| name | find-the-keystone |
| description | Derive a product's keystone metric — who × does-what × N times × window — by working backwards from engaged users, then stress-test it for causation and gameability before anyone reorganizes around it. Use when a team asks "what's our North Star", when activation work has no target, when an existing keystone was adopted from a blog post rather than cohort evidence, or when GROWTH.md has an Aha but an empty Keystone. |
| metadata | {"pack":"2026-07-growth-circle","forged-by":"claude-fable-5","forged-from":"session-2026-07-02-growth-circle — the Growth Circle protocol (github.com/zakelfassi/growth-circle)","forged-reason":"The growth canon's most famous artifact is one legible activation metric a whole company organized around. Retellings disagree on its constants — which is the tell that the shape is the durable part: derived from engaged users, falsifiable, mortal.","usage-count":"0"} |
Find the Keystone
Work backwards from your most engaged users to one metric that
operationalizes core value — then try to break it before you build on it.
Gate
Requires gate: aha = open in GROWTH.md (non-empty ## Aha, not
NONE FOUND). If closed, stop and run name-the-aha first — a keystone
without a named aha is a number in search of a meaning.
Inputs
GROWTH.md (## Aha), and event/cohort data: analytics export, SQL
access, or server logs. No data → stop, route to instrument-the-truth
for a minimal keystone-path event trail, resume after first light.
Steps
-
Define "engaged" from the aha, not from activity. Pick the behavior
that IS repeated core value delivery (weekly voice note sent, not weekly
app open). Select a broad cross-section of users who sustain it.
-
Path backwards. For each engaged user, reconstruct the first N days:
what did they do, in what order, how fast? Contrast with a matched
cohort that signed up and died. You're hunting the earliest common fork.
-
Compress to the four fields. who × does-what × how-many-times × within-window. If it needs a fifth clause, it's a dashboard, not a
keystone. Prefer legible-to-everyone over statistically maximal.
-
Try to kill it — correlation first. The classic critique: the early
behavior may be the symptom of intent, not the cause of retention.
Design the cheapest causal probe — an experiment that pushes marginal
users over the threshold and watches whether retention follows.
Pre-register it in the experiment ledger; set causal-probe status.
-
Try to kill it — gameability second. Red-team: how would a
well-meaning team inflate this number while destroying value? (Auto-add
connections. Prompt pressure.) Write the top 2 gaming vectors next to
the metric — they are the anti-Goodhart tripwires.
-
Write the spec with a sunset. To ## Keystone: the four fields,
derivation evidence, causal-probe status, gaming vectors, and a review
date (default: 2 quarters). Set gate: keystone = open. A keystone
without planned obsolescence is Goodhart fuel.
Conventions
- One keystone. Contexts may read it differently (
reset-for-context),
but the product has one center of gravity at a time.
- Never adopt a keystone by analogy ("we're like Slack, so 2,000
messages"). Analogy proposes; only your cohorts dispose.
- Legibility is load-bearing: if the whole team can't recite it, it can't
do its actual job, which is organizing people.
Edge Cases
- Too few users to cohort. Say so. Output a provisional keystone tagged
hypothesis, restrict run-the-loop to activation-only experiments, and
set the review to "at 500 engaged users".
- The causal probe fails. A success of the method. Log the busted
candidate in the lore ledger with its epitaph and return to step 2 for
the next common fork.
- Two candidates tie. Pick the one the team can recite and affect.
- Sunset date passed. Re-run steps 1–5 against fresh cohorts before any
other growth work; a stale keystone silently closes its gate.
