Skill flagged — suspicious patterns detected

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text-cleaner-lite

v1.0.0

Normalize whitespace, remove duplicated blank lines, and trim leading/trailing spaces from text input.

0· 78·0 current·0 all-time

Install

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Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "text-cleaner-lite" (askjda/text-cleaner-lite) from ClawHub.
Skill page: https://clawhub.ai/askjda/text-cleaner-lite
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

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openclaw skills install text-cleaner-lite

ClawHub CLI

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npx clawhub@latest install text-cleaner-lite
Security Scan
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Suspicious
high confidence
!
Purpose & Capability
The skill name/description say it normalizes whitespace and trims text, but main.py does not implement any text processing: it only prints a JSON object echoing the provided --input and --mode. The declared outputs (cleaned_text) are not produced. This is a direct mismatch between claimed capability and actual code.
!
Instruction Scope
SKILL.md instructs running 'python main.py --input sample.txt --mode clean' implying the tool will read/process a file or input text. main.py treats --input as a string or path but does not read files or perform cleaning. The SKILL.md also contains a templated risk statement (looks like an unresolved template) and a minor formatting artifact in the usage example ('\b ash'), indicating sloppy packaging. Instructions therefore over-promise relative to runtime behavior.
Install Mechanism
No install spec and no third-party dependencies; the skill is instruction-only with a single small Python file using only argparse and json. There are no downloads or install steps that add risk.
Credentials
The skill requests no environment variables, no credentials, and no config paths. There is no evidence of requests for unrelated secrets or excessive permissions.
Persistence & Privilege
The skill does not request always:true, does not modify other skills or system settings, and has no install-time persistence. Autonomous invocation defaults are unchanged (normal).
What to consider before installing
This package appears to be a stub/placeholder rather than a working text-cleaner. If you need a text-normalization skill, do not rely on this in production until the author provides code that actually performs the described transformations. Before installing or invoking: (1) request the full source or a link to a repository showing the implementation, (2) verify main.py reads input and outputs cleaned_text (not just metadata), (3) confirm version consistency (registry metadata says 1.0.0 but skill.json lists 0.1.0) and fix unresolved template text in SKILL.md, and (4) run the code in a safe, isolated environment to validate behavior. The current footprint is low-risk but functionally misleading.

Like a lobster shell, security has layers — review code before you run it.

latestvk973egc00g6r3pjt23wmmm2yb984d5th
78downloads
0stars
1versions
Updated 2w ago
v1.0.0
MIT-0

Skill Name

text-cleaner-lite

Function Description

Normalize whitespace, remove duplicated blank lines, and trim leading/trailing spaces.

Input Parameters

  • raw_text (string), keep_newlines (boolean, optional)

Output Result

  • cleaned_text (string)

Usage Example

ash python main.py --input sample.txt --mode clean

Risk Statement

  • Risk level: L1
  • This skill may perform actions matching category $(@{skill_id=skill_001; name=text-cleaner-lite; category=text-processing; risk_level=L1; source_template=skillsmp_download/1/SKILL.md; description=Normalize whitespace, remove duplicated blank lines, and trim leading/trailing spaces.; input=raw_text (string), keep_newlines (boolean, optional); output=cleaned_text (string); tags=System.Object[]; example=python main.py --input sample.txt --mode clean}.category) and should be reviewed before production use.

Category Tags

  • text-processing, normalization, safe

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