dartrics 0.6.4

Citation-anchored Dart code-quality metrics (CK, Halstead, McCabe, Martin, Cognitive) plus Periphery-style unused public-API detection, shaped for AI refactor loops.

dartrics

Dart code-quality metrics and unused public-API detection, designed as the AI-loop counterpart of dart analyze.

What it does

dartrics computes a battery of code-quality metrics — McCabe, Cognitive Complexity (Sonar), Chidamber & Kemerer, Martin, Halstead — on top of package:analyzer, alongside an analyzer-element BFS for unreachable public API à la Periphery. Every report mode is shaped to be consumed: --reporter ai ships a token-efficient YAML-ish bundle, sorted by actionability, with each metric's rationale, refactor hints, and primary-source citation embedded inline.

The wager: the academic catalogue is reusable now in a way it wasn't before — not because the metrics changed, but because the consumer did. Humans cannot compute LCOM4 by eye; the number alone doesn't tell you what to change; even when it does, the refactor isn't free. An AI loop absorbs all three costs. The CLI computes in milliseconds, auto-explain ships the rationale alongside every violation, the agent does the edit, and dartrics regression confirms the metric actually settled.

Each metric is treated as a lens: one specific dimension of "hard to read", anchored to its original paper. Lenses are independent — a function can be clean by cyclomatic complexity and tangled by cognitive complexity. dartrics does not gate; it surfaces what each lens reads, and leaves the accept / refactor / dismiss decision in the loop.

Designed for the AI loop

• Auto-explain by default — rationale, refactor hints, and primary-source citation ride alongside every fired metric, so an agent reads the why without a second tool call.
• Stable IDs across runs — every violation carries a 16-hex-char id (sha256("<file>|<scope>|<metric>")), reappearing across runs so AI loops can detect "my fix didn't take". Surfaces as partialFingerprints.dartrics/v1 in SARIF.
• Docs in the binary — dartrics manual and dartrics ai-loop print the operator's reference and the four-station walkthrough; dart pub global activate dartrics is enough, no separate doc download.

Install

dart pub global activate dartrics

Quick start

# Token-efficient YAML-ish report optimised for LLM consumption.
dartrics analyze lib/ --reporter ai | claude -p "Refactor the threshold violations"

# After the agent applies a fix: confirm metrics actually improved.
dartrics regression --before HEAD~1 --after HEAD --reporter ai

# Read the operator's manual or the AI-loop walkthrough in the terminal.
dartrics manual
dartrics ai-loop

Subcommands

Command	Purpose
analyze	Every metric + the public-API unused detector.
unused	Public-API reachability only (fast path).
report <input.json>	Re-emit a previously saved JSON report in another format.
rules	Catalogue every metric with rationale, refactor hints, and references.
regression	Compare metrics between two git states; classify each delta as improved / regressed / unchanged / added / removed.
manual	Print the operator's manual (mirrors doc/manual.md).
ai-loop	Print the AI-loop walkthrough (mirrors doc/ai-loop.md).
doctor	Validate the dartrics: block in analysis_options.yaml.

Each subcommand only exposes the flags it actually consumes — dartrics <command> --help lists them. Full flag reference, dismissal mechanics, coverage / snapshot / regression details, and the refactor / dismiss decision tree all live in dartrics manual and dartrics ai-loop.

Provided metrics

dartrics ships a curated set anchored to published sources. For the audit trail — selection principles, deviations from the cited definitions, off-by-default rationale — see doc/calibration.md.

Each metric exposes rationale, refactorHints, references (the primary source — McCabe 1976, Hitz & Montazeri 1995, Martin 1994, …), and polarity (down / neutral). All four surface through dartrics rules and the AI / md / SARIF reporters so an agent can verify a metric against its original paper rather than paraphrasing from training data.

Lenses marked off ship disabled by default; opt in via dartrics: { metrics: { <id>: { enabled: true } } }. A — in Default warning means the lens emits a measurement but fires no warning until you set a threshold via dartrics: { metrics: { <id>: { warning: <n> } } }.

Function / method level

Metric	Reference	Default warning
Cyclomatic Complexity	McCabe 1976	10
Cognitive Complexity	SonarSource 2017 (rev.)	15
Number Of Parameters	Fowler 1999	4
Source Lines Of Code	Boehm 1981	—
Method Length off	Beck 1996	opt-in
Halstead Volume off	Halstead 1977	opt-in

Class level

Metric	Reference	Default warning
Number Of Methods	Lorenz & Kidd 1994; CK 1994	—
Weighted Methods Per Class	CK 1994	—
LCOM4	Hitz & Montazeri 1995	—
Coupling Between Objects	CK 1994	—
Response For a Class	CK 1994	—
Class Length	Beck 1996; Fowler 1999; Lippert & Roock 2006	—

Library / file level (Martin 1994)

Polarity neutral; values rank change-impact rather than fire as Pain/Uselessness verdicts (see doc/calibration.md).

