arcanum-artifex

Build PowerPoint (.pptx) slide decks in a Nord-themed house style — Nord palette with a dark theme (default) and a light theme, Consolas mono figures, kicker + title + standfirst anatomy, white-on-dark cards with coloured accent bars, green takeaway banners, facilitation-grade speaker notes. Use this skill whenever the user asks for a deck, slides, a presentation, an onboarding session, an inspiration session, a workshop deck, a kickoff deck, or any .pptx output.

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.

Assess tool-using and multi-agent AI systems against OWASP ASI-style controls, mapping evidence to prompt injection, tool governance, agency, escalation, trust boundaries, audit, identity, policy integrity, supply chain, and behavioural monitoring risks.

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.

Beginner-friendly interactive tutorial for GitHub Copilot CLI concepts, slash commands, permissions, file context, planning, and custom instructions.

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 create agents in Foundry, sync, deploy, register, or push agents to Foundry, update agent instructions, or scaffold the manifest and sync script for a new repository. Triggers: 'create agent in foundry', 'sync foundry agents', 'deploy agents to foundry', 'register agents in foundry', 'push agents', 'create foundry agent manifest', 'scaffold agent sync'.

Generate a complete MCP server implementation optimised for Copilot Studio integration with proper schema constraints and streamable HTTP support

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.

Plain-English response style for non-technical Copilot CLI users. Explains approval prompts, errors, command output, and technical choices with clear risk indicators.

Guide users through creating high-quality GitHub Copilot prompt files with clear structure, appropriate tools, validation criteria, and maintainable instructions.

Transform lessons learned into domain-organised memory instructions for global or workspace scope. Syntax: `/remember [>domain [scope]] lesson clue`.

Create tldr-style summaries for GitHub Copilot customisation files, MCP server documentation, or Copilot documentation from files, URLs, or focused queries.

Socratic mentoring for junior developers and AI newcomers. Guides through questions, never answers. Triggers: "help me understand", "explain this code", "I'm stuck", "Im stuck", "I'm confused", "Im confused", "I don't understand", "I dont understand", "can you teach me", "teach me", "mentor me", "guide me", "what does this error mean", "why doesn't this work", "why does not this work", "I'm a beginner", "Im a beginner", "I'm learning", "Im learning", "I'm new to this", "Im new to this", "walk me through", "how does this work", "what's wrong with my code", "what's wrong", "can you break this down", "ELI5", "step by step", "where do I start", "what am I missing", "newbie here", "junior dev", "first time using", "how do I", "what is", "is this right", "not sure", "need help", "struggling", "show me", "help me debug", "best practice", "too complex", "overwhelmed", "lost", "debug this", "/socratic", "/hint", "/concept", "/pseudocode". Progressive clue systems, teaching techniques, and success metrics.

Draft performance reviews, self-assessments, peer reviews, and upward feedback in your own voice. Analyses your contributions, emails, and meeting history via WorkIQ, then produces honest, impact-focused drafts using the STAR format. USE FOR: write my performance review, draft self-assessment, peer review, 360 feedback, annual review, mid-year review, upward feedback, write review for colleague, performance appraisal.

Ranks any list of impediments and their countermeasures using a value-stream scoring model (ROI, Cost to Implement, Ease of Deployment, Risk Factor) and a fixed prioritisation formula. Use when someone asks to prioritise, rank, sequence, or triage impediments, countermeasures, remediation items, risks, findings, gaps, action items, or backlog entries; or mentions value-stream prioritisation, A3 / lean countermeasure ranking, ROI vs. effort scoring, or building a remediation / improvement backlog. Works with GHQR findings, audit results, retrospective action items, risk registers, architecture review gaps, or any free-form `{impediment, countermeasure}` list.

Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.

Produce Philippe Kruchten's 4+1 architectural view model for a software system, with rendered diagrams (Mermaid primary, PlantUML fallback for deployment) AND Miro RISEN prompts for each view. Use this skill whenever the user mentions "4+1", "architecture views", "architectural documentation", "logical view", "process view", "development view", "physical view", or "deployment view" — and also whenever the user asks to document system architecture, produce architecture blueprints, generate architecture diagrams, or prepare architecture content for review with non-developer stakeholders, even if they don't say "4+1" explicitly. Works with zero-input (user gives a paragraph, skill drafts a full architecture), interview mode (user has detailed context), or partial mode (user wants only one or two views). Flexes notation to audience — UML-flavoured for dev-only audiences, BPMN-style swimlanes for cross-functional audiences, simplified C4 for executives.

Create an Architectural Decision Record (ADR) document for AI-optimised decision documentation.

Comprehensive technology stack blueprint generator that analyses codebases to create detailed architectural documentation. Automatically detects technology stacks, programming languages, and implementation patterns across multiple platforms (.NET, Java, JavaScript, React, Python). Generates configurable blueprints with version information, licensing details, usage patterns, coding conventions, and visual diagrams. Provides implementation-ready templates and maintains architectural consistency for guided development.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

Design and build production-grade dashboards and infographics with Dash and Plotly Python: layout strategy, colour semantics, accessibility, and pre-ship validation. Use when creating or beautifying a dashboard, KPI panel, or data infographic.

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analysing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.

Low-level plotting library for full customisation. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualisation.

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Analyse chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency

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Comprehensive toolkit for creating, analysing, and visualising complex networks and graphs in Python. Use when working with network/graph data structures, analysing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualising network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.

[TODO] Define the specific workflow this skill standardises, including default libraries, quality checks, and expected deliverables.