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-refinernpx skills add OpenDCAI/DataFlow-WebUI --skill prompted-refinergit 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-refiner)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/prompted-refiner"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/prompted-refiner.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 | $0.00029 | $0.00667 |
| Opus 5 | $0.00015 | $0.00333 |
| Sonnet 5 | $0.00006 | $0.00133 |
| Haiku 4.5 | $0.00003 | $0.00067 |
Grade A, and why
prompted-refiner 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 6d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PromptedRefiner Operator Reference
PromptedRefiner uses an LLM to refine/rewrite text in a column and overwrites the original column with refined results.
1. Import
from dataflow.operators.core_text import PromptedRefiner
2. Constructor
PromptedRefiner(
llm_serving=llm_serving,
system_prompt="You are a helpful agent.",
)
| Parameter | Required | Default | Description |
|---|---|---|---|
llm_serving |
Yes | None | LLM service object |
system_prompt |
No | "You are a helpful agent." |
Instruction for text refinement |
3. run() Signature
op.run(
storage=self.storage.step(),
input_key="raw_content",
)
# returns: None
| Parameter | Required | Default | Description |
|---|---|---|---|
storage |
Yes | None | Storage step object |
input_key |
No | "raw_content" |
Column to refine (overwritten in place) |
4. Usage Example
from dataflow.operators.core_text import PromptedRefiner
from dataflow.serving import APILLMServing_request
from dataflow.utils.storage import FileStorage
class MyPipeline:
def __init__(self):
self.storage = FileStorage(
first_entry_file_name="./data/input.jsonl",
cache_path="./cache",
file_name_prefix="step",
cache_type="jsonl"
)
self.llm_serving = APILLMServing_request(
api_url="https://api.openai.com/v1/chat/completions",
key_name_of_api_key="DF_API_KEY",
model_name="gpt-4o",
max_workers=10
)
self.refiner = PromptedRefiner(
llm_serving=self.llm_serving,
system_prompt="Refine the following text for clarity."
)
def forward(self):
self.refiner.run(
storage=self.storage.step(),
input_key="content"
)
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.
- 6d ago First seen · 103 lines · 29 tokens per session scan A c28219a8a73a
prompted-refiner is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (231 stars, last pushed 10d ago), licensed Apache-2.0. It adds 29 tokens to every session and 667 once invoked, about $0.0001 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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