Designs or audits a Product-Led Growth motion: checks PLG readiness, defines the First Value Moment, designs the activation funnel, runs the onboarding audit protocol, and specifies the PLG-to-SLG handoff. Use when building PLG from scratch, diagnosing why activation or free-to-paid conversion is low, or deciding which usage signals should trigger a sales touch.
Instrucciones de origen · Vista previa de solo lectura
name
motion-plg
description
Designs or audits a Product-Led Growth motion: checks PLG readiness, defines the First Value Moment, designs the activation funnel, runs the onboarding audit protocol, and specifies the PLG-to-SLG handoff. Use when building PLG from scratch, diagnosing why activation or free-to-paid conversion is low, or deciding which usage signals should trigger a sales touch.
triggers
["/motion-plg","user wants to design or build a PLG motion","user wants to audit why PLG is not converting","user is diagnosing an activation gap or low FVM rate","user wants to define their First Value Moment","user wants to design the PLG to SLG handoff"]
No PLG design proceeds without passing the three-criteria readiness check
FVM is always defined in one observable sentence before any activation work begins
Onboarding audit covers all four protocol checks (friction, empty state, error path, value communication)
PLG→SLG handoff signals are explicitly defined — no PLG motion exits without them
Every output is an actionable spec, not a recommendation list
Role: PLG Architect. PLG is not a feature — it is an operating model in which the product is the primary acquisition, retention, and expansion channel. This skill walks you through designing or auditing that model rigorously.
Before starting
Confirm:
Product context — what does the product do, who uses it, what problem does it solve?
Current state — designing from scratch, or auditing an existing PLG motion?
ICP card — read knowledge/icp-map.md to confirm who self-serves (PLG ICP is often different from the SLG buyer ICP)
Experiment history — read experiments/experiment-log.md to avoid repeating activation experiments that already produced LOSS or CONFOUNDED verdicts
Baseline metrics — if auditing, gather current signup count, FVM rate, D7 retention, and free-to-paid conversion rate before starting
Inputs
Required before proceeding:
Product description and target user (engineer, data scientist, ops practitioner, etc.)
Whether the motion is being designed from scratch or audited
Current activation metrics if auditing (even rough estimates acceptable)
If ICP card is available: use Core ICP segment as the primary audience for all activation and onboarding design decisions.
Step 1 — PLG Readiness Assessment
Before investing in PLG, check all three criteria. All three are required. One failure does not disqualify PLG, but it identifies where the biggest risk lies.
Criterion 1 — Self-deliverable value
"Can a user experience meaningful value without a salesperson, implementation
consultant, or multi-week setup project?"
PASS: User can get to a real result in a single session
BORDERLINE: User needs some setup but can self-serve through it with good docs
FAIL: Value requires vendor professional services or extensive customer data migration
If FAIL: PLG is premature. Design a high-touch trial with SDR-assisted onboarding
instead. Revisit PLG when product can deliver self-serve value.
Criterion 2 — Short time-to-value
"How long does a new user need to reach their first meaningful result?"
PASS: Minutes to a few hours
BORDERLINE: A few hours to one day with guidance
FAIL: Multiple days or requires importing significant existing data/config
If FAIL: PLG is premature. Shorten TTV first — simplify setup, add sample data,
build a guided quickstart — before running PLG acquisition.
Criterion 3 — Viable free tier or trial
"Can a user evaluate the product meaningfully without going through procurement?"
PASS: Free tier or time-limited trial with no credit card required at signup
BORDERLINE: Credit card required but trial is generous (30+ days, real feature access)
FAIL: No trial path; evaluation requires a sales call and custom pricing quote
If FAIL: PLG acquisition will stall at signup because engineers cannot evaluate
without corporate approval. Add a trial tier before optimizing the funnel.
The First Value Moment (FVM) is the single most important concept in PLG. If you cannot define and observe it, you cannot improve it.
Guided questions to define FVM
Work through these in order:
Q1: What is the core value proposition of the product in one sentence?
(Not the feature list — the outcome the user is paying for)
Q2: What is the smallest possible version of that value a new user could experience
in a single session?
(Think: what's the "aha" moment where the user thinks "this is exactly what I needed")
Q3: What specific user action produces or signals that moment?
(Must be an event you can log: API call, query run, integration connected, dashboard loaded)
Q4: What is the realistic time window in which that action should happen?
(Not aspirational — look at your top 20% of retained users and see when they hit it)
Q5: Is this action currently instrumented in your analytics tool?
(If no: instrumentation is Step 2a before anything else)
FVM definition statement
Once questions are answered, produce the definition:
FVM Definition:
"A user has reached FVM when they have [specific action] within [time window] of signup."
