bighardperson/computer-science-skills-collection/antivirus
Claude Code and agent skill by bighardperson/computer-science-skills-collection
- community
Checking that the SKILL.md file is still in the repository.
Scan installed OpenClaw skills for malicious code patterns including ClickFix social engineering, reverse shell (RAT), and data exfiltration. Uses OG-Text model for agentic detection.
Install
npx vskill@latest install bighardperson/computer-science-skills-collection/antivirusSKILL.md
Skill Scanner — Malicious Skill Detection for OpenClaw
Scan all installed OpenClaw skills to detect hidden malicious behavior such as:
- ClickFix — Social engineering that tricks users into downloading and running malicious code (e.g., fake update prompts, disguised install commands)
- RAT (Remote Access Trojan) — Reverse shells, backdoors, or covert command-and-control connections
- Info Stealer — Exfiltration of credentials, SSH keys, tokens, environment variables, browser data, or keychain items
How It Works
This skill uses the OG-Text security model to analyze each installed skill's source code. The agent reads skill files, chunks long content, sends each chunk to the model with a specialized security prompt, and aggregates the findings into a risk report.
Workflow
Follow these steps in order when the user asks to scan skills.
Step 1: Discover installed skills
Find all installed skill directories. Skills can be in multiple locations:
For each directory found, look for these files that define a skill:
SKILL.md— Skill definition and instructions*.ts,*.js— Source code (TypeScript/JavaScript)*.sh,*.bash— Shell scripts*.py— Python scriptspackage.json— Package definition with dependenciesopenclaw.plugin.json— Plugin configuration
Step 2: Read and collect skill content
For each skill found, read ALL source files. Prioritize files in this order:
- Any
.ts,.js,.sh,.bash,.pyfiles (executable code — highest risk) SKILL.md(may contain embedded shell commands or curl calls)package.json(check for suspicious dependencies or scripts)openclaw.plugin.json(check for dangerous hooks or permissions)
Concatenate all content for each skill with clear file markers:
Step 3: Chunk the content
If a skill's combined content exceeds 4000 characters, split it into chunks:
- Chunk size: 4000 characters
- Overlap: 200 characters (to catch malicious patterns that span chunk boundaries)
If the content is under 4000 characters, treat it as a single chunk.
Step 4: Analyze each chunk with OG-Text
For each chunk, call the OG-Text model using curl:
The <ANALYSIS_PROMPT> must be constructed as follows (replace {CHUNK_INDEX}, {CHUNK_TOTAL}, {SKILL_NAME}, and {CHUNK_CONTENT} with actual values):
{CHUNK_CONTENT}
Step 5: Parse the response
The OG-Text model returns a JSON response in the choices[0].message.content field. Parse it to extract:
isRisky— Whether malicious patterns were foundconfidence— How confident the model is (0.0-1.0)category— The threat type detectedseverity— Risk severity levelfindings— Detailed list of suspicious code snippets
If the response is not valid JSON, try to extract JSON from markdown code fences. If parsing still fails and the response text contains words like "malicious", "suspicious", "backdoor", "reverse shell", treat it as a detection with confidence 0.7.
Step 6: Aggregate results per skill
For each skill, combine results from all chunks:
- If ANY chunk has
isRisky: truewithconfidence >= 0.7, mark the skill as risky - Use the highest severity found across all chunks
- Collect all findings from all chunks
- Track the highest confidence score
Step 7: Generate the report
Present results to the user in plain language. Use this format:
Step 8: Recommend actions
Based on findings, suggest these actions:
For Critical/High risks:
- Remove the skill immediately:
openclaw skills remove <skill-name> - If credentials were at risk, advise rotating affected keys/passwords
- Check system for persistence (cron jobs, launchd agents)
For Medium risks:
- Review the suspicious code manually
- Consider disabling the skill until reviewed
- Run a deeper scan
For Low risks:
- Note the finding for awareness
- Monitor the skill's behavior
Severity Guide
| Severity | Criteria | |
|---|---|---|
| Critical | Active exfiltration, reverse shell, or confirmed malicious payload | |
| High | Download-and-execute patterns, credential access with external send | |
| Medium | Suspicious obfuscation, unusual file access, or untrusted dependencies | |
| Low | Minor concerns like overly broad file access or unusual permissions |
On-Demand Deep Scan
If the user asks for a deep scan of a specific skill, perform additional checks:
- Dependency audit — Read
package.jsonand check each dependency:
- Is it a well-known package or an obscure one?
- Does the version look like typosquatting (e.g.,
lod-ashinstead oflodash)? - Are there suspicious preinstall/postinstall scripts?
- Network analysis — Extract all URLs, hostnames, and IP addresses from the code:
- Are they well-known services or unknown hosts?
- Are there hardcoded IP addresses (potential C2)?
- Do any URLs use URL shorteners or redirects?
- Permission analysis — What system access does the skill request?
- File system access scope
- Network access patterns
- Shell command execution
- Environment variable reads
Example Malicious Patterns
These are examples of what to detect (for reference, DO NOT include these in analysis prompts):
ClickFix example hidden in a weather skill:
RAT example hidden in a notes skill:
Info stealer example hidden in a productivity skill:
Scheduling Periodic Scans
Offer to schedule regular skill scans:
Notes
This is the start of the file. Read the full SKILL.md on GitHub.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/antivirus/SKILL.md
Popularity
- Repository stars
- 56
- Repository forks
- 10
- 7-day trend score
- 20
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
Works with
Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.
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