Proposal: EvalPort export for evals.json/grading.json/benchmark.json (interop with Anthropic skill-creator format)

Hi — first, thanks for building this. The safety/quality/reliability/cost split (and the audit scope rules around `--include-all`) is a genuinely well-thought-out shape for skill evaluation, and I appreciate that `skill_eval/eval_schemas.py` was already written to match Anthropic skill-creator's `evals.json`/`grading.json`/`benchmark.json` formats rather than inventing yet another bespoke schema. That's a rare and useful design choice.

I maintain [EvalPort](https://github.com/adhabnr-ux/evalport) (Apache-2.0), an open interchange format for portable LLM eval test cases, graders, suites, and results, plus a Python/TS SDK (`evalport-sdk`) and 37 adapter packages that translate to/from other eval tools (DeepEval, LangSmith, MLflow, Ragas, Guardrails, etc. — all under `adapters/` in that repo). The goal is the same instinct visible in your `eval_schemas.py` docstring, generalized: instead of every eval tool inventing its own JSON shape, define one shared shape and let tools import/export it.

**Why I think skill-eval is an interesting fit:** your dataclasses already map almost 1:1 onto EvalPort's core objects:

| skill-eval | EvalPort |
|---|---|
| `EvalCase` (id, prompt, expected_output, files, assertions) | `TestCase` |
| `AssertionResult` / `GradingResult` (assertion_results, pass_rate, execution_metrics) | `Grader` output / `Result` |
| `BenchmarkReport` (scores across the 4 dimensions, runs) | `ResultSet` |
| `TriggerQuery` / `TriggerReport` | `TestCase` + `ResultSet` (trigger-rate as a metric) |

Because skill-eval already speaks the skill-creator vocabulary, an EvalPort adapter here would effectively give you two interop paths for the price of one: skill-eval's native format, skill-creator's format, and EvalPort as the neutral middle layer other frameworks can already read/write. Concretely, something like:

```python
from evalport import TestCase, Result, ResultSet
from skill_eval.eval_schemas import EvalCase, BenchmarkReport

def eval_case_to_evalport(ec: EvalCase) -> TestCase:
return TestCase(
id=ec.id,
input=ec.prompt,
expected=ec.expected_output,
metadata={"assertions": ec.assertions, "files": ec.files},
)

def benchmark_report_to_resultset(report: BenchmarkReport) -> ResultSet:
return ResultSet(
suite_id=report.skill_name,
results=[Result.from_dict(r) for r in report.runs],
scores=report.scores,
passed=report.passed,
)
```

Comparable adapters already in the repo if it's useful to see the shape of a finished one: `adapters/deepeval-openeval-adapter` (assertion/metric-based grading, closest in spirit to your `AssertionResult`) and `adapters/guardrails-openeval-adapter` (safety-oriented, closest in spirit to your audit dimension).

I don't want to presume a PR is wanted (I saw the CONTRIBUTING.md note to open an issue first for anything nontrivial, which is exactly what this is), so I'm floating the idea here first. Happy to build a `skill-eval` <-> EvalPort adapter and send it as a PR if that's of interest, or to just leave this here as a pointer in case it's useful for future compatibility work — no pressure either way.

— Sahi, independent contributor (not affiliated with AWS)