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/general-filternpx skills add OpenDCAI/DataFlow-WebUI --skill general-filtergit clone --depth 1 https://github.com/OpenDCAI/DataFlow-WebUIWhat 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 | $0.00047 | $0.00774 |
| Opus 5 | $0.00023 | $0.00387 |
| Sonnet 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 3d 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.
- 3d 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 (224 stars, last pushed 7d ago), licensed Apache-2.0. It adds 47 tokens to every session and 774 once invoked, about $0.0002 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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