Claude Skills by MarieLynneBlock

Validate YAML frontmatter of Copilot customisation assets in this lab (SKILL.md, WORKFLOW.md, *.agent.md, *.instructions.md, *.prompt.md). Use when adding, reviewing, or fixing an asset's frontmatter, or when asked to check whether an asset follows repository conventions.

Check that a Copilot customisation asset (skill, workflow, agent, instruction, or prompt folder) stays standalone and copyable, with no runtime dependency on paths outside its own folder. Use when packaging an asset for reuse, reviewing whether an asset can be copied out of this repo, or investigating why a copied asset breaks elsewhere.

Audit Markdown links, images, and local anchors for broken relative references. Use when reviewing documentation changes, moving or renaming assets, updating indexes, or investigating broken links.

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.

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.

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: 'creat...

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 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.

Micro-skill that reminds the agent to use an available REPL or live runtime as the source of truth during interactive programming tasks.

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.

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.

Provides a repeatable, evidence-based framework for reviewing software architectures.

Provides a repeatable framework for identifying and prioritising gaps between where things are now and where they need to be.

Generates Miro board content in two modes:

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"...

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.

Produces a structured process map with swim lanes, task nodes, decision diamonds, and handoff arrows. Output is in **Mermaid** flowchart notation (renderable in GitHub, VS Code, Confluence, and Miro) or **BPMN-lite** text when a formal notation is required.

Prompt for creating an Epic Product Requirements Document (PRD) for a new epic. This PRD will be used as input for generating a technical architecture specification.

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 back...

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

Generates a Business Requirements Document (BRD) or Functional Requirements Specification (FRS) skeleton populated from the user's input. It structures requirements with unique IDs, priority, source, and acceptance notes — making them traceable and reviewable.

Provides a structured framework for risk identification and assessment.

Produces a stakeholder map structured around the classic influence × interest grid (also called a power/interest matrix). It classifies each stakeholder, recommends an engagement strategy per quadrant, and surfaces alignment risks — stakeholders who are high-influence but opposed or unengaged.

Provides a structured framework for comparing options under uncertainty. It surfaces criteria, weights them by stated priorities, scores each option, and produces a transparent recommendation — including the conditions under which a different option would be the right call.

Produces a complete use case following the Cockburn/RUP format. A use case describes a goal-directed interaction between an actor and the system — capturing not just the happy path, but the extension flows (error, alternative, and exception paths) that user stories typically omit.

Produces a complete epic definition following Atlassian agile guidance. An epic captures a large initiative that is too big for a single sprint and must be decomposed into user stories.

Produces a complete, ready-to-groom user story following the standard Atlassian/agile format. It applies the INVEST criteria and the 3 Cs framework (Card, Conversation, Confirmation) to ensure each story is well-scoped, valuable, and testable — not just syntactically correct.

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 arc...

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...

Use when calculating, validating, interpreting, or improving DORA software delivery performance metrics from GitHub, GitLab, Jira, incident-management, deployment, or Copilot usage data.

Use when building, reviewing, or debugging Plotly Dash apps: laying out pages with dash.html/dash.dcc, wiring callbacks (Input/Output/State, chained callbacks, pattern-matching ALL/MATCH, duplicate outputs), sharing data safely between callbacks, structuring multi-page apps with Dash Pages, or diagnosing slow, stuck, or inconsistent callback behaviour. Produces a stateless callback graph, a data-sharing strategy appropriate to payload size and concurrency, and a test/deployment plan. Dash app...

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. For callback wiring, data-sharing strategy, or Dash app architecture rather than visual design, see the `dash` skill instead.

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 scien...