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/prompted-generatornpx skills add OpenDCAI/DataFlow-WebUI --skill prompted-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/prompted-generator)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/prompted-generator"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/prompted-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 | $0.00050 | $0.00819 |
| Opus 5 | $0.00025 | $0.00409 |
| Sonnet 5 | $0.00010 | $0.00164 |
| Haiku 4.5 | $0.00005 | $0.00082 |
Grade A, and why
prompted-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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PromptedGenerator Operator Reference
PromptedGenerator is DataFlow's basic single-field LLM generation operator.
For each row in the current DataFrame, it reads the value from input_key. If that value is truthy, it builds one LLM input as:
user_prompt + str(row[input_key])
It then calls llm_serving.generate_from_input(...) in batch and writes the
generated results into output_key.
1. Import
from dataflow.operators.core_text import PromptedGenerator
2. Constructor
PromptedGenerator(
llm_serving, # required
system_prompt="You are a helpful agent.", # optional
user_prompt="", # optional
json_schema=None, # optional
)
| Parameter | Required | Default | Description |
|---|---|---|---|
llm_serving |
Yes | None | LLM service object implementing generate_from_input(...) |
system_prompt |
No | "You are a helpful agent." |
System prompt passed to the serving layer |
user_prompt |
No | "" |
Prefix prepended directly before each valid row's input text |
json_schema |
No | None |
Optional schema forwarded to the serving layer |
3. run() Signature
op.run(
storage=self.storage.step(),
input_key="raw_content",
output_key="generated_content",
)
# returns: output_key
| Parameter | Required | Default | Description |
|---|---|---|---|
storage |
Yes | None | Current operator-step storage object |
input_key |
No | "raw_content" |
Column read from the current DataFrame |
output_key |
No | "generated_content" |
Column written back to the DataFrame |
4. Actual Execution Logic
The operator performs the following steps:
- Read the current DataFrame from
storage. - Iterate over the DataFrame row by row.
- For each row, read
raw_content = row.get(input_key, ""). - Only if
raw_contentis truthy, appenduser_prompt + str(raw_content)to the batch LLM input list. - Call:
What ships with it
2 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 · 121 lines · 50 tokens per session scan A 8aed2b140986
prompted-generator is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (231 stars, last pushed 9d ago), licensed Apache-2.0. It adds 50 tokens to every session and 819 once invoked, about $0.0003 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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