shipsignal 0.6.4

AI impact, delivery-health & readiness scanner — is AI actually changing how your team ships?

ShipSignal — the AI impact & delivery-health scanner

Is AI actually changing how your team ships — and can you prove it without overclaiming? One read-only, local command points at any repo and tells you three things: how much AI is actually being used (measured from commit trailers, not guessed), whether delivery health is sound (graded against general engineering norms, never falsely credited to AI), and whether the repo is set up for agents to succeed — with the specific fixes. The only scanner honest enough to withhold a number it can't back up.

Pure Python stdlib, no runtime deps, runs on any repo in seconds. Read-only and local — nothing leaves your box.

Install

uvx shipsignal report <repo>   # zero-install, via uv
# or
pip install shipsignal

Requires Python 3.11+. No runtime dependencies.

Quick start

# Unified audit — all three numbers + fixes, one deliverable (recommended)
shipsignal report ../crown --html crown-audit.html

# Or run a single lens
shipsignal impact ../crown          # impact + delivery health
shipsignal scan ../crown            # readiness only
shipsignal scan . --fail-under 80   # CI gate

See examples/crown-audit.html for a real audit deliverable. From a source checkout, the same commands run as python -m shipsignal.cli ….

Impact lens — three always-on numbers

Every impact scan headlines with three numbers that are always computed (above a tiny sample floor):

Number	What it is
AI Adoption	Co-Authored-By: trailer share + level (None / Emerging / Established / Pervasive). The one direct, AI-specific signal — reported as a lower bound (squash-merges drop trailers).
Delivery Health	A 0–100 snapshot scored against general engineering norms — not AI-attributed. Combines change-size discipline, test discipline, and (for teams) knowledge distribution. Flags surface real risks (low test discipline, concentration risk).
Readiness	The static-state score (the readiness lens, below). Runs by default; --no-readiness to skip.

A fourth, conditional Before/after AI Enablement delta appears only when the data supports it — a clean pre-AI baseline window AND ≥ 20 commits in both windows AND ≥ 50 commits total AND ≥ 6 weeks of history. In the wild that combination is rare (most repos are AI-from-inception, no-AI, or ambient-AI), so it's the bonus, not the headline — competitors fake this score; we don't.

Calibrated across crown (Pervasive · 55/F · 83/B — flags a real test gap), chalk (None · 77/C · 80/B — flags maintainer concentration), vitest (Emerging · 97/A · 97/A — clean). Every delivery number carries an attribution caveat: it measures general delivery health, never proves AI caused a change.

Readiness lens — is the repo set up for agents?

Detector	What
Entry point	Root README present and substantial
Agent instructions	CLAUDE.md / AGENTS.md / .cursor/rules / copilot-instructions (size-scaled)
Module README coverage	Each detected module is documented
Setup & conventions	test command, CI, deps/lockfile, lint/format/type config, .editorconfig, LICENSE, CONTRIBUTING, MCP path-resolution
Broken links	Markdown links resolve (with false-positive guards)
Doc freshness	Module docs haven't drifted behind their code

Module detection is ecosystem-aware (npm / pnpm / Cargo workspaces, then a directory fallback), respects .gitignore, and excludes vendored/build dirs. Six scored categories sum to 100 (entry 20, agent 15, coverage 20, setup 20, integrity 13, freshness 12). Categories can be n/a or indeterminate; the score renormalizes over what was actually scored, so a small well-documented library isn't punished.

Output

A canonical JSON (readiness.json / impact.json / combined report JSON — findings or metrics, never file or diff contents), CLI text, optional Markdown / HTML reports, and a readiness: N/100 badge SVG. Exit non-zero with --fail-under N for CI gates.

Readiness fixes are ranked by payoff — each carries an ≈+N pts estimate (computed by re-scoring as if it were resolved, so the number always matches the model), an effort tag (quick / moderate), and a file:line location where one applies. Desync flags that don't move the score are labelled informational rather than padded with a number.

Add --snapshot to any command to persist a small (<8KB) JSON record under .shipsignal/snapshots/YYYY-MM-DD-<sha>.json. Gitignored by default; remove the .shipsignal/ line from .gitignore to commit your audit history. Then:

shipsignal trend . --html trend.html   # readiness/breadth/AI deltas + SVG line chart
shipsignal trend . --limit 8 --since 2026-01-01

The trend view reads only existing snapshots — no re-scan, fully offline. Honest about single-snapshot ("scan again to start a trend"), schema-version mismatches (skips the fixes diff rather than inventing false resolutions), and large window jumps (warns when one snapshot covers >30% more commits than its predecessor).

Project layout

• shipsignal/ — the package (module map inside)
• tests/ — stdlib unittest suite
• examples/ — a committed sample audit
• Working with an agent? See CLAUDE.md / AGENTS.md.

License

MIT (see pyproject.toml).