auraone-evalkit 0.1.1

Local open-source evaluation tooling for rubric validation, linting, and deterministic scoring.

AuraOne EvalKit

AuraOne EvalKit is a standalone local Python package for rubric validation, rubric linting, and deterministic scoring. It installs as auraone-evalkit, imports as auraone_evalkit, and exposes the evalkit CLI.

EvalKit does not require an AuraOne account, API key, hosted tenant, database, or private reviewer pool. The files in examples/tutorial/ are synthetic tutorial data only. They are not expert-authored, human-validated, benchmark-grade, safety certifications, or claims about model quality.

Package Distinction

AuraOne has separate hosted SDKs:

Tool	Package or binary	Purpose
EvalKit	auraone-evalkit, auraone_evalkit, evalkit	Local open-source rubric tools. No API key.
Hosted Python SDK	auraone-sdk	Hosted AuraOne API client. Uses hosted services.
Hosted TypeScript SDK	@auraone/sdk	Hosted AuraOne API client for Node/TypeScript. Uses hosted services.
Hosted API CLI	aura	Hosted AuraOne command line workflows. Separate from evalkit.

Use evalkit for local files and tutorial workflows. Use auraone-sdk, @auraone/sdk, or aura only when you intend to call hosted AuraOne services.

Install

From this repository:

cd opensource/evalkit
python -m pip install -e .

After install:

evalkit --help
evalkit --version

Five-Minute Quickstart

Validate the synthetic tutorial rubric:

evalkit validate-rubric examples/tutorial/rubric.jsonl

Lint the same rubric:

evalkit lint-rubric examples/tutorial/rubric.jsonl

Score the synthetic tutorial model outputs. If --labels is omitted, EvalKit looks for labels.jsonl next to the responses file.

evalkit score \
  --rubric examples/tutorial/rubric.jsonl \
  --responses examples/tutorial/model_outputs.jsonl \
  --out /tmp/evalkit-tutorial-scores.json

Expected summary for the bundled tutorial data:

{
  "average_score": 0.645833,
  "pass_rate": 0.666667,
  "scored_outputs": 3
}

The full deterministic expected output is stored in examples/tutorial/expected_scores.json.

Commands

evalkit validate-rubric

Validates JSONL or JSON-array rubric files against the AuraOne EvalKit rubric contract.

evalkit validate-rubric examples/tutorial/rubric.jsonl --format json

Validation errors include row number, field, message, and a suggested fix.

evalkit lint-rubric

Runs rubric quality checks that catch common authoring problems before scoring.

evalkit lint-rubric examples/tutorial/rubric.jsonl --format json

The v0.1 linter includes rules for compound criteria, vague wording, missing examples, missing weight, duplicate IDs, duplicate text, inconsistent severity, unscorable language, unavailable context, unclear scoring boundaries, and weight totals.

evalkit score

Aggregates per-criterion labels into deterministic weighted scores.

evalkit score \
  --rubric examples/tutorial/rubric.jsonl \
  --responses examples/tutorial/model_outputs.jsonl \
  --labels examples/tutorial/labels.jsonl \
  --format json \
  --out /tmp/evalkit-tutorial-scores.json

Supported output formats are json, jsonl, csv, and report-json.

Data Contracts

Rubric rows are JSON objects with required fields:

• criterion_id
• domain
• task_type
• criterion
• weight
• severity
• scoring_type
• examples
• edge_cases
• disagreement_risk

See docs/schema/rubric-schema.md for the full schema and examples.

Scoring labels use:

• output_id
• criterion_id
• score
• optional applicable
• optional rationale

Scores are normalized by scoring type, multiplied by criterion weight, and divided by the applicable rubric weight. Missing labels are reported in every output record. In --strict mode, missing labels fail the command.

Documentation

• docs/architecture/two-package-architecture.md
• docs/schema/rubric-schema.md
• Repository roadmap context: ../../opensource.md
• Public AuraOne open resources: https://auraone.ai/open

Limitations

• v0.1 ships local tooling and synthetic tutorial fixtures only.
• The tutorial data is not a benchmark and should not be used to compare vendors or publish model claims.
• The linter is a deterministic authoring aid, not a replacement for domain review.
• The scorer aggregates labels supplied by the user. It does not generate labels, call LLM judges, or contact AuraOne hosted services.

Development

Run focused checks from opensource/evalkit:

python -m pytest tests/test_package_imports.py tests/schema/test_rubric_schema.py tests/scoring/test_score_cli.py tests/linting/test_rules.py tests/examples/test_tutorial_dataset.py
python -m pip wheel . --no-deps -w /tmp/evalkit-wheel