Metric	Notes	Default warning
Efferent Coupling (Ce)	distinct project-internal + package: dependencies (excludes dart:*)	—
Afferent Coupling (Ca)	incoming internal-import edges	—
Instability (I)	Ce / (Ca + Ce); informational, surfaces drift in change-impact ranking	—

Configuration

Minimal analysis_options.yaml:

# yaml-language-server: $schema=https://raw.githubusercontent.com/koji-1009/dartrics/main/schemas/dartrics-config.schema.json

dartrics:
  metrics:
    cyclomatic-complexity:
      warning: 10
      error: 20
    cognitive-complexity:
      warning: 15
  exclude:
    - "lib/generated/**"

The dartrics: section is read by both the CLI and the analyzer plugin. The # yaml-language-server directive turns on autocomplete + typo detection in editors with yaml-language-server integration. Every key (per-metric thresholds, dismissals, snapshot mode, unused-detector filters) is documented in schemas/dartrics-config.schema.json and explained in dartrics manual.

Analyzer plugin

dartrics ships its own analyzer plugin so the three lightweight function-level rules surface inline in dart analyze and the IDE.

# analysis_options.yaml in your project
plugins:
  dartrics:

After saving, restart the analysis server. Three rules ship by default:

Rule	Default threshold
dartrics_cyclomatic_complexity	10
dartrics_cognitive_complexity	15
dartrics_number_of_parameters	4

Heavier metrics (LCOM4, CBO, RFC, library coupling) and the public-API unused detector intentionally stay CLI-only because they need a project-wide index that an analysis-server plugin can't maintain efficiently per file.

Documentation

• dartrics manual — operator's reference: every flag, dismissal mechanics, refactor / dismiss decision tree, Flutter-aware and test-aware modes, exit codes.
• dartrics ai-loop — four-station walkthrough of one full refactor iteration with sample prompts.
• doc/calibration.md — citation audit, selection principles, counting-rule deviations.
• schemas/ — JSON Schema files: dartrics-config.schema.json for analysis_options.yaml's dartrics: block, dartrics-report.schema.json for the JSON reporter output, dartrics-dismissals.schema.json for the dismissals sidecar. All draft-2020-12.

Output formats

--reporter accepts console (default), json (stable schema, see schemas/dartrics-report.schema.json), md (PR comments / issue bodies), ai (token-efficient YAML-ish bundle starting with # dartrics ai-report v1), and sarif (SARIF 2.1.0 for GitHub Code Scanning / GitLab).

Embedding

lib/dartrics.dart is intentionally tight — it exposes only the function-level metric calculators so a custom CI bot or editor extension can compute one metric on a parsed CompilationUnit without spinning up the full engine.

What you get	Names
Function-level metric calculators	CyclomaticComplexity, CognitiveComplexity, NumberOfParameters, MethodLength, SourceLinesOfCode, HalsteadCounts / HalsteadVolume
Calculator interface	FunctionMetric, FunctionMetricInput, MetricPolarity
Version string	dartricsVersion

Anything not in this table is CLI-only and unsupported as a Dart import; reach for dartrics analyze --reporter json instead. example/main.dart shows a 30-line standalone embedding against CyclomaticComplexity.

Limitations

• The analyzer plugin covers only the three function-level rules (CC, Cognitive, Number of parameters). LCOM4 / CBO / RFC / library coupling and the unused detector are CLI-only because they need a project-wide index that the analyzer-plugin API can't maintain efficiently per-file.
• Built-in metric set is curated. See doc/calibration.md for the selection principles.
• Per-file Martin granularity. efferent-coupling / afferent-coupling / instability ship as change-impact rankings rather than Pain/Uselessness verdicts (polarity neutral, no default warning). See doc/calibration.md.
• Not a fit if you need per-line metric thresholds in the IDE for the full metric suite, you don't engage with the dismiss channel at all (a pure-fail-fast linter is a better fit), or you're on Dart < 3.10 / analyzer < 13.

Development

dart pub get
dart format lib test example
dart analyze lib test example   # `dart analyze` (no path) loads the plugin isolate and may flake
dart test
dart pub run coverage:test_with_coverage  # 100% line coverage is required

See AGENTS.md for the contributor / AI-agent workflow notes.

Bundled Claude Code skill

.claude/skills/dart-cli-app-best-practices/ is a verbatim copy of the dart-cli-app-best-practices skill from kevmoo/dash_skills (Apache-2.0). It informs the CLI entrypoint conventions used in bin/ and lib/src/entry_point.dart — keep it in sync upstream when refactoring.

License

MIT for dartrics itself; the bundled skill in .claude/skills/ is Apache-2.0 (see its SKILL.md frontmatter).