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 text2multihopqa-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/text2multihopqa-generator)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/text2multihopqa-generator"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/text2multihopqa-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/text2multihopqa-generator"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/text2multihopqa-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 | $0.00089 | $0.00943 |
| Opus 5.5 | $0.00036 | $0.00377 |
| Sonnet 5.5 | $0.00018 | $0.00189 |
| Haiku 4.5 | $0.00009 | $0.00094 |
Grade A, and why
text2multihopqa-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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Text2MultiHopQAGenerator Operator Reference
Text2MultiHopQAGenerator reads one text column, generates up to num_q multi-hop QA pairs per input row, stores the per-row QA list in output_key, stores metadata in output_meta_key, then filters out rows whose generated QA list is empty.
See examples/good.md for a valid pipeline pattern and examples/bad.md for common failure cases.
1. Import
from dataflow.operators.core_text import Text2MultiHopQAGenerator
2. Constructor
Text2MultiHopQAGenerator(
llm_serving=llm,
seed=0,
lang="en",
prompt_template=None,
num_q=5,
)
| Parameter | Required | Default | Description |
|---|---|---|---|
llm_serving |
Yes | None | LLM backend passed through to ExampleConstructor, which later calls generate_from_input(...). |
seed |
No | 0 |
Used to initialize random.Random(seed). |
lang |
No | "en" |
Controls prompt construction and sentence splitting logic. |
prompt_template |
No | None |
If omitted, uses Text2MultiHopQAGeneratorPrompt(lang=self.lang). |
num_q |
No | 5 |
Maximum number of QA pairs kept per row after generation. |
3. run() Signature
op.run(
storage=self.storage.step(),
input_key="cleaned_chunk",
output_key="QA_pairs",
output_meta_key="QA_metadata",
)
| Parameter | Required | Default | Description |
|---|---|---|---|
storage |
Yes | None | Used as storage.read("dataframe") and storage.write(dataframe). |
input_key |
No | "cleaned_chunk" |
Source text column. This column must already exist. |
output_key |
No | "QA_pairs" |
Output column containing a list of QA dicts for each remaining row. This column must not already exist. |
output_meta_key |
No | "QA_metadata" |
Output column containing metadata dicts for each remaining row. |
Return Value
The method returns [output_key].
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 · 116 lines · 89 tokens per session scan A e243500b51ef
text2multihopqa-generator is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (253 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 89 tokens to every session and 943 once invoked, about $0.0004 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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