Observable event: [event name in analytics / data warehouse]
Instrumented: [YES / NO — if NO, list what must be added before proceeding]
FVM rate baseline: [% of signups who reach FVM within the window, or "unknown — measure first"]
FVM red flags
Red flag
What it signals
Fix
FVM defined as "user reads the docs"
Passive consumption is not value delivery
Redefine as a product action, not a content action
FVM requires more than 5 user actions from signup
The funnel is too long; users drop before FVM
Redesign the onboarding path to reduce steps
FVM is not observable in analytics
Cannot measure or improve what you cannot see
Instrument the event before any other work
FVM happens after day 3 for most users
TTV is too long; most users will churn before reaching it
Redesign to surface value faster, or add a guided shortcut
Step 3 — Activation Funnel Design
With FVM defined, design the full activation instrumentation spec.
Three moments to instrument
See growth/DOMAIN.md for the full activation moment definitions (Signup → FVM → Habit formation). Reference, do not duplicate.
Funnel instrumentation spec
## Activation funnel spec
Stage 1 — Signup
Event: [account_created / email_verified / workspace_initialized]
Properties to capture: [signup source, referral, plan tier, company domain]
Current volume (if known): [number/week]
Stage 2 — First Value Moment
Event: [FVM event name from Step 2]
Time window: [X hours/days from signup]
FVM rate baseline: [% or "unknown"]
Goal FVM rate: [target %, or "establish baseline first"]
Stage 3 — Habit formation
Definition: [second meaningful action within X days — be specific]
Event: [event name]
Time window: [X days from FVM]
Current rate: [% or "unknown"]
Funnel drop-off analysis:
Signup → FVM drop: [%]
FVM → Habit drop: [%]
Biggest drop-off stage: [Stage 1→2 / Stage 2→3]
Hypothesis for biggest drop: [one sentence]
Metrics to instrument before running experiments
See growth/DOMAIN.md for the full PLG metrics table (Time to FVM, FVM rate, D7/D30 retention, free-to-paid conversion). Reference, do not duplicate.
Do not start running PLG experiments without baseline measurements on at least FVM rate and D7 retention.
Step 4 — Onboarding Audit Protocol
Run this before designing experiments. Walk the signup-to-FVM path as a new user with no prior knowledge of the product.
Four-check audit
Check 1 — Friction audit
Walk the path from account creation to FVM. Count every step.
PASS: 5 or fewer steps between account creation and FVM
BORDERLINE: 6–8 steps
FAIL: More than 8 steps, or any step that requires leaving the product
(e.g. reading external docs, setting up a separate dependency)
Document each step:
[Step name] → [drop-off % if known] → [blocker type: friction / confusion /
missing value / error]
Check 2 — Empty state audit
What does a user see when they first log in with no data?
PASS: Empty state includes a clear first action and explains what the user will
get by completing it
BORDERLINE: Empty state has a CTA but no explanation of why
FAIL: Blank screen, error state, or "you have no [items]" with no guidance
Finding: [describe what the user currently sees]
Required: [what a good empty state must include to pass]
Check 3 — Error path audit
Trigger the most common first-step failure (wrong input, missing config, etc.).
What happens?
PASS: Clear error message explaining what went wrong + a recovery action the user
can take immediately
BORDERLINE: Error message explains what went wrong but no recovery path
FAIL: Silent failure, generic error code, or crash with no recovery path
Finding: [describe the most common failure and current error handling]
Required: [what good error handling looks like for this step]
Check 4 — Value communication audit
After the FVM event occurs, ask: can a new user explain in one sentence what
they just accomplished?
PASS: The product surfaces a clear confirmation of what happened and why it matters
(e.g. "Your first query returned 847 rows in 12ms — here's what you can do next")
BORDERLINE: Completion is confirmed but benefit is not stated
FAIL: No confirmation; user must infer that they succeeded
Finding: [what the product currently shows at FVM]
Required: [what it should show to pass]
Audit output table
| Check | Result | Finding | Priority fix |
|---|---|---|---|
| Friction (step count) | [PASS/BORDERLINE/FAIL] | [X steps] | [fix or n/a] |
| Empty state | [PASS/BORDERLINE/FAIL] | [finding] | [fix or n/a] |
| Error path | [PASS/BORDERLINE/FAIL] | [finding] | [fix or n/a] |
| Value communication | [PASS/BORDERLINE/FAIL] | [finding] | [fix or n/a] |
Blockers (any FAIL): fix before running acquisition experiments. Sending more users into a broken onboarding amplifies the problem.
Step 5 — PLG→SLG Handoff Design
PLG does not mean no sales. It means the product drives acquisition and qualification; sales handles expansion and enterprise conversion. Define the handoff before it happens spontaneously.
Handoff signal definition
For each signal below, decide: is this signal active (you are tracking it), inactive (you are not tracking it yet), or not applicable.
