ai-data-security

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name: agent-security-hardening

description: Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials. Use when building or reviewing an agent runtime, autonomous worker, tool gateway, memory service, or multi-tenant agent deployment. Do not use for general web application security or offensive testing.

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name: ponytail-review

description: Quality review of a diff: bugs, security, real load, missing tests, speed, and what to delete. Each finding says what goes wrong and how to fix it.

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name: ponytail-audit

description: Quality audit of the whole repo: bugs, security, real load, missing tests, speed, and what to delete. Most important first.

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name: ponytail-audit

description: Quality audit of a whole repo: bugs, security holes, what breaks under real load, risky code without tests, slow paths, and what to delete, merge or split. Ranked, each finding explained in plain English. One-shot report, changes nothing. Use for "audit this codebase", "review the whole repo", "find bloat", "what can I delete", /ponytail-audit.

---

name: cso

description: Security audit: supported static findings; qualified profiles add reproduction and repair candidates. (gstack)

---

name: security-and-hardening

description: Hardens code against vulnerabilities. Use when auditing an input handler for vulnerabilities, when handling user input, authentication, data storage, or external integrations, or when checking a login flow is safe against the OWASP Top Ten. Use when building any feature that accepts untrusted data, manages user sessions, or interacts with third-party services. Use when auditing dependencies for known vulnerabilities, triaging package-manager audit findings, or assessing supply-chain risk in a new package. Use when personal data or privacy compliance (GDPR, CCPA) is involved.