Design and implement governance controls for tool-using and multi-agent AI systems, including policy enforcement, approval gates, audit trails, trust scoring, rate limits, and safe tool execution.
Review, generate, and verify supply-chain integrity controls for AI agent tools, plugins, MCP servers, skills, prompts, and custom agents, including SHA-256 manifests, dependency pinning, provenance evidence, promotion gates, and CI verification.
Use when designing and implementing evaluation loops for AI agents, including reflection, evaluator-optimiser patterns, rubric scoring, LLM-as-judge review, test-driven refinement, convergence checks, and iteration logging.
Guide users to review and install the external ai-ready skill from its upstream repository. Use when the user asks to install or try John Papa's ai-ready skill.
Build an automated evaluation pipeline that tests a Python LLM application end-to-end with pixie test — real code paths, real LLM calls, instrumented external data — and scores outputs with evaluators instead of assertions. Use when adding evals to a Python AI app.
Interactive task-refinement workflow that clarifies scope, deliverables, and constraints before carrying out the task. Uses Joyride input tools when available.
Generate GitHub Copilot migration instructions by comparing two project versions and extracting conventions for framework upgrades, refactoring, dependency changes, or technology migrations.
Set up a GitHub Copilot customisation starter pack for a new project based on its technology stack, including instructions, skills, agents, and optional setup workflow files.
Create and synchronise prompt-based AI agents directly within Azure AI Foundry via REST API, from a local JSON manifest. Unlike scaffolding skills that only generate local code, this skill registers agents in the Foundry service itself — making them immediately available for invocation. Use when the user asks to…
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.
Generate or edit images via OpenRouter with the Gemini 3 Pro Image model. Use for prompt-only image generation, image edits, and multi-image compositing; supports 1K/2K/4K output.
Create tldr-style summaries for GitHub Copilot customisation files, MCP server documentation, or Copilot documentation from files, URLs, or focused queries.
Identify which files or folders Copilot needs to inspect before answering a user question, including required context, helpful context, and uncertainties.
Produces a traceable acceptance test plan with individually numbered test cases. Each test case maps to a source requirement (user story, use case, or FR), specifies preconditions, steps, and expected results, and includes coverage of the happy path, edge cases, and negative scenarios.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: