confidence

name	confidence
description	Analyze test suite quality using the Confidence CLI tool. Use when asked to assess test quality, review test health, generate a test confidence report, or when phrases like "run confidence", "test quality report", "how are the tests", "scan tests", "test health" are used. Also use proactively after completing major test-related work to validate quality.
allowed-tools	Bash(confidence *)

Confidence — Test Suite Quality Analysis

Analyze test suite quality through static analysis and git history using the confidence CLI.

Install

If confidence is not on PATH, build it from the repo:

go build -o confidence ./cmd/confidence

Or install globally:

go install github.com/timusus/test-confidence/cmd/confidence@latest

Commands

Default to the HTML report — it's the most useful output for understanding a codebase. Only use terminal or JSON for quick checks or CI.

HTML report (preferred — visual, self-contained)

confidence scan <path> --html > /tmp/confidence-report.html && open /tmp/confidence-report.html

HTML with historical trends (recommended for first run)

confidence scan <path> --html --trend-months 12 > /tmp/confidence-report.html && open /tmp/confidence-report.html

Terminal report (quick check)

confidence scan <path>

JSON output (for CI or programmatic use)

confidence scan <path> --json

With coverage data (optional, enriches analysis)

# Android — Kover/JaCoCo XML
confidence scan <path> --coverage build/reports/kover/report.xml

# iOS — xccov JSON (generate with: xcrun xccov view --report --json <bundle>.xcresult > coverage.json)
confidence scan <path> --platform ios --coverage coverage.json

iOS projects (need platform flag)

confidence scan <path> --platform ios

Verbose per-file detail

confidence scan <path> --verbose

Compare to baseline

confidence scan <path> --since 3months
confidence scan <path> --compare-ref v1.0
confidence scan <path> --compare old.json

When to use

• User asks about test quality, test health, or test confidence for a project
• After completing a major feature or test refactor, to check impact
• When reviewing a project's test strategy or test suite
• When comparing test quality across projects or over time
• When asked to identify the highest-risk untested code

Interpreting results

The tool presents signals, not opinions. Focus on:

• Behavioral % — proportion of tests checking outcomes vs implementation
• Where To Start — prioritized recommendations ranked by impact
• Evidence — observed maintenance cost from git history (how often refactors break tests)
• Risk Hotspots — files with high churn AND fragile tests
• Coverage Gaps — untested surfaces and business logic
• Patterns — novel cross-file signals (mock blast radius, test freshness, production complexity shadow)

Notes

• The tool reads source files and git history only — never compiles or executes tests
• All analysis is offline and deterministic
• Coverage data is optional but enriches every signal when available
• Works on Android/Kotlin and iOS/Swift codebases