メニュー
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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when implementing features, fixing bugs, or refactoring code that may invalidate existing docs. Detects stale documentation by matching the code diff against in-repo doc files and applies targeted updates. Relevant when code changes rename, remove, or add APIs, fields, config keys, or CLI flags. Also applies when the user says "update docs", "check docs", or "are the docs stale", or when reviewing a branch for documentation accuracy.
Unified verification engine for Python data science projects. Covers environment checks, type checking, linting, tests, security scans, code review with DS anti-patterns, and notebook checks. Commands (/verify, /quality-gate) invoke different subsets of this skill.
Use when the user asks to design, plan, or explore approaches before implementing — creating features, building components, or adding functionality that would benefit from design exploration first.
Measurement-driven code refactoring — profile before changing, measure after, keep only if metrics improve. Covers complexity reduction, extraction patterns, and bulk refactoring for mechanical changes across many files.
Team-specific Python conventions — credential management with dotenv, API client rules, LLM response parsing, TDD workflow, and testing patterns for data pipelines.
Team conventions for Python data pipelines — stage structure, JSON output format, debugging workflow, and anti-patterns. Supplements standard patterns with team-specific rules.
| 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. |
Scan for credential leaks, insecure code patterns, and LLM security issues in Python data science projects.
.env, .gitignore, or config filesRead skills/scan-process.md for the full 6-step scan process with bash commands.
Read skills/pattern-tables.md for API key regex patterns, insecure Python code patterns, and LLM-specific security patterns with severity classifications.
Read guidelines.md for severity classifications, remediation requirements, and escalation rules.