product-analyst

Name: product-analyst
Author: ncklrs

175installs3forks

How to install product-analyst?

ncklrs/startup-os-skills/product-analyst

$npx -y skills add ncklrs/startup-os-skills --skill product-analyst

Installs into the current project.

Summary

Expert product analytics strategist for SaaS and digital products. Use when designing product metrics frameworks, funnel analysis, cohort retention, feature adoption tracking, A/B testing, experimentation design, data instrumentation, or product dashboards. Covers AARRR, HEART, b

Files · 1

View on GitHub

SKILL.md

1	# Product Analyst
2
3	Strategic product analytics expertise for data-driven product decisions — from metrics framework selection to experimentation design and impact measurement.
4
5	## Philosophy
6
7	Great product analytics isn't about tracking everything. It's about measuring what matters to drive better product decisions.
8
9	The best product analytics:
10	1. Start with decisions, not data — What will you do differently based on this metric?
11	2. Instrument once, measure forever — Invest in solid event tracking upfront
12	3. Balance leading and lagging — Predict outcomes, don't just report them
13	4. Make data accessible — Self-serve dashboards beat SQL queues
14	5. Experiment before you ship — Validate hypotheses with real users
15
16	## How This Skill Works
17
18	When invoked, apply the guidelines in `rules/` organized by:
19
20	- `metrics-*` — Frameworks (AARRR, HEART), KPI selection, metric hierarchies
21	- `funnel-*` — Conversion analysis, drop-off diagnosis, optimization
22	- `cohort-*` — Retention analysis, segmentation, lifecycle tracking
23	- `feature-*` — Adoption tracking, usage patterns, feature success
24	- `experiment-*` — A/B testing, hypothesis design, statistical rigor
25	- `instrumentation-*` — Event tracking, data modeling, collection best practices
26	- `dashboard-*` — Visualization, stakeholder reporting, self-serve analytics
27
28	## Core Frameworks
29
30	### AARRR (Pirate Metrics)
31
32	\| Stage \| Question \| Key Metrics \|
33	\|-------\|----------\|-------------\|
34	\| Acquisition \| Where do users come from? \| Traffic sources, CAC, signup rate \|
35	\| Activation \| Do they have a great first experience? \| Time-to-value, setup completion, aha moment \|
36	\| Retention \| Do they come back? \| DAU/MAU, D1/D7/D30 retention, churn \|
37	\| Revenue \| Do they pay? \| Conversion rate, ARPU, LTV \|
38	\| Referral \| Do they tell others? \| NPS, referral rate, viral coefficient \|
39
40	### HEART Framework (Google)
41
42	\| Dimension \| Definition \| Signal Types \|
43	\|-----------\|------------\|--------------\|
44	\| Happiness \| User attitudes, satisfaction \| NPS, CSAT, surveys \|
45	\| Engagement \| Depth of involvement \| Sessions, time-in-app, actions/session \|
46	\| Adoption \| New users/features uptake \| New users, feature adoption % \|
47	\| Retention \| Continued usage over time \| Retention curves, churn rate \|
48	\| Task Success \| Efficiency and completion \| Task completion, error rate, time-on-task \|
49
50	### The Metrics Hierarchy
51
52	```
53	┌─────────────────┐
54	│ North Star │ ← Single metric that matters most
55	│ Metric │
56	├─────────────────┤
57	│ Primary │ ← 3-5 key performance indicators
58	│ KPIs │
59	├─────────────────┤
60	│ Supporting │ ← Diagnostic and health metrics
61	│ Metrics │
62	├─────────────────┤
63	│ Operational │ ← Day-to-day tracking
64	│ Metrics │
65	└─────────────────┘
66	```
67
68	### Retention Analysis Types
69
70	```
71	┌───────────────────────────────────────────────────────────┐
72	│ RETENTION VIEWS │
73	├───────────────────────────────────────────────────────────┤
74	│ N-Day Retention │ % who return on exactly day N │
75	│ Unbounded │ % who return on or after day N │
76	│ Bracket Retention │ % who return within a time window │
77	│ Rolling Retention │ % still active after N days │
78	└───────────────────────────────────────────────────────────┘
79	```
80
81	### Experimentation Rigor Ladder
82
83	\| Level \| Approach \| When to Use \|
84	\|-------\|----------\|-------------\|
85	\| 1. Gut \| Ship and hope \| Never for important features \|
86	\| 2. Qualitative \| User research, feedback \| Early exploration \|
87	\| 3. Observational \| Pre/post analysis \| Low-risk changes \|
88	\| 4. Quasi-experiment \| Cohort comparison \| When randomization hard \|
89	\| 5. A/B Test \| Randomized control \| Optimization, validation \|
90	\| 6. Multi-arm Bandit \| Adaptive allocation \| When speed > precision \|
91
92	## Metric Selection Criteria
93
94	\| Criterion \| Question \| Good Sign \|
95	\|-----------\|----------\|-----------\|
96	\| Actionable \| Can we influence this? \| Direct lever exists \|
97	\| Accessible \| Can we measure it reliably? \| <5% missing data \|
98	\| Auditable \| Can we debug anomalies? \| Clear calculation logic \|
99	\| Aligned \| Does it tie to business value? \| Executive cares \|
100	\| Attributable \| Can we trace changes to causes? \| A/B testable \|
101
102	## Anti-Patterns
103
104	- Vanity metrics — Tracking what looks good, not wh

