security-check

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Aktualisiert8. Mai 2026 um 06:40

Scan Python projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Focused on data science pipelines handling API keys, tokens, and LLM integrations.

Installation

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Quelle

Benkapner

Benkapner/claude-code-basecamp

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Verwandte BerufeSOC

Basierend auf der SOC-Berufsklassifikation

InformationssicherheitsanalystenInformatik- und Mathematikberufe·SOC 15-1212

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4 Dateien

SKILL.md

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name	security-check
version	1.0
description	Scan Python projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Focused on data science pipelines handling API keys, tokens, and LLM integrations.

Security Check Skill

Scan for credential leaks, insecure code patterns, and LLM security issues in Python data science projects.

When to Activate

Before committing changes
After modifying .env, .gitignore, or config files
When adding new API integrations or credentials
When reviewing code that handles tokens, keys, or passwords
When code sends data to external LLM APIs (Gemini, OpenAI, etc.)
Periodic security hygiene checks

Check Process

Read skills/scan-process.md for the full 6-step scan process with bash commands.

Pattern Reference

Read skills/pattern-tables.md for API key regex patterns, insecure Python code patterns, and LLM-specific security patterns with severity classifications.

Behavioral Rules

Read guidelines.md for severity classifications, remediation requirements, and escalation rules.

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name	security-check
version	1.0
description	Scan Python projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Focused on data science pipelines handling API keys, tokens, and LLM integrations.

Security Check Skill

Scan for credential leaks, insecure code patterns, and LLM security issues in Python data science projects.

When to Activate

Before committing changes
After modifying .env, .gitignore, or config files
When adding new API integrations or credentials
When reviewing code that handles tokens, keys, or passwords
When code sends data to external LLM APIs (Gemini, OpenAI, etc.)
Periodic security hygiene checks

Check Process

Read skills/scan-process.md for the full 6-step scan process with bash commands.

Pattern Reference

Read skills/pattern-tables.md for API key regex patterns, insecure Python code patterns, and LLM-specific security patterns with severity classifications.

Behavioral Rules

Read guidelines.md for severity classifications, remediation requirements, and escalation rules.