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harrylabsj

Crypto News Noise Filter

by haidong · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
71
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1
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Install in OpenClaw
/install crypto-news-noise-filter
Description
A critical reading skill that helps users separate signal from hype in crypto news headlines. Use when the user sees alarming or exciting crypto news. Prompt...
README (SKILL.md)

crypto-news-noise-filter

A critical reading skill that helps users separate signal from hype in crypto news headlines.

Workflow

  1. Take the news headline or pasted article text.
  2. Identify what actually happened: separate facts from interpretation.
  3. Check whether the headline matches the substance of the story.
  4. Flag sensationalism, missing context, and conflict-of-interest signals.
  5. Give a reading verdict: high signal, mixed, or noise.

Output Format

  • What actually happened
  • Headline vs. reality check
  • Context that changes the picture
  • Who benefits from this narrative
  • What to watch before reacting

Quality Bar

  • Does not declare the market direction or price impact.
  • Focuses on media quality and information integrity.
  • Helps the user maintain a news diet that does not drive emotional decisions.

Edge Cases

  • If the news involves a regulatory announcement, flag that interpretation is especially difficult and context matters enormously.
  • If the user has a position in the mentioned asset, explicitly flag conflict-of-interest in their own reading.

Compatibility

  • Prompt-only, no news API required.
  • Works from pasted text or manually described headlines.
Usage Guidance
The skill's written instructions are benign (prompt-only), but the included handler.py contains a hard-coded file read from /Users/jianghaidong/.openclaw/skills/{skill_name}/SKILL.md — this is unnecessary for a headline-analysis skill and could expose local files. Before installing: (1) ask the publisher why the code reads that absolute path and request a version that does not access arbitrary home directories; (2) inspect or remove the file-read code (_load_skill_meta) or change it to read only packaged resources relative to the skill bundle; (3) run the skill in a sandboxed environment with no access to sensitive files or networks; (4) prefer skills from a known homepage/maintainer. If you cannot get a clear explanation or a fixed code version, avoid installing it.
Capability Analysis
Type: OpenClaw Skill Name: crypto-news-noise-filter Version: 1.0.0 The skill contains a 'handler.py' file with a hardcoded absolute path referencing a specific local user directory (/Users/jianghaidong/), which is highly irregular for a portable skill bundle. Furthermore, the '_load_skill_meta' function uses the 'skill_name' argument to construct file paths without any sanitization, creating a Local File Inclusion (LFI) or path traversal vulnerability. While no explicit exfiltration logic is present, the combination of unsafe file handling and the discrepancy between the 'prompt-only' description in 'SKILL.md' and the actual Python implementation is a significant security risk.
Capability Tags
crypto
Capability Assessment
Purpose & Capability
Name/description: crypto headline analysis. SKILL.md is prompt-only and needs no filesystem or credentials. However handler.py tries to load SKILL.md from a hard-coded absolute path (/Users/jianghaidong/.openclaw/skills/{skill_name}/SKILL.md). Reading an arbitrary user home path is unrelated to the stated purpose and is disproportionate.
Instruction Scope
The SKILL.md instructions are scoped to analyzing pasted headlines and explicitly say no API or external services are required. They do not mention reading local files or credentials. The handler implementation, however, instructs the runtime to read a specific local file path — a scope mismatch.
Install Mechanism
No install spec (instruction-only) and no external downloads, so there is low install-time risk.
Credentials
No environment variables or credentials are declared or required by SKILL.md, yet the code accesses the local filesystem at a hard-coded user path. That access is not justified by the skill's purpose and could expose unrelated local data if the path or code is changed.
Persistence & Privilege
always is false and the skill does not request system-wide changes or persistent privileges. It does not modify other skills' configs in the provided code.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install crypto-news-noise-filter
  3. After installation, invoke the skill by name or use /crypto-news-noise-filter
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of crypto-news-noise-filter. - Enables critical reading of crypto news by separating facts from hype. - Flags sensationalism, missing context, and conflict-of-interest signals in headlines. - Provides a structured output: what happened, reality check, context, beneficiaries, and action tips. - Focuses on integrity of information, advising users against emotionally-driven decisions. - Designed for prompt-only use—no integrations or news APIs required.
Metadata
Slug crypto-news-noise-filter
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Crypto News Noise Filter?

A critical reading skill that helps users separate signal from hype in crypto news headlines. Use when the user sees alarming or exciting crypto news. Prompt... It is an AI Agent Skill for Claude Code / OpenClaw, with 71 downloads so far.

How do I install Crypto News Noise Filter?

Run "/install crypto-news-noise-filter" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Crypto News Noise Filter free?

Yes, Crypto News Noise Filter is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Crypto News Noise Filter support?

Crypto News Noise Filter is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Crypto News Noise Filter?

It is built and maintained by haidong (@harrylabsj); the current version is v1.0.0.

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