ShipSignal · AI impact audit

crown

5 Feb – 22 Jun 202619.7 weeks724 dev commitsexcluded 3 merges + 0 maintenance-bot
Generated 4 Jul 2026, 00:51 UTC · shipsignal v0.8.0
AI Adoption
Pervasive97%
Claude 705 · lower bound
Delivery Health
55/100 F
general eng norms
Readiness
85/100 B
static repo state
How to read this report
AI Adoption
The share of commits an AI tool co-authored — the one directly measured sign AI is actually being used here, not a survey.
Delivery Health
How sound the team's shipping habits are by general engineering norms — deliberately NOT credited to AI. High adoption means little if delivery health is poor.
Outcomes
How often changes get reverted or fixed, and how fast — outcome signals to complement the habit-based numbers above. Always context, never part of any score.
Readiness
Whether the repo is set up so an AI agent (or a new human) can navigate it and trust what it reads — the conditions that decide whether AI adoption actually pays off.
Before/after AI Enablement
When a clean pre-AI baseline exists, how delivery metrics shifted after adoption — shown as context, never proof AI caused the change.
Trajectory
How AI adoption and delivery health moved over the repo's history — two parallel timelines, correlation only, never proof one caused the other.
AI adoption 97.4% (705/724 commits — lower bound)
Adoption date: 2026-02-02
Breadth: n/a — only 1 active contributor(s) — breadth needs ≥3 to be meaningful and to avoid de-anonymization on small teams
Rate / week: ███▇█▇████████▇█████ last 20w · 0–100%
The share of commits an AI tool co-authored — the one directly measured sign AI is actually being used here, not a survey.

Delivery Health

How sound the team's shipping habits are by general engineering norms — deliberately NOT credited to AI. High adoption means little if delivery health is poor.

change_size_discipline
100%
test_discipline
10% low test discipline
knowledge_distribution
n/a (solo author)

Context (not scored): 36.72 commits/wk · 1 contributors.

Where to focus

General engineering norms — not AI-attributed; where delivery health has the most headroom, not a defect list.

Outcomes context, never scored

Revert pairs: 4 · median time-to-correction 0d

Change-failure proxy: 26% (189 commits)

Before/after AI Enablement (bonus)

n/a — AI adoption is at or before repo inception — no pre-AI window to compare
A before/after needs a clean pre-AI baseline; the three numbers above stand on their own.

Trajectory over time — parallel timelines, NOT a causal link

0501002026-02-052026-06-15adoption %delivery health
Attribution caveat. Delivery pillars (flow, quality, risk) measure GENERAL delivery health — only AI-adoption and readiness are AI-specific. A delivery change may come from hiring, a finished migration, or a calmer quarter. The score asks whether the conditions under which AI pays off are improving — it does NOT prove AI caused any change.

Readiness — 85/100 · B

entry_point
20/20
agent_instructions
15/15
module_coverage
13.3/20
setup_tooling
11.8/20
doc_integrity
13/13
doc_freshness
12/12

Top Readiness fixes (4 issues, grouped by area, highest payoff first)

Module docs

Setup

Freshness

≈ marks each fix's marginal payoff (resolving it alone). Renormalization means totals aren't additive — fixing several won't equal the sum.