Add benchflow evaluation framework

[xdotli]

Summary

• Adds benchflow to the EVALUATION_FRAMEWORKS constant in packages/tasks/src/eval.ts.

BenchFlow is the evaluation framework backing SkillsBench (arXiv:2602.12670). It runs containerized agent trials with paired with-skills / without-skills configurations, exposing the lift skills give a fixed agent + model as the headline metric.

This entry follows the same shape as the recent claw-eval (#2129), parsebench (#2096), and exgentic (#2079) additions: 6 lines added, no behaviour or data-flow changes — just registers the framework key so `eval.yaml` files in benchmark datasets can reference it.

Traction

SkillsBench is an active benchmark with material adoption:

• Paper: arXiv:2602.12670 — 40 co-authors across academic and industry contributors.
• Tasks: 91 tasks in the main repo, spanning financial analysis, code-patch, multimodal output, scientific computation, infrastructure, and other professional workflows.
• Repo activity: 1,109 ⭐ / 277 🍴 / 226 merged PRs / 56 contributors on `benchflow-ai/skillsbench` at time of writing.
• Trajectory archive: 8 contributor namespaces with multi-experiment trial bundles in `benchflow-ai/skillsbench-trajectories`.
• HF datasets already live:
  • `benchflow/skillsbench` — task corpus (parquet, full source-commit pin).
  • `benchflow/skillsbench-leaderboard` — trajectory submission target patterned after `harborframework/terminal-bench-2-leaderboard`.

Tested locally

• `pnpm format:check` — pass
• `pnpm lint:check` — pass
• `pnpm check` (tsc) — pass
• `pnpm test` — 16/16 pass
• `pnpm build` — `dist/esm/eval.js`, `dist/commonjs/eval.js`, and `eval.d.ts` all carry the new entry.

Followup

After this lands, `benchflow/skillsbench` will publish its `eval.yaml` and request allow-list inclusion via `huggingface/hub-docs` (per the registering-a-benchmark doc).

---

Note

Low Risk
Low risk: this only adds a new entry to the EVALUATION_FRAMEWORKS allowlist with metadata (name/description/url) and does not change evaluation logic or data flow.

Overview
Adds benchflow to the EVALUATION_FRAMEWORKS registry in packages/tasks/src/eval.ts, enabling eval.yaml benchmark configs to reference BenchFlow by key and surface its metadata (name/description/url).

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[xdotli]

End-to-end demo that the new framework key threads through every consumer surface.

1. Built artefacts carry the entry

$ pnpm --filter @huggingface/tasks build
$ grep -A 3 benchflow packages/tasks/dist/esm/eval.js
    benchflow: {
        name: "benchflow",
        description: "BenchFlow is an evaluation framework …",
        url: "https://github.com/benchflow-ai/benchflow",
    },
$ grep -A 3 benchflow packages/tasks/dist/esm/eval.d.ts
    readonly benchflow: {
        readonly name: "benchflow";
        readonly description: "BenchFlow is an evaluation framework …";
        readonly url: "https://github.com/benchflow-ai/benchflow";
    };

2. Downstream eval.yaml validates against the spec

The benchmark dataset that will consume this key is already public at https://huggingface.co/datasets/benchflow/skillsbench. Its `eval.yaml` is drafted and validates against `docs/hub/eval-results.md`:

```yaml
name: SkillsBench
description: >
SkillsBench measures how well AI agents leverage Skills — structured
packages of procedural knowledge — to complete realistic professional
workflows across many domains. The headline metric is the with-skills
vs. without-skills delta. See arXiv:2602.12670.
evaluation_framework: benchflow
tasks:

• id: skillsbench
  config: default
  split: train
  ```

Validation transcript:

```
$ python validate.py /tmp/skillsbench-eval.yaml
PASS — eval.yaml conforms to spec
• required fields present (name, description, evaluation_framework, tasks)
• evaluation_framework='benchflow' is in the patched EVALUATION_FRAMEWORKS set
• tasks[0] has required id
```

3. Model-repo `.eval_results/skillsbench.yaml` round-trips

```yaml

• dataset:
  id: benchflow/skillsbench
  task_id: skillsbench
  value: 0.42
  date: "2026-05-05"
  source:
  url: https://huggingface.co/datasets/benchflow/skillsbench-leaderboard
  name: SkillsBench Leaderboard submissions
  user: benchflow
  notes: "with-skills"
  ```

```
$ python validate_eval_results.py /tmp/skillsbench-model-eval-result.yaml
PASS — 1 entry valid against eval-results spec
• dataset.id resolves to a Hub dataset
• dataset.task_id matches the eval.yaml's tasks[].id
• value is numeric, date is ISO-8601, source.url is set
```

4. Allow-list request already filed

huggingface/hub-docs#2456 — references this PR and lists the dataset's traction signals so the allow-list flip is one step after merge.

Once this lands and the hub deploys, `benchflow/skillsbench` pushes its `eval.yaml` and the chain is live.

[NathanHB]

Hey @xdotli ! Thanks for the submission. The bench looks super interesting, measuring how well models can use skills is a fundamental right now.
On what open model did you run the benchmark and is it reported in recent model release?