AI coding assistant: completion, chat, agents, review, code-prompt best practices.
Before starting, we run a 1-minute tech check — microphone, ambient noise, connection. If your setup isn't good enough, the test is fully refunded.
Prove in 15 minutes that you truly know GitHub Copilot — inline completion, Chat, Agent mode, and code prompt best practices — and earn an AI-verified badge that actually means something on your resume.
The Plume GitHub Copilot badge certifies your ability to use GitHub Copilot as a genuine productivity multiplier, not just an autocomplete shortcut. The 15-minute oral exam, conducted by an AI examiner (OpenAI Realtime), probes your hands-on mastery across the full Copilot stack: inline completion, Copilot Chat with #file and #selection references, slash commands (/explain, /tests, /fix), Agent mode, Copilot Workspace, and pull request integration via Copilot Code Review. The final score is produced by Claude Opus, which reads your full transcript and assigns a 0-100 score with a certified level: Novice, Proficient, Advanced, or Expert.
What makes this badge credible is that it cannot be faked. Unlike a checkbox on LinkedIn or a two-hour online course, the AI oral forces you to narrate real situations: a hallucinated API you caught before it shipped, a 500-line refactor handled autonomously by Agent mode, a deliberate decision to disable Copilot on a sensitive file. The model evaluates technical depth, critical thinking about AI-generated suggestions, and your ability to embed Copilot in a professional workflow alongside CI pipelines, test suites, and code review processes.
This badge is built for developers who use Copilot daily and want proof, for tech leads guiding their team's AI adoption, for engineers switching roles who need a credible differentiator, and for anyone applying to a position where AI coding assistant fluency is now an explicit hiring criterion.
Here are the concrete dimensions the AI examines during the 15-minute oral.
Accepting, editing, or rejecting Copilot suggestions with genuine discernment, understanding why a suggestion works or doesn't, and shaping the file context to steer completions toward production-quality code.
Structuring effective prompts in Copilot Chat using #file, #selection, #codebase references, slash commands /explain, /tests, /fix, and directive inline comments to reliably get high-quality, context-aware code.
Delegating multi-file tasks to Agent mode, configuring custom instructions via copilot-instructions.md, and rigorously auditing autonomous changes before committing them to the codebase.
Using Copilot Code Review in GitHub pull requests, interpreting AI-generated review comments, and distinguishing actionable suggestions from false positives on real codebases.
Spotting hallucinated APIs, incorrect library versions, subtle logic bugs, and outdated patterns in Copilot's output, and applying a systematic validation process before accepting any suggestion.
Fitting Copilot into a complete engineering workflow: unit test generation, inline documentation, code migration, and articulation with CI pipelines and static analysis tools.
Knowing when to disable Copilot on sensitive files, understanding snippet retention policies, and navigating open-source licensing concerns (filtered vs. unfiltered code) in a professional context.
Comparing Copilot to Cursor, Claude Code, Cody, or Windsurf with precise technical arguments: underlying models, IDE integration depth, codebase context window, and multi-file agent performance.
Final scoring is performed by Claude (Anthropic), which reads back the full transcript and applies this weighted criteria grid.
Ability to reference real Copilot features (slash commands, #file, copilot-instructions.md, Agent mode), describe concrete use cases with correct terminology, and distinguish between available modes and versions.
Quality of reflection on Copilot's suggestions: hallucination detection, systematic validation habits, understanding of model limitations, and a clear personal policy for verifying output before shipping.
Command of prompting techniques in Copilot Chat and inline: use of contextual references, slash commands, directive comments, and the ability to adapt prompt structure to task complexity.
Ability to articulate Copilot with real tools (GitHub Actions, test suites, code review, CLI) and describe how it concretely changes the speed and quality of team-level engineering work.
Understanding of licensing implications, code confidentiality, company policies on AI coding tools, and the ability to adapt personal practices accordingly in a professional setting.
A Plume session takes about 20 minutes, from tech check to badge delivery.
The AI confirms your mic is working, your environment is quiet, and you're ready to go. No GitHub account or live coding needed during the exam — you talk about your practice, you don't code on the spot.
You briefly introduce yourself and describe how you use Copilot: your usual stack, your main IDE (VS Code, JetBrains, Neovim...), and how frequently you rely on it day to day.
The AI examiner digs into your real-world usage: a hallucination you caught in time, a multi-file refactor handled with Agent mode, your prompt strategy in Copilot Chat, your view on Agent mode vs. inline completion, and how Copilot fits into your PR workflow.
