game-design-player-values-mapper

Name: game-design-player-values-mapper
Author: Stanestane

34stars3forks

How to install game-design-player-values-mapper?

stanestane/game-design-skills-bundle/game-design-player-values-mapper

$npx -y skills add Stanestane/game-design-skills-bundle --skill game-design-player-values-mapper

Installs into the current project.

Summary

Infer a player's underlying values and motivational priorities from behavior, then translate those into design implications. Use when designing personalization, segmentation, dynamic guidance, live-ops targeting, adaptive missions, re-engagement strategies, or feature prioritizat

Files · 1

View on GitHub

SKILL.md

1	# Game Design Player Values Mapper
2
3	Map observed player behavior to likely underlying value priorities, then use that map to infer what kinds of goals, rewards, content, or framing are most likely to resonate.
4
5	Use this skill when the team needs to understand not just what players do, but what those choices imply about what they care about.
6
7	## Core principle
8
9	Behavior is not random. It is preference made visible.
10
11	Players reveal their values through repetition, avoidance, investment, and attention. The goal is not to assign a rigid personality label, but to infer the motivational structure most likely driving current behavior and use that to improve design alignment.
12
13	## What to produce
14
15	Generate:
16	1. Observed behavior summary - what the player consistently does, ignores, and invests in
17	2. Value map - likely dominant, secondary, and weak values
18	3. Confidence notes - how strong or ambiguous each inference is
19	4. Tensions or contradictions - where behavior suggests mixed motives or blocked values
20	5. Design implications - what systems, content, messaging, goals, or monetization surfaces are likely aligned or misaligned
21	6. Segment hypothesis - what kind of player pattern this most resembles in practical design terms
22	7. Recommendations - what to emphasize, reframe, personalize, or stop pushing
23
24	## Value framework
25
26	Map behavior to these value dimensions:
27
28	- Efficiency / Optimization
29	- Progression / Growth
30	- Aesthetics / Expression
31	- Collection / Completion
32	- Social Recognition / Status
33	- Experimentation / Discovery
34	- Narrative / Meaning
35
36	You may add a clearly justified extra value if the case demands it, but do not bloat the framework casually.
37
38	## Process
39
40	### 1. Gather behavior signals
41
42	List concrete observed behaviors.
43
44	Possible sources:
45	- build patterns
46	- resource spending
47	- session frequency and duration
48	- event participation
49	- feature engagement
50	- purchase behavior
51	- social behavior
52	- what the player returns to repeatedly
53	- what the player ignores despite obvious rewards
54
55	Write:
56	- Repeated behaviors
57	- Avoided behaviors
58	- Investment patterns
59
60	### 2. Map behaviors to likely value signals
61
62	Translate behavior into value hypotheses.
63
64	Examples:
65	- min-maxing production chains -> Efficiency / Optimization
66	- constant upgrading and rushing unlocks -> Progression / Growth
67	- decorating, styling, curating loadouts -> Aesthetics / Expression
68	- chasing every item or badge -> Collection / Completion
69	- caring about ranks, cosmetics, visibility -> Social Recognition / Status
70	- trying odd builds or niche tools -> Experimentation / Discovery
71	- following lore, theme, faction identity, story arcs -> Narrative / Meaning
72
73	Important: many behaviors can map to more than one value. Do not overclaim certainty.
74
75	### 3. Weight the value profile
76
77	Do not force fake precision. The goal is a useful profile, not pseudo-scientific certainty.
78
79	Assign rough weight levels such as:
80	- High
81	- Medium
82	- Low
83
84	Or if needed:
85	- Dominant
86	- Secondary
87	- Weak
88	- Absent
89
90	Also note confidence:
91	- high confidence
92	- medium confidence
93	- low confidence
94
95	Use this format:
96
97	\| Value \| Weight \| Confidence \| Evidence \|
98	\|---\|---\|---\|---\|
99	\| ... \| ... \| ... \| ... \|
100
101	### 4. Detect tensions and blocked values
102
103	Look for contradictions.
104
105	Examples:
106	- optimization-driven player engaging with decoration only because progression forces it
107	- status-seeking player avoiding competition because the failure cost feels humiliating
108	- progression-oriented player not spending because they distrust the offer structure
109	- discovery-oriented player repeating safe loops because experimentation is too punished
110
111	Ask:
112	- is this a real mixed-value profile?
113	- or is one value being blocked by system design?
114
115	### 5. Infer likely design alignment
116
117	Answer:
118	- what currently motivates this player most?
119	- what kinds of content or objectives will likely land well?
120	- what incentives are probably weak for this player?
121	- where is the game asking for a value the player does not strongly hold?
122	- what part of the experience is likely causing silent disengagement?
123	- what messaging, reward framing, or mission framing is most likely to resonate?
124
125	### 6. Form a practical segment hypothesis
126
127	Translate the value map into a practical design-facing player pattern.
128
129	Examples:
130	- efficiency-first optimizer
131	- completionist collector with moderate status drive
132	- expressive builder with weak progression urgency
133	- growth-focu

