PLG Motion Designer — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited PLG Motion Designer (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
A product-led motion lives or dies on one number most teams never define: the percentage of signups who reach value before they churn in silence. You design the path from signup → aha moment → activated and instrument the gates between each step, so the product sells itself instead of waiting on a human. The most common failure is a vanity definition of activation — "created an account" — that correlates with nothing. This skill fixes the definition first, then builds the motion around it.
Reach for it once positioning and the offer are set ([[positioning-statement]], [[messaging-hierarchy]]) and the self-serve product can be signed up for without a sales call. It owns the in-product journey; the acquisition page is [[landing-page-copy]], the paywall economics are [[saas-pricing]] / [[pricing-strategy]], and the broad launch plan is [[go-to-market-planner]] and [[launch-plan-sequencer]]. Run it before [[launch-day-runbook]] so the motion is live and measured when traffic hits.
The aha moment is the first in-product action after which a user's odds of retaining jump sharply. Find it, do not guess it:
action is the one with the widest retention gap that a new user can plausibly reach in the first session.
a second person." Slack's was ~2,000 messages sent by a team; the shape (frequency x breadth x time) matters more than the figure.
A crisp aha definition reads: "<count> of <core action> across <breadth> within <time window>." If you cannot fill that template, you do not yet know your aha moment — and every downstream nudge is guesswork.
Activation is reaching the aha moment. Work backward and name the 3-5 ordered milestones a user must clear to get there. Each milestone is a discrete, logged event — the rungs of the ladder:
M0 Signup account created
M1 Setup the one config without which value is impossible
M2 First action core action done once (the "empty state" defeated)
M3 Aha moment the Step-1 definition is met ← ACTIVATED
M4 Habit / PQL repeated value → ready to convert (hand to saas-pricing)Rules that keep the ladder honest:
leaks users.
meets a new user. Design it deliberately (templates, sample data, a guided first action).
Every transition between milestones gets two numbers, and you do not move on until they are instrumented:
speed metric; long TTV silently kills self-serve.
number the whole motion optimizes.
The biggest single drop in step conversion is your bottleneck — fix it before touching anything else. Optimizing a 90% step while a 30% step bleeds upstream is the classic waste.
Nudges are interventions to lift a specific step's conversion — never decoration. For each leaking step, pick the lightest nudge that moves it:
| Leak | Nudge | Note |
|---|---|---|
| Stalls at M1 setup | Inline checklist / progress bar | Show the path; reduce the unknown |
| Empty state at M2 | Templates, sample data, guided first action | Defeat the blank screen |
| Drops before aha | Contextual tooltip at the moment of need | In-product beats email here |
| Goes dark after signup | Lifecycle email tied to the missed milestone | Behavior-triggered, not drip |
| Hits aha, no repeat | Habit loop: trigger → action → reward | Build toward M4 / PQL |
Discipline: one nudge per leak, instrument its effect, keep it only if step conversion rises. Over-nudging (badges, popups, tours everywhere) trains users to dismiss everything and buries the one that matters.
Drop your per-milestone counts in; it returns step conversion, where the funnel leaks worst, TTV, and the overall activation rate. Self-contained Python.
# activation_funnel.py — python3 activation_funnel.py
from statistics import median
# Ordered milestones: signup is M0, aha is the activation gate.
MILESTONES = ["M0 signup", "M1 setup", "M2 first action", "M3 aha (ACTIVATED)"]
COUNTS = [10000, 6200, 3900, 2600] # users reaching each
AHA_INDEX = 3 # index of the aha milestone
TTV_HOURS = [0.5, 6.0, 22.0, 34.0, 48.0, 70.0] # signup→aha per activated user
def report():
print(f"{'transition':<34}{'reached':>9}{'step conv':>11}")
worst = ("", 1.0)
for i in range(1, len(MILESTONES)):
conv = COUNTS[i] / COUNTS[i - 1]
arrow = f"{MILESTONES[i-1].split()[0]} -> {MILESTONES[i].split()[0]}"
print(f"{arrow:<34}{COUNTS[i]:>9}{conv:>10.0%}")
if conv < worst[1]:
worst = (arrow, conv)
activation = COUNTS[AHA_INDEX] / COUNTS[0]
print("-" * 54)
print(f"activation rate (signup -> aha){'':<3}{activation:>20.1%}")
print(f"median time-to-value (hours){'':<6}{median(TTV_HOURS):>20.1f}")
print(f"biggest leak{'':<22}{worst[0]:>16} ({worst[1]:.0%})")
report()Worked output for the sample numbers above:
transition reached step conv
M0 -> M1 6200 62%
M1 -> M2 3900 63%
M2 -> M3 2600 67%
------------------------------------------------------
activation rate (signup -> aha) 26.0%
median time-to-value (hours) 28.0
biggest leak M0 -> M1 (62%)Read: 26% activation with the worst leak at the very first setup step (M0→M1) and a 28-hour TTV. The move is a Step-4 nudge on setup (inline checklist) and a hard look at why first value takes a day — not a new feature, not more signups.
Aha moment: <count> of <core action> across <breadth> within <time window>
Activation = reaching the aha moment within <TTV window>
Milestones (ordered, each a logged event):
M0 Signup → event: ______
M1 Setup → event: ______ gate: step conv ____% nudge: ______
M2 First action → event: ______ gate: step conv ____% nudge: ______
M3 Aha/ACTIVATED → event: ______ gate: step conv ____%
M4 Habit / PQL → event: ______ (hand to saas-pricing for conversion)
North-star gate: activation rate = ____% (target: ____%)
Speed gate: median TTV = ____ (target: ____)
Current bottleneck: step ______ at ____% → intervention: ______<count> of <action> across <breadth> within <window>template and is backed by a retention gap, not an opinion.
value — no decorative steps.
activation rate and a median TTV. If a step is uninstrumented, the motion is not done.
before/after on that step's conversion.
a healthier step.
this skill stops at activation.
correlate with nothing; activation is reaching value.
leak first.
kept only if it moves the number.
[[launch-plan-sequencer]] and [[launch-day-runbook]].
handling — a PLG motion replaces the rep; for the sales-led path use [[sales-enablement-kit]] instead.
[[landing-page-copy]] and [[saas-pricing]] / [[pricing-strategy]].
activation target sends the whole motion chasing the wrong behavior.
missing nudge, found in Step 3.
A one-page activation spec: the aha-moment definition with its retention backing, the ordered milestone ladder, the step-conversion / TTV / activation-rate gates, one nudge per leaking step with its measured lift, the named current bottleneck and the intervention for it, and a clean handoff at M4/PQL to [[saas-pricing]] for conversion. Fits inside the GTM launch built by [[go-to-market-planner]] and [[launch-plan-sequencer]].
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.