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 kcentergreedy-filtergit 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/kcentergreedy-filter)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/kcentergreedy-filter"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/kcentergreedy-filter.svg" alt="Measured on agentmods" 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 29 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 157 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 | $0.00054 | $0.01301 |
| Opus 5 | $0.00027 | $0.00651 |
| Sonnet 5 | $0.00011 | $0.00260 |
| Haiku 4.5 | $0.00005 | $0.00130 |
Grade A, and why
kcentergreedy-filter 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 8d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KCenterGreedyFilter Operator Reference
KCenterGreedyFilter uses the K-Center Greedy algorithm to select the most diverse num_samples rows, deleting all others.
1. Imports
from dataflow.operators.core_text import KCenterGreedyFilter
from dataflow.serving import APILLMServing_request
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,
)
Option B: Local embedding model with LocalEmbeddingServing
LocalEmbeddingServing(
model_name="all-MiniLM-L6-v2",
device=None,
max_workers=2,
)
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,
)
Option D: Local vLLM backend with LocalModelLLMServing_vllm
LocalModelLLMServing_vllm(
hf_model_name_or_path="your-embedding-capable-model",
vllm_tensor_parallel_size=1,
)
Practical Serving Note
The operator calls embedding_serving.generate_embedding_from_input(texts). Any serving object implementing this method can be used.
Supported: APILLMServing_request, LocalEmbeddingServing, LiteLLMServing, LocalModelLLMServing_vllm
Not supported: LocalModelLLMServing_sglang (raises NotImplementedError)
3. Constructor
KCenterGreedyFilter(
num_samples=1000,
embedding_serving=embedding_serving,
)
| Parameter | Required | Default | Description |
|---|---|---|---|
num_samples |
Yes | None | Number of rows to keep; must be ≤ total DataFrame row count. |
embedding_serving |
No | None |
Embedding service object implementing generate_embedding_from_input(...). Must point to /v1/embeddings endpoint if using APILLMServing_request. |
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.
- 8d ago First seen · 180 lines · 54 tokens per session scan A bca0ba915b11
kcentergreedy-filter is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (234 stars, last pushed 12d ago), licensed Apache-2.0. It adds 54 tokens to every session and 1,301 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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