Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add OpenDCAI/DataFlow-WebUI --skill bench-dataset-evaluator-questiongit clone --depth 1 https://github.com/OpenDCAI/DataFlow-WebUIWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/opendcai/dataflow-webui/bench-dataset-evaluator-question)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/bench-dataset-evaluator-question"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/bench-dataset-evaluator-question.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01497 |
| Opus 5 | $0.00022 | $0.00749 |
| Sonnet 5 | $0.00009 | $0.00299 |
| Haiku 4.5 | $0.00004 | $0.00150 |
Grade A, and why
bench-dataset-evaluator-question scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BenchDatasetEvaluatorQuestion Operator Reference
BenchDatasetEvaluatorQuestion extends BenchDatasetEvaluator with support for question context and subquestions.
1. Import
from dataflow.operators.core_text import BenchDatasetEvaluatorQuestion
2. Match Mode
Constructor
BenchDatasetEvaluatorQuestion(
eval_result_path=None,
compare_method="match",
)
| Parameter | Required | Default | Description |
|---|---|---|---|
eval_result_path |
No | Auto-generated | Path to save evaluation statistics |
compare_method |
No | "match" |
Must be "match" |
system_prompt |
No | "You are a helpful assistant..." |
Not used in match mode |
llm_serving |
No | None |
Not used in match mode |
prompt_template |
No | AnswerJudgePromptQuestion |
Not used in match mode |
run() Signature
op.run(
storage=self.storage.step(),
input_question_key="question",
input_test_answer_key="generated_cot",
input_gt_answer_key="golden_answer",
)
# returns: list of column names
| Parameter | Required | Default | Description |
|---|---|---|---|
storage |
Yes | None | Storage step object |
input_question_key |
No | "question" |
Question column |
input_test_answer_key |
No | "generated_cot" |
Predicted answer column |
input_gt_answer_key |
No | "golden_answer" |
Ground truth column |
Usage Example
from dataflow.operators.core_text import BenchDatasetEvaluatorQuestion
from dataflow.utils.storage import FileStorage
class MyPipeline:
def __init__(self):
self.storage = FileStorage(
first_entry_file_name="./data/bench.jsonl",
cache_path="./cache",
file_name_prefix="step",
cache_type="jsonl"
)
self.evaluator = BenchDatasetEvaluatorQuestion(
compare_method="match",
eval_result_path="./results/match_eval.json"
)
def forward(self):
self.evaluator.run(
storage=self.storage.step(),
input_question_key="question",
input_test_answer_key="predicted_answer",
input_gt_answer_key="ground_truth"
)
if __name__ == "__main__":
pipeline = MyPipeline()
pipeline.forward()
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 222 lines · 43 tokens per session scan A ad51097701ea
bench-dataset-evaluator-question is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (231 stars, last pushed 11d ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,497 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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