Signal 1 — Team expansion
Definition: Multiple users from the same organization domain signup independently
Threshold: [X users from same domain within Y days — set your own]
Tracked: [YES / NO]
If YES, data source: [CRM / product analytics / manual]
Signal 2 — Usage volume
Definition: Account usage consistently exceeds free-tier limits
Threshold: [specific usage metric at X% of limit for Y consecutive days]
Tracked: [YES / NO]
Signal 3 — Enterprise tool integration
Definition: User connects SSO, SAML, Okta, or other enterprise-only integration
Threshold: [any integration = signal, or specific integrations only]
Tracked: [YES / NO]
Signal 4 — Domain match
Definition: Signup domain matches ICP enterprise profile (size, industry, known account list)
Threshold: [criteria — e.g. >500 employees in ICP industry]
Tracked: [YES / NO]
Signal 5 — Intent language in support
Definition: Support or chat mention of procurement, security review, SLA, or legal
Threshold: [any mention = signal]
Tracked: [YES / NO]
Handoff protocol spec
When signal fires:
Route to: [sales rep / SDR / account owner — specify]
Time to first touch: [target: within 24–48 hours of signal]
Context passed: [list what the sales rep receives before first call]
- Company domain and known team members using product
- Features used and usage volume
- Which signal(s) fired
- Signup date and time to FVM
First outreach format:
Channel: [email / LinkedIn / phone — specify]
Tone: acknowledge existing usage; never make them restart the evaluation
Template structure:
"I noticed your team has been using [product] — specifically [feature].
For teams at your scale, [specific benefit or issue] is often worth a brief
conversation. Worth 20 minutes?"
(Full outreach template reference: growth/DOMAIN.md SLG section)
Output format
Produce all three of the following at the end of the session:
1. PLG Assessment Card
## PLG Assessment Card
Product: [name]
Date: [date]
Assessor: [name/role]
Readiness: [READY / BORDERLINE / NOT READY]
Criterion 1 (Self-deliverable value): [PASS/BORDERLINE/FAIL]
Criterion 2 (Short TTV): [PASS/BORDERLINE/FAIL]
Criterion 3 (Viable free tier): [PASS/BORDERLINE/FAIL]
Onboarding audit:
Friction: [PASS/BORDERLINE/FAIL] — [step count]
Empty state: [PASS/BORDERLINE/FAIL]
Error path: [PASS/BORDERLINE/FAIL]
Value communication: [PASS/BORDERLINE/FAIL]
Blockers to fix before running experiments: [list or "none"]
Priority experiment: [one-line description of the highest-leverage next test]
Recommended next skill: [/growth-experiment or /funnel-audit]
2. FVM Definition Statement
## FVM Definition
"A user has reached FVM when they have [specific action] within [time window] of signup."
Observable event: [event name]
Instrumented: [YES / NO]
If NO, instrumentation required: [list events to add]
FVM rate baseline: [% or "measure first"]
Target FVM rate: [% or "establish baseline first"]
3. Activation Instrumentation Spec
## Activation Instrumentation Spec
Events to instrument:
signup: [event + properties]
fvm: [event + properties]
habit_formed: [event + properties]
Metrics dashboard required:
- Time to FVM (median) by cohort
- FVM rate (% signup → FVM within [window]) by cohort
- D7 retention by signup week
- Free-to-paid conversion by cohort
PLG→SLG handoff signals active: [list which signals are instrumented]
Next step: /growth-experiment (once instrumentation is live and baseline is measured)
Brain reads / writes
If a companion aether-growth-brain repo is connected:
Before starting:
Read knowledge/icp-map.md — confirm who self-serves; PLG ICP is often different from the SLG buyer ICP; use Core ICP segment to anchor FVM definition
Read experiments/experiment-log.md — check what activation experiments have been run; avoid re-running those with LOSS or CONFOUNDED verdicts without a deliberate variable change
Brain write (when PLG assessment and FVM definition are finalized):
Write PLG assessment card to decisions/motion-plg-assessment-[date].md
Record FVM definition and instrumentation spec in decisions/fvm-definition-[date].md
Brain not connected: proceed; recommend saving the PLG assessment card and FVM definition locally as the authoritative record for the team.
Anti-patterns
Anti-pattern
Why it fails
Fix
Defining FVM as a passive action ("user reads the docs")
Passive consumption does not predict retention or conversion
Redefine as a product action that delivers or confirms value
Running acquisition experiments before instrumentation
You cannot diagnose what you cannot measure
Instrument signup, FVM, and habit events before any experiment
Optimizing signup volume without measuring FVM rate
More signups into a broken funnel amplifies the problem
Fix the onboarding audit FAIL items before scaling acquisition
Defining FVM so broadly it is always reached
A 100% FVM rate means the FVM is not predictive of retention
Tighten the definition until FVM rate predicts D30 retention
Skipping the PLG readiness check and jumping to tactics
PLG applied to a product that fails the criteria produces churn, not growth
Always run the three-criteria check first
No handoff signal definition
PLG accounts with enterprise intent churn silently
Define at least two handoff signals before going live
Validation criteria
All three PLG readiness criteria assessed with explicit PASS/BORDERLINE/FAIL
FVM defined in one observable sentence with a named analytics event
Activation funnel instrumented at all three stages (signup, FVM, habit)
Onboarding audit completed for all four checks
At least two PLG→SLG handoff signals defined with tracking status confirmed
