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-answer-generatorgit 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-answer-generator)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/bench-answer-generator"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/bench-answer-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/bench-answer-generator"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/bench-answer-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.01748 |
| Opus 5.5 | $0.00022 | $0.00699 |
| Sonnet 5.5 | $0.00011 | $0.00350 |
| Haiku 4.5 | $0.00006 | $0.00175 |
Grade A, and why
bench-answer-generator 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 1mo 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BenchAnswerGenerator Operator Reference
BenchAnswerGenerator generates model answers from a benchmark dataframe and is designed to align with UnifiedBenchDatasetEvaluator.
1. Import
from dataflow.operators.core_text import BenchAnswerGenerator
2. Constructor
BenchAnswerGenerator(
eval_type="key2_qa",
llm_serving=llm,
prompt_template=FormatStrPrompt(f_str_template="Question: {question}\nAnswer:"),
system_prompt="You are a helpful assistant specialized in generating answers to questions.",
allow_overwrite=False,
force_generate=False,
)
| Parameter | Required | Default | Description |
|---|---|---|---|
eval_type |
No | "key2_qa" |
Evaluation type |
llm_serving |
Yes | None |
LLM service object implementing generate_from_input(...) |
prompt_template |
No | FormatStrPrompt |
Prompt object used to build prompts. In practice, pass a FormatStrPrompt(...) instance, None, or a DIYPromptABC subclass instance |
system_prompt |
No | "You are a helpful assistant specialized in generating answers to questions." |
System prompt forwarded to the serving layer when supported |
allow_overwrite |
No | False |
Whether to overwrite an existing output column |
force_generate |
No | False |
Whether to force generation for some types that are skipped by default |
Important prompt_template Note
Although the source code sets the default value to FormatStrPrompt, that
default is the class object itself, not an instance.
In normal usage, you usually want to pass a FormatStrPrompt(...) instance so
you can explicitly control the prompt text. None is also supported and makes
the operator fall back to its built-in prompt builder.
Use one of these patterns instead:
from dataflow.prompts.core_text import FormatStrPrompt
prompt_template=FormatStrPrompt(
f_str_template="Question: {question}\nAnswer:"
)
or
prompt_template=None
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.
- 1mo ago First seen · 179 lines · 55 tokens per session scan A d8c078527003
bench-answer-generator is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (253 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 55 tokens to every session and 1,748 once invoked, about $0.0002 per session on Opus 5.5. 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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