50 ms	Swink, p.45
Response feels tight	50–100 ms	Swink, p.45
Response feels sluggish	100–150 ms	Swink, p.45
Response feels broken	>240 ms	Swink, p.45

Below 10 fps the eye loses motion illusion; above 240 ms input-to-response the player infers the game is broken or networked-laggy. Targets in between are genre- and verb-dependent.

→ Deep dive: references/definition-and-perception.md

The 6-Metric Framework

Swink decomposes “feel” into six measurable axes. Tune any one in isolation and you affect the whole, but each has a distinct knob set and anti-pattern.

Metric	Definition	Key Knob	Anti-Pattern	File
Input	The player’s control surface	sensitivity, dimensions, dead zone	mushy, oversensitive	references/metric-input.md
Response	Input → state mapping (ADSR envelope)	attack / decay / sustain / release	floaty (>100ms attack)	references/metric-response.md
Context	Spatial framing of the controlled object’s motion	object spacing, scale, camera framing	hemmed-in, autopilot	references/metric-context.md
Polish	Sensory cues that emphasize each interaction	animation curves, particles, audio decay	over-/under-/muddy-polish	references/metric-polish.md
Metaphor	Inferred physical reality (mass, weight, friction)	mass cues consistency across senses	lightweight giant	references/metric-metaphor.md
Rules	Meaning, agency, and difficulty around the action	difficulty curve, reward clarity	arbitrary, opaque	references/metric-rules.md

Reading order if you only have 30 minutes: Response → Polish → Input. These three account for most “this feels wrong” diagnoses.

The 8 Principles of Good Game Feel

#	Principle	Audit lens
1	Predictable Results	Same input + same state → same outcome
2	Instantaneous Response	Avatar moves on the input frame
3	Easy but Deep	Trivial to learn one verb, lifetime to master combinations
4	Novelty	Each level/system introduces a fresh input-response pairing
5	Appealing Response	Output is sensorially pleasant on its own
6	Organic Motion	Curves, not linear ramps; secondary motion
7	Harmony	All 6 metrics reinforce one feel, not contradict
8	Ownership	Player owns the wins AND the losses

Each principle has a yes/no audit test (observable from the shipped build, not designer introspection).

→ Audit tests: references/principles-checklist.md

30-Min Reverse-Engineering Methodology

When asked “why does game X feel so good — can we copy that?”, apply the 12-step methodology to extract X’s feel signature into measurable parameters you can re-implement.

→ Checklist + 5 worked cases: references/case-study-methodology.md

Polish-vs-Theory Decision Rule

Both game-feel-foundations and game-feel-juice cover “feel.” They are complementary, not redundant:

• Asking why a hit doesn’t feel meaty / how much polish is enough → this skill (Polish is one of the 6 metrics; framework lets you measure it)
• Asking how to add the meaty hit (screen-shake values, hit-stop frames, particle counts, easing curves) → game-feel-juice (tactical toolbox)
• Both skills MAY co-fire when the request needs theory + tactics together (e.g., “diagnose why my hits feel wrong AND tune the screen shake”)

Cross-References

• game-feel-juice — tactical polish toolbox (screen shake, hit-stop, particles, easing curves) — sibling skill in this module
• game-ux-design — UI/UX feedback budget vs HUD readability
• engine camera skills (auto-activated via registry) — camera-feel implementation
• engine animation skills (auto-activated via registry) — curve and tween implementation

Reference Files

File	Coverage
references/definition-and-perception.md	3-part definition, perception thresholds (frame rate, response time), interactivity loop, Fitts’ Law
references/metric-input.md	Player control surface, sensitivity, dimensions, dead zone, mushy/oversensitive anti-patterns
references/metric-response.md	ADSR envelope (attack/decay/sustain/release), floaty/twitchy diagnosis, value ranges per genre
references/metric-context.md	Spatial framing, object spacing, scale, camera context, hemmed-in/autopilot anti-patterns
references/metric-polish.md	Polish as a metric (theory) vs juice as toolbox (tactics); cross-link to game-feel-juice; audio-polish dimensions
references/metric-metaphor.md	Inferred physical reality, mass cues, consistency across senses, lightweight-giant anti-pattern
references/metric-rules.md	Meaning + agency, difficulty curve, reward clarity, arbitrary/opaque anti-patterns
references/principles-checklist.md	8 principles, each with an observable yes/no audit test
references/case-study-methodology.md	12-step reverse-engineering checklist + 5 worked summaries