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 agentmods add skills/opendcai/dataflow-webui/random-domain-knowledge-row-generatornpx skills add OpenDCAI/DataFlow-WebUI --skill random-domain-knowledge-row-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/random-domain-knowledge-row-generator)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/random-domain-knowledge-row-generator"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/random-domain-knowledge-row-generator.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 | $0.00103 | $0.01066 |
| Opus 5 | $0.00051 | $0.00533 |
| Sonnet 5 | $0.00021 | $0.00213 |
| Haiku 4.5 | $0.00010 | $0.00107 |
Grade A, and why
random-domain-knowledge-row-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 5d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
RandomDomainKnowledgeRowGenerator Operator Reference
RandomDomainKnowledgeRowGenerator does not read any input column values, but it still reads the input DataFrame itself. The operator builds generation_num prompts from domain_keys, calls llm_serving.generate_from_input(...), and assigns the returned list into dataframe[output_key].
See examples/good.md for a runnable example and examples/bad.md for common failure cases.
1. Import
from dataflow.operators.core_text import RandomDomainKnowledgeRowGenerator
from dataflow.prompts.general_text import SFTFromScratchGeneratorPrompt
2. Constructor
RandomDomainKnowledgeRowGenerator(
llm_serving=llm,
generation_num=200,
domain_keys="machine learning, deep learning, neural networks",
prompt_template=SFTFromScratchGeneratorPrompt(),
)
| Parameter | Required | Default | Description |
|---|---|---|---|
llm_serving |
Yes | None | LLM serving object. It must implement generate_from_input(user_inputs, ...). Examples in dataflow.serving include APILLMServing_request, LiteLLMServing, and LocalModelLLMServing_vllm. |
generation_num |
Yes | None | Number of prompts to build and number of outputs expected from the LLM call. |
domain_keys |
Yes | None | Domain description passed directly into SFTFromScratchGeneratorPrompt.build_prompt(domain_keys). The source annotation is str, so use a string such as "finance, accounting, tax". |
prompt_template |
No in signature, but effectively required | None |
Prompt object used for every generation call. In practice you must pass an instantiated SFTFromScratchGeneratorPrompt() or another prompt allowed by @prompt_restrict(...). Leaving it as None will fail before generation starts. |
Important Constructor Notes
prompt_template=Noneis not a safe fallback. The code callsself.prompt_template.build_prompt(self.domain_keys)directly, soNoneraisesAttributeError.- The default prompt class is
SFTFromScratchGeneratorPrompt, and itsbuild_prompt()method expectsdomain_keys: str. - The prompt asks the LLM to output a single-line JSON object containing fields such as
instruction,input,output, anddomain.
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.
- 5d ago First seen · 114 lines · 103 tokens per session scan A b89ea121dc1b
random-domain-knowledge-row-generator is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (224 stars, last pushed 9d ago), licensed Apache-2.0. It adds 103 tokens to every session and 1,066 once invoked, about $0.0005 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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