testland/exec-quality-narrative
Build-an-X workflow that turns already-computed quality data - weekly digests, KPI roll-ups, DORA delivery metrics, escape-defect trends, OKR grading - into an executive or QBR narrative structured by the Minto Pyramid Principle: governing answer first, MECE-grouped support beneath it, SCQA opening (Barbara Minto, The Pyramid Principle, ISBN 978-0273710516). Distinct from single-team digest computation (which computes the RAG digest from raw CI and tracker signals; this skill consumes such digests and writes the upward story), from portfolio-review aggregation (which aggregates teams into a portfolio review; this skill is the communication layer either output feeds), and from QA OKR authoring (forward-looking commitments; this skill narrates what happened and what it means). Use before a QBR, board update, or exec review when the data exists but the story does not.
74
93%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Passed
No findings from the security scan
qbr-narrative-example.mdreferences/
Worked example - top of a QBR quality narrative
The governing layer of a real QBR narrative built with exec-quality-narrative: the Answer first, an SCQA opening, four MECE support groups, and the ask as its own group. Every number carries the artifact it came from. Everything below this layer in the real document is appendix - per-team tables, the digests themselves, methodology - that executives can descend into but do not need to reach the answer.
# Quality QBR - 2026-Q2
**Answer first:** Quality held through a 2x delivery acceleration on three of four
teams; checkout regressed (3 P1 escapes vs 1 in Q1) and recovers only if this
group approves the test-data investment below.
**Situation.** Four product teams, weekly releases, quality reporting per team
digest. **Complication.** Deployment frequency doubled (38 -> 81 deploys/quarter,
CI deploy log) at flat QA headcount. **Question.** Did quality hold?
## 1. Outcomes - held, except checkout
P1 escapes: 4 in Q2 vs 5 in Q1 (tracker, severity=P1, found_in=production), but
3 of 4 concentrated in checkout (vs 1 in Q1). Checkout's escapes trace to
unseeded test environments in 9 of 11 retro findings (Q2 escape retros).
## 2. Delivery - faster, stable
Deployment frequency 38 -> 81; change fail rate 4.9% -> 5.2% (deploy log over
incident tags; definitions per dora.dev). Delivery acceleration did not buy
instability - the checkout regression is a test-gap story, not a velocity story.
## 3. Commitments - 3 of 4 OKRs landed
KR grades from the Q2 OKR set: regression cycle time 1.0, flake budget 0.8,
escape-rate KR missed on checkout only (qa-okr-author grading sheet).
## 4. The decision
One ask: 6 engineer-weeks for seeded checkout test data. Expected effect:
removes the cause named in 9 of 11 escape retros. Alternative considered and
rejected: +1 headcount (slower, does not fix the environment gap).