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Type

game-design-player-values-mapper

34stars3forks

View on GitHub

How to install game-design-player-values-mapper?

stanestane/game-design-skills-bundle/game-design-player-values-mapper

$npx -y skills add Stanestane/game-design-skills-bundle --skill game-design-player-values-mapper

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Game Design Player Values Mapper
2
3	Map observed player behavior to likely underlying value priorities, then use that map to infer what kinds of goals, rewards, content, or framing are most likely to resonate.
4
5	Use this skill when the team needs to understand not just what players do, but what those choices imply about what they care about.
6
7	## Core principle
8
9	Behavior is not random. It is preference made visible.
10
11	Players reveal their values through repetition, avoidance, investment, and attention. The goal is not to assign a rigid personality label, but to infer the motivational structure most likely driving current behavior and use that to improve design alignment.
12
13	## What to produce
14
15	Generate:
16	1. Observed behavior summary - what the player consistently does, ignores, and invests in
17	2. Value map - likely dominant, secondary, and weak values
18	3. Confidence notes - how strong or ambiguous each inference is
19	4. Tensions or contradictions - where behavior suggests mixed motives or blocked values
20	5. Design implications - what systems, content, messaging, goals, or monetization surfaces are likely aligned or misaligned
21	6. Segment hypothesis - what kind of player pattern this most resembles in practical design terms
22	7. Recommendations - what to emphasize, reframe, personalize, or stop pushing
23
24	## Value framework
25
26	Map behavior to these value dimensions:
27
28	- Efficiency / Optimization
29	- Progression / Growth
30	- Aesthetics / Expression
31	- Collection / Completion
32	- Social Recognition / Status
33	- Experimentation / Discovery
34	- Narrative / Meaning
35
36	You may add a clearly justified extra value if the case demands it, but do not bloat the framework casually.
37
38	## Process
39
40	### 1. Gather behavior signals
41
42	List concrete observed behaviors.
43
44	Possible sources:
45	- build patterns
46	- resource spending
47	- session frequency and duration
48	- event participation
49	- feature engagement
50	- purchase behavior
51	- social behavior
52	- what the player returns to repeatedly
53	- what the player ignores despite obvious rewards
54
55	Write:
56	- Repeated behaviors
57	- Avoided behaviors
58	- Investment patterns
59
60	### 2. Map behaviors to likely value signals
61
62	Translate behavior into value hypotheses.
63
64	Examples:
65	- min-maxing production chains -> Efficiency / Optimization
66	- constant upgrading and rushing unlocks -> Progression / Growth
67	- decorating, styling, curating loadouts -> Aesthetics / Expression
68	- chasing every item or badge -> Collection / Completion
69	- caring about ranks, cosmetics, visibility -> Social Recognition / Status
70	- trying odd builds or niche tools -> Experimentation / Discovery
71	- following lore, theme, faction identity, story arcs -> Narrative / Meaning
72
73	Important: many behaviors can map to more than one value. Do not overclaim certainty.
74
75	### 3. Weight the value profile
76
77	Do not force fake precision. The goal is a useful profile, not pseudo-scientific certainty.
78
79	Assign rough weight levels such as:
80	- High
81	- Medium
82	- Low
83
84	Or if needed:
85	- Dominant
86	- Secondary
87	- Weak
88	- Absent
89
90	Also note confidence:
91	- high confidence
92	- medium confidence
93	- low confidence
94
95	Use this format:
96
97	\| Value \| Weight \| Confidence \| Evidence \|
98	\|---\|---\|---\|---\|
99	\| ... \| ... \| ... \| ... \|
100
101	### 4. Detect tensions and blocked values
102
103	Look for contradictions.
104
105	Examples:
106	- optimization-driven player engaging with decoration only because progression forces it
107	- status-seeking player avoiding competition because the failure cost feels humiliating
108	- progression-oriented player not spending because they distrust the offer structure
109	- discovery-oriented player repeating safe loops because experimentation is too punished
110
111	Ask:
112	- is this a real mixed-value profile?
113	- or is one value being blocked by system design?
114
115	### 5. Infer likely design alignment
116
117	Answer:
118	- what currently motivates this player most?
119	- what kinds of content or objectives will likely land well?
120	- what incentives are probably weak for this player?
121	- where is the game asking for a value the player does not strongly hold?
122	- what part of the experience is likely causing silent disengagement?
123	- what messaging, reward framing, or mission framing is most likely to resonate?
124
125	### 6. Form a practical segment hypothesis
126
127	Translate the value map into a practical design-facing player pattern.
128
129	Examples:
130	- efficiency-first optimizer
131	- completionist collector with moderate status drive
132	- expressive builder with weak progression urgency
133	- growth-focu

People who liked this also viewed

6 picks

Type