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 embedding-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/embedding-generator)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/embedding-generator"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/embedding-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/embedding-generator"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/embedding-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 35 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 180 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00056 | $0.01283 |
| Opus 5.5 | $0.00022 | $0.00513 |
| Sonnet 5.5 | $0.00011 | $0.00257 |
| Haiku 4.5 | $0.00006 | $0.00128 |
Grade A, and why
embedding-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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EmbeddingGenerator Operator Reference
EmbeddingGenerator reads one text column from the current dataframe, sends the
full column as a batch into an embedding service, writes the returned vectors
into output_key, persists the dataframe, and returns [output_key].
1. Imports
from dataflow.operators.core_text import EmbeddingGenerator
from dataflow.serving import APILLMServing_request
from dataflow.serving import LocalEmbeddingServing
from dataflow.serving import LiteLLMServing
from dataflow.serving import LocalModelLLMServing_vllm
2. Embedding Serving Options
Option A: Remote API with APILLMServing_request
APILLMServing_request(
api_url="https://api.openai.com/v1/embeddings",
key_name_of_api_key="DF_API_KEY",
model_name="text-embedding-3-small",
max_workers=20,
)
This works because APILLMServing_request implements:
generate_embedding_from_input(texts)
Option B: Local embedding model with LocalEmbeddingServing
LocalEmbeddingServing(
model_name="all-MiniLM-L6-v2",
device=None,
max_workers=2,
)
Requires:
pip install "open-dataflow[vectorsql]"
Option C: Provider-agnostic API via LiteLLMServing
LiteLLMServing(
serving_type="embedding",
model_name="text-embedding-3-small",
key_name_of_api_key="DF_API_KEY",
max_workers=10,
)
This is suitable when you want to route embedding requests through LiteLLM.
Option D: Local vLLM backend with LocalModelLLMServing_vllm
LocalModelLLMServing_vllm(
hf_model_name_or_path="your-embedding-capable-model",
vllm_tensor_parallel_size=1,
)
This works only if the selected vLLM model/backend supports embedding through
llm.embed(...).
Practical List of Supported Serving Examples
The following classes in dataflow.serving currently expose
generate_embedding_from_input(...) and are practical candidates for
EmbeddingGenerator:
APILLMServing_requestLocalEmbeddingServingLiteLLMServingLocalModelLLMServing_vllm
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 · 196 lines · 56 tokens per session scan A 8fe6d2d04a59
embedding-generator is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (253 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 56 tokens to every session and 1,283 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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