Guidelines for building safe, governed AI agent systems. Apply when writing code that uses agent frameworks, tool-calling LLMs, or multi-agent orchestration to ensure proper safety boundaries, policy enforcement, and auditability.
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement…
Interactive, input-tool powered, task refinement workflow: interrogates scope, deliverables, constraints before carrying out the task; Requires the Joyride extension.
Interface for MCP (Model Context Protocol) servers via CLI. Use when you need to interact with external tools, APIs, or data sources through MCP servers, list available MCP servers/tools, or call MCP tools from command line.
Audit MCP (Model Context Protocol) server configurations for security issues. Use this skill when: Reviewing .mcp.json files for security risks Checking MCP server args for hardcoded secrets or shell injection patterns Validating that MCP servers use pinned versions (not @latest) Detecting unpinned dependencies in MCP…
Instructions for asterixix/polish-academic-mcp, covering agents.md — ai coding agent reference, project overview, file map, 1. stateless — one mcpserver instance per request and 2. rate limiting before mcp dispatch.