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 general-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/general-filter)<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/general-filter"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/general-filter/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/general-filter"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/general-filter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Prompt Injection · line 46 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00047 | $0.00774 |
| Opus 5.5 | $0.00019 | $0.00310 |
| Sonnet 5.5 | $0.00009 | $0.00155 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
general-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 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GeneralFilter Operator Reference
GeneralFilter filters DataFrame rows using a custom rule list, combining all rules with AND. It does not add new columns — it only removes rows that do not satisfy all conditions.
1. Import
from dataflow.operators.core_text import GeneralFilter
2. Constructor
GeneralFilter(
filter_rules=[
lambda df: df["score"] >= 4,
lambda df: df["text"].str.len() > 10,
]
)
| Parameter | Required | Default | Description |
|---|---|---|---|
filter_rules |
Yes | None | List of rules; each rule is a callable with signature (df: DataFrame) -> Series[bool] |
Each rule returns a boolean Series the same length as the DataFrame; True means keep the row. Multiple rules are combined with AND.
3. run() Signature
op.run(
storage=self.storage.step(),
)
# returns: "" (empty string)
| Parameter | Required | Default | Description |
|---|---|---|---|
storage |
Yes | None | DataFlowStorage step object. The operator reads a DataFrame from here and writes the filtered DataFrame back. |
Note: run() has no input_key / output_key parameters. Column names referenced in rules are written directly in the lambda.
Return Value
The method returns "" (empty string).
4. Actual Runtime Logic
The source code behavior is:
- Read the DataFrame from
storage.read("dataframe"). - Initialize a boolean mask as
pd.Series(True, index=df.index). - For each rule in
filter_rules:- Validate the rule is callable.
- Call
cond = rule_fn(df). - Validate
condis a boolean Series. - Update mask:
mask &= cond.
- Filter the DataFrame:
filtered_df = df[mask]. - Write the filtered DataFrame back via
storage.write(filtered_df). - Return
"".
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 · 109 lines · 47 tokens per session scan A 29a53a276bae
general-filter is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (253 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 47 tokens to every session and 774 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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