growth-loops

Name: growth-loops
Author: Productfculty-aipm

70stars33forks

How to install growth-loops?

productfculty-aipm/pm-copilot-by-product-faculty/growth-loops

$npx -y skills add Productfculty-aipm/PM-Copilot-by-Product-Faculty --skill growth-loops

Installs into the current project.

Summary

Use this skill when the user asks about "growth loops", "viral loops", "product-led growth", "PLG", "how does our product grow itself", "referral mechanics", "word of mouth", "how to build growth into the product", "organic growth", or wants to identify and design the mechanisms

Files · 1

View on GitHub

SKILL.md

1	# Growth Loops
2
3	You are helping the user identify and design growth loops — self-reinforcing cycles where product usage generates new users, which generates more usage, compounding over time.
4
5	Framework: Lenny Rachitsky (growth loops), Andrew Chen (network effects and viral growth), Brian Balfour (loops vs. funnels).
6
7	Key insight: Funnels leak. Loops compound. A product with a strong growth loop doesn't need to keep increasing acquisition spend — each cohort of users generates the next cohort.
8
9	## Step 1 — Load Context
10
11	Read `memory/user-profile.md` for product stage, business model, and current bets. Read `context/product/personas.md` for user type and behavior patterns.
12
13	## Step 2 — Growth Loop Types
14
15	Identify which types of loops are possible for this product:
16
17	Viral / social loops:
18	User uses product → creates something shareable → shares with others → new users discover the product → they sign up
19	Example: User creates a shareable PRD → shares on LinkedIn → peers see it and want to create their own
20
21	Content loops:
22	Users generate or curate content → content is indexed → search brings new users → they become users and generate more content
23	Example: PM shares their research synthesis publicly → Google indexes it → other PMs find it and discover PM Copilot
24
25	Word of mouth loops (engineered):
26	User gets value → built-in mechanism prompts sharing → they share → referral drives new user
27	Example: After a successful PRD, PM Copilot surfaces: "Your PRD looks great. Want to share it with your team or post it to LinkedIn?"
28
29	Integration loops:
30	User integrates product into their stack → other users in the same stack discover it → they request access
31	Example: User connects PM Copilot to Linear → Linear ticket shows "Generated with PM Copilot" → other team members ask to use it
32
33	Product-led loops:
34	Users invite others to get more value → invited users also get value → they invite more users
35	Example: Stakeholder receives PM Copilot-generated update → quality of update prompts them to ask for the tool → they become users
36
37	Network effect loops:
38	Product gets better as more users join → better product attracts more users
39	Example: Shared memory layer across teams → team templates improve → more PMs adopt it
40
41	## Step 3 — Loop Identification
42
43	For the user's specific product, identify:
44	- Which loop types are structurally possible given the product's nature?
45	- Which loops already exist (even informally)?
46	- Which loops would be highest-leverage to engineer?
47
48	Rate each possible loop on:
49	- Potential magnitude: How many new users could each iteration of this loop generate?
50	- Friction to activate: How hard is it for a user to complete the loop action?
51	- Speed: How quickly does the loop cycle? (Days vs. weeks vs. months)
52	- Build effort: How much engineering is needed to create or improve this loop?
53
54	## Step 4 — Loop Design
55
56	For the top 1–2 loops identified:
57
58	Map the full loop: Start → Action → Output → New User Touchpoint → Conversion → Back to Start
59
60	Identify the friction points: Where does the loop break or slow down?
61
62	Design the intervention: What product or marketing change would close the loop or reduce friction?
63
64	Measurement: How will you know if the loop is working? (What's the loop conversion rate? How many new users does one loop cycle generate?)
65
66	## Step 5 — PLG vs. Sales-Assisted
67
68	Based on the ICP and product:
69	- Can the product sell itself (PLG) or does it need sales assist?
70	- If PLG: what's the free-to-paid trigger? What in-product experience drives conversion?
71	- If sales-assisted: where does the loop end and the sales conversation begin?
72
73	For PM Copilot: PLG is the natural motion — users install the plugin themselves. The loop is: user uses PM Copilot → generates a high-quality output → shares output → peer asks "how did you make that?" → discovers and installs PM Copilot.
74
75	## Step 6 — Output
76
77	Produce:
78	- Loop identification (which loops exist and which are possible)
79	- Loop prioritization (highest leverage × lowest effort)
80	- Full loop design for top 1–2 loops
81	- Measurement framework for each loop (inputs, outputs, cycle rate)
82	- First experiment to test whether a proposed loop actually works

Productfculty-aipmPublisher · 69 listings

Preview

Productfculty-aipm/PM-Copilot-by-Product-Faculty

# Growth Loops

You are helping the user identify and design growth loops — self-reinforcing cycles where product usage generates new users, which generates more usage, compoun

Framework: Lenny Rachitsky (growth loops), Andrew Chen (network effects and viral growth), Brian Balfour (loops vs. funnels).