People who liked this also viewed

6 picks

Type

product-analyst

175installs3forks

View on GitHub

How to install product-analyst?

ncklrs/startup-os-skills/product-analyst

$npx -y skills add ncklrs/startup-os-skills --skill product-analyst

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Product Analyst
2
3	Strategic product analytics expertise for data-driven product decisions — from metrics framework selection to experimentation design and impact measurement.
4
5	## Philosophy
6
7	Great product analytics isn't about tracking everything. It's about measuring what matters to drive better product decisions.
8
9	The best product analytics:
10	1. Start with decisions, not data — What will you do differently based on this metric?
11	2. Instrument once, measure forever — Invest in solid event tracking upfront
12	3. Balance leading and lagging — Predict outcomes, don't just report them
13	4. Make data accessible — Self-serve dashboards beat SQL queues
14	5. Experiment before you ship — Validate hypotheses with real users
15
16	## How This Skill Works
17
18	When invoked, apply the guidelines in `rules/` organized by:
19
20	- `metrics-*` — Frameworks (AARRR, HEART), KPI selection, metric hierarchies
21	- `funnel-*` — Conversion analysis, drop-off diagnosis, optimization
22	- `cohort-*` — Retention analysis, segmentation, lifecycle tracking
23	- `feature-*` — Adoption tracking, usage patterns, feature success
24	- `experiment-*` — A/B testing, hypothesis design, statistical rigor
25	- `instrumentation-*` — Event tracking, data modeling, collection best practices
26	- `dashboard-*` — Visualization, stakeholder reporting, self-serve analytics
27
28	## Core Frameworks
29
30	### AARRR (Pirate Metrics)
31
32	\| Stage \| Question \| Key Metrics \|
33	\|-------\|----------\|-------------\|
34	\| Acquisition \| Where do users come from? \| Traffic sources, CAC, signup rate \|
35	\| Activation \| Do they have a great first experience? \| Time-to-value, setup completion, aha moment \|
36	\| Retention \| Do they come back? \| DAU/MAU, D1/D7/D30 retention, churn \|
37	\| Revenue \| Do they pay? \| Conversion rate, ARPU, LTV \|
38	\| Referral \| Do they tell others? \| NPS, referral rate, viral coefficient \|
39
40	### HEART Framework (Google)
41
42	\| Dimension \| Definition \| Signal Types \|
43	\|-----------\|------------\|--------------\|
44	\| Happiness \| User attitudes, satisfaction \| NPS, CSAT, surveys \|
45	\| Engagement \| Depth of involvement \| Sessions, time-in-app, actions/session \|
46	\| Adoption \| New users/features uptake \| New users, feature adoption % \|
47	\| Retention \| Continued usage over time \| Retention curves, churn rate \|
48	\| Task Success \| Efficiency and completion \| Task completion, error rate, time-on-task \|
49
50	### The Metrics Hierarchy
51
52	```
53	┌─────────────────┐
54	│ North Star │ ← Single metric that matters most
55	│ Metric │
56	├─────────────────┤
57	│ Primary │ ← 3-5 key performance indicators
58	│ KPIs │
59	├─────────────────┤
60	│ Supporting │ ← Diagnostic and health metrics
61	│ Metrics │
62	├─────────────────┤
63	│ Operational │ ← Day-to-day tracking
64	│ Metrics │
65	└─────────────────┘
66	```
67
68	### Retention Analysis Types
69
70	```
71	┌───────────────────────────────────────────────────────────┐
72	│ RETENTION VIEWS │
73	├───────────────────────────────────────────────────────────┤
74	│ N-Day Retention │ % who return on exactly day N │
75	│ Unbounded │ % who return on or after day N │
76	│ Bracket Retention │ % who return within a time window │
77	│ Rolling Retention │ % still active after N days │
78	└───────────────────────────────────────────────────────────┘
79	```
80
81	### Experimentation Rigor Ladder
82
83	\| Level \| Approach \| When to Use \|
84	\|-------\|----------\|-------------\|
85	\| 1. Gut \| Ship and hope \| Never for important features \|
86	\| 2. Qualitative \| User research, feedback \| Early exploration \|
87	\| 3. Observational \| Pre/post analysis \| Low-risk changes \|
88	\| 4. Quasi-experiment \| Cohort comparison \| When randomization hard \|
89	\| 5. A/B Test \| Randomized control \| Optimization, validation \|
90	\| 6. Multi-arm Bandit \| Adaptive allocation \| When speed > precision \|
91
92	## Metric Selection Criteria
93
94	\| Criterion \| Question \| Good Sign \|
95	\|-----------\|----------\|-----------\|
96	\| Actionable \| Can we influence this? \| Direct lever exists \|
97	\| Accessible \| Can we measure it reliably? \| <5% missing data \|
98	\| Auditable \| Can we debug anomalies? \| Clear calculation logic \|
99	\| Aligned \| Does it tie to business value? \| Executive cares \|
100	\| Attributable \| Can we trace changes to causes? \| A/B testable \|
101
102	## Anti-Patterns
103
104	- Vanity metrics — Tracking what looks good, not wh

People who liked this also viewed

6 picks

Type