User asks for design, planning, or approach exploration before implementation. Covers new features, components, refactors, or architecture decisions. Creates design docs and proposes approaches with trade-offs.
日本語の概要は準備中です。原文の説明を表示しています。
Scan projects for credential leaks, secrets in code, insecure patterns, LLM API key exposure, PII leakage to external AI services, and .env/.gitignore misconfigurations. Especially useful for data and API integrations, regardless of implementation language.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
User asks for design, planning, or approach exploration before implementation. Covers new features, components, refactors, or architecture decisions. Creates design docs and proposes approaches with trade-offs.
日本語の概要は準備中です。原文の説明を表示しています。
Use when starting work in a repository under repositories/ that may lack CI configuration. Detects missing CI workflows (GitHub Actions, GitLab CI, CircleCI) and alerts the user to add one. Skips repos marked as research-only.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user asks to measure command execution time or optimize a feedback loop. Records explicit measurements and recommends faster alternatives such as unit tests versus integration tests. It is opt-in; it does not run on every command.
日本語の概要は準備中です。原文の説明を表示しています。
Team conventions for Python development — credentials, API clients, LLM response parsing, testing patterns, and data pipeline structure. Covers dotenv loading, retry logic, secret validation, and pipeline anti-patterns.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Use when committing or pushing changes and the repository needs a version bump. Analyzes commits using conventional commit prefixes to determine whether the next release is a major, minor, or patch increment. Also use when the user asks about versioning, release planning, or changelog generation.
日本語の概要は準備中です。原文の説明を表示しています。