Key insight: Funnels leak. Loops compound. A product with a strong growth loop doesn't need to keep increasing acquisition spend — each cohort of users generate

RepoProductfculty-aipm/PM-Copilot-by-Product-Faculty

License—

TypeSkills

CategoryPlanning

UpdatedApr 2026

Repo stars70

First seenJul 22, 2026

People who liked this also viewed

6 picks

Type

growth-loops

70stars33forks

View on GitHub

How to install growth-loops?

productfculty-aipm/pm-copilot-by-product-faculty/growth-loops

$npx -y skills add Productfculty-aipm/PM-Copilot-by-Product-Faculty --skill growth-loops

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Growth Loops
2
3	You are helping the user identify and design growth loops — self-reinforcing cycles where product usage generates new users, which generates more usage, compounding over time.
4
5	Framework: Lenny Rachitsky (growth loops), Andrew Chen (network effects and viral growth), Brian Balfour (loops vs. funnels).
6
7	Key insight: Funnels leak. Loops compound. A product with a strong growth loop doesn't need to keep increasing acquisition spend — each cohort of users generates the next cohort.
8
9	## Step 1 — Load Context
10
11	Read `memory/user-profile.md` for product stage, business model, and current bets. Read `context/product/personas.md` for user type and behavior patterns.
12
13	## Step 2 — Growth Loop Types
14
15	Identify which types of loops are possible for this product:
16
17	Viral / social loops:
18	User uses product → creates something shareable → shares with others → new users discover the product → they sign up
19	Example: User creates a shareable PRD → shares on LinkedIn → peers see it and want to create their own
20
21	Content loops:
22	Users generate or curate content → content is indexed → search brings new users → they become users and generate more content
23	Example: PM shares their research synthesis publicly → Google indexes it → other PMs find it and discover PM Copilot
24
25	Word of mouth loops (engineered):
26	User gets value → built-in mechanism prompts sharing → they share → referral drives new user
27	Example: After a successful PRD, PM Copilot surfaces: "Your PRD looks great. Want to share it with your team or post it to LinkedIn?"
28
29	Integration loops:
30	User integrates product into their stack → other users in the same stack discover it → they request access
31	Example: User connects PM Copilot to Linear → Linear ticket shows "Generated with PM Copilot" → other team members ask to use it
32
33	Product-led loops:
34	Users invite others to get more value → invited users also get value → they invite more users
35	Example: Stakeholder receives PM Copilot-generated update → quality of update prompts them to ask for the tool → they become users
36
37	Network effect loops:
38	Product gets better as more users join → better product attracts more users
39	Example: Shared memory layer across teams → team templates improve → more PMs adopt it
40
41	## Step 3 — Loop Identification
42
43	For the user's specific product, identify:
44	- Which loop types are structurally possible given the product's nature?
45	- Which loops already exist (even informally)?
46	- Which loops would be highest-leverage to engineer?
47
48	Rate each possible loop on:
49	- Potential magnitude: How many new users could each iteration of this loop generate?
50	- Friction to activate: How hard is it for a user to complete the loop action?
51	- Speed: How quickly does the loop cycle? (Days vs. weeks vs. months)
52	- Build effort: How much engineering is needed to create or improve this loop?
53
54	## Step 4 — Loop Design
55
56	For the top 1–2 loops identified:
57
58	Map the full loop: Start → Action → Output → New User Touchpoint → Conversion → Back to Start
59
60	Identify the friction points: Where does the loop break or slow down?
61
62	Design the intervention: What product or marketing change would close the loop or reduce friction?
63
64	Measurement: How will you know if the loop is working? (What's the loop conversion rate? How many new users does one loop cycle generate?)
65
66	## Step 5 — PLG vs. Sales-Assisted
67
68	Based on the ICP and product:
69	- Can the product sell itself (PLG) or does it need sales assist?
70	- If PLG: what's the free-to-paid trigger? What in-product experience drives conversion?
71	- If sales-assisted: where does the loop end and the sales conversation begin?
72
73	For PM Copilot: PLG is the natural motion — users install the plugin themselves. The loop is: user uses PM Copilot → generates a high-quality output → shares output → peer asks "how did you make that?" → discovers and installs PM Copilot.
74
75	## Step 6 — Output
76
77	Produce:
78	- Loop identification (which loops exist and which are possible)
79	- Loop prioritization (highest leverage × lowest effort)
80	- Full loop design for top 1–2 loops
81	- Measurement framework for each loop (inputs, outputs, cycle rate)
82	- First experiment to test whether a proposed loop actually works

Productfculty-aipmPublisher · 69 listings

Preview

Productfculty-aipm/PM-Copilot-by-Product-Faculty

# Growth Loops

You are helping the user identify and design growth loops — self-reinforcing cycles where product usage generates new users, which generates more usage, compoun

Framework: Lenny Rachitsky (growth loops), Andrew Chen (network effects and viral growth), Brian Balfour (loops vs. funnels).

Key insight: Funnels leak. Loops compound. A product with a strong growth loop doesn't need to keep increasing acquisition spend — each cohort of users generate

RepoProductfculty-aipm/PM-Copilot-by-Product-Faculty

License—

TypeSkills

CategoryPlanning

UpdatedApr 2026

Repo stars70

First seenJul 22, 2026

People who liked this also viewed

6 picks

Type