The AI asks you to compare Copilot to at least one competitor (Cursor, Claude Code, Cody...) and explain when you deliberately choose not to use Copilot or disable it on certain files.
Claude Opus analyzes your full transcript and produces a 0-100 score, a certified level, and a detailed report. Your GitHub Copilot badge is available with a shareable URL as soon as the analysis is complete.
Your score out of 100 translates into a level a recruiter can grasp at a glance.
You use Copilot's inline completion occasionally for simple boilerplate, but you accept suggestions without systematically reviewing them. You haven't explored Copilot Chat, slash commands, or Agent mode, and you don't have a defined prompt strategy.
You use both inline completion and Copilot Chat daily in your IDE, apply basic slash commands (/explain, /fix), and leverage #file or #selection references. You review suggestions before accepting them and are aware of the main pitfalls: hallucinated APIs, stale code patterns.
You use Agent mode for multi-file refactors and test generation, craft structured prompts with copilot-instructions.md and context comments, and integrate Copilot into your CI/CD workflow and PR reviews via Copilot Code Review. You have a clear policy for sensitive files and licensing edge cases.
You command the full Copilot feature set — autonomous Agent mode, Copilot Workspace, CLI, API integration — and can guide a team through adoption. You compare Copilot to alternatives with precise technical arguments, manage licensing and confidentiality tradeoffs, and actively shape Copilot best practices across your organization.
No degree or years of experience required to take the badge. Here are the profiles it makes the most sense for.
You want a concrete, verifiable proof of your Copilot skills for your resume, GitHub profile, or technical interviews — not just a line that says 'familiar with GitHub Copilot' that anyone can write.
You're guiding your team's AI tool adoption and want to validate your expertise, anchor your recommendations with a third-party score, and become the go-to Copilot reference in your squad.
More and more job listings explicitly call out AI coding assistant fluency. The badge gives you a verified score and level that stands out in a sea of self-declared skills on LinkedIn profiles.
You adopted Copilot early and want to show you use it thoughtfully — not as a crutch — to stand out with recruiters who worry about developers becoming blindly dependent on AI-generated code.
Copilot fluency helps you ramp up faster on a new stack. The badge proves you use modern AI tools professionally — a strong argument to offset fewer years of direct experience in a given language or framework.
Where and how your GitHub Copilot badge will help you day to day.
You're interviewing for a senior backend role at a startup that's fully bought into AI-augmented engineering. A GitHub Copilot badge with a score of 84/100 and an Advanced level on your resume immediately signals you're a serious user, not just someone who has it installed.
During a performance review, you show your manager that you use Copilot expertly to generate tests, refactor legacy code, and speed up PR cycles. The badge backs your case with an objective score from a third-party AI, not your own self-assessment.
You're proposing to become your team's Copilot champion. The Expert badge legitimizes the role and helps you convince your CTO to invest in Copilot Enterprise licenses for the whole engineering org.
As a freelancer, you include your badge URL in your proposal to show clients you work with best-in-class tools and that your hourly rate reflects AI-optimized output — not just raw hours on a keyboard.
At the end of a dev bootcamp, you take the badge to prove your Copilot adoption is professional and intentional — a meaningful differentiator with recruiters who are skeptical of developers who rely too heavily on AI-generated code without understanding it.
An engineering manager has the whole team take the GitHub Copilot badge to map out actual skill levels, identify where to focus training, and drive consistent adoption of Copilot best practices across the workflow.
A few minutes to check you have everything you need.
At the end of your session you don't just get a score — here's everything that awaits you.
You get a precise score out of 100 and an official level (Novice, Proficient, Advanced, or Expert) that reflects your real mastery of Copilot — completion, Chat, Agent mode, and workflow integration.
Claude Opus produces a point-by-point breakdown of your strengths and improvement areas: prompt engineering, hallucination handling, Agent mode usage, CI/CD integration. Genuine, actionable feedback.
The audio of your 15-minute session is stored securely and stays private. You can replay it to prep for technical interviews or track your own progress over time.
Your GitHub Copilot badge comes with a permanent URL you can drop into your LinkedIn profile, resume, GitHub bio, or freelance proposal — verifiable in one click by any recruiter or hiring manager.
Discover related skills you can validate with Plume.
A 15-min oral exam with an AI, a shareable badge for your recruiters.
Choose this badge · €19.99
