If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/Ebazhanov/linkedin-skill-assessments-quizzes shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

WAIT — Mixed signals — read the receipts

• Last commit 1w ago
• 22+ active contributors
• Distributed ownership (top contributor 23% of recent commits)
• AGPL-3.0 licensed
• CI configured
• ⚠ AGPL-3.0 is copyleft — check downstream compatibility
• ⚠ No test directory detected

_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live Ebazhanov/linkedin-skill-assessments-quizzes repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/Ebazhanov/linkedin-skill-assessments-quizzes.

What it runs against: a local clone of Ebazhanov/linkedin-skill-assessments-quizzes — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in Ebazhanov/linkedin-skill-assessments-quizzes | Confirms the artifact applies here, not a fork | | 2 | License is still AGPL-3.0 | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 40 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of Ebazhanov/linkedin-skill-assessments-quizzes. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/Ebazhanov/linkedin-skill-assessments-quizzes.git
#   cd linkedin-skill-assessments-quizzes
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of Ebazhanov/linkedin-skill-assessments-quizzes and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "Ebazhanov/linkedin-skill-assessments-quizzes(\\.git)?\\b" \\
  && ok "origin remote is Ebazhanov/linkedin-skill-assessments-quizzes" \\
  || miss "origin remote is not Ebazhanov/linkedin-skill-assessments-quizzes (artifact may be from a fork)"

# 2. License matches what RepoPilot saw
(grep -qiE "^(AGPL-3\\.0)" LICENSE 2>/dev/null \\
   || grep -qiE "\"license\"\\s*:\\s*\"AGPL-3\\.0\"" package.json 2>/dev/null) \\
  && ok "license is AGPL-3.0" \\
  || miss "license drift — was AGPL-3.0 at generation time"

# 3. Default branch
git rev-parse --verify main >/dev/null 2>&1 \\
  && ok "default branch main exists" \\
  || miss "default branch main no longer exists"

# 4. Critical files exist
test -f "README.md" \\
  && ok "README.md" \\
  || miss "missing critical file: README.md"
test -f "CONTRIBUTING.md" \\
  && ok "CONTRIBUTING.md" \\
  || miss "missing critical file: CONTRIBUTING.md"
test -f ".prettierrc" \\
  && ok ".prettierrc" \\
  || miss "missing critical file: .prettierrc"
test -f "package.json" \\
  && ok "package.json" \\
  || miss "missing critical file: package.json"
test -f ".github/workflows/formatting.yml" \\
  && ok ".github/workflows/formatting.yml" \\
  || miss "missing critical file: .github/workflows/formatting.yml"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 40 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~10d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/Ebazhanov/linkedin-skill-assessments-quizzes"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

A comprehensive crowdsourced reference of 1,500+ LinkedIn Skill Assessment quiz questions and answers across 13+ domains (AWS Lambda, REST APIs, JavaScript, React, Git, HTML, jQuery, MongoDB, Java, Go, Python, Machine Learning, PowerPoint). The repository archives official LinkedIn assessment content as markdown files organized by skill category, with translations in Spanish, French, Italian, Chinese, German, Hindi, and Vietnamese. It serves as a study guide and knowledge reference since LinkedIn retired the assessments in December 2023. Flat monorepo structure: root directory contains skill subdirectories (e.g., accounting/, adobe-acrobat/, adobe-ai/, adobe-illustrator/), each containing *-quiz.md files (base English version) and language variants like *-quiz-es.md, *-quiz-fr.md, plus an images/ folder for question screenshots. Top-level config files (.prettierrc, package.json) enforce formatting. No src/ or build/ directories—content is served directly as markdown to web apps or parsed by terminal tools.

Job seekers and professionals preparing for LinkedIn Skill Assessments or refreshing knowledge in technical domains (backend/frontend developers, cloud engineers, data scientists). Contributors are multilingual community members adding translations and verified answers. Internal users leverage this via playground web apps (MD2Practice, Skill Assessments Quizzes web app) and terminal tools (Kodyfire).

Highly mature and community-driven: 1,586 contributors (badge indicates substantial engagement), organized directory structure with 60+ skill categories each containing 4+ language variants, active CI/CD pipeline (.github/workflows/formatting.yml enforces Prettier formatting), 1.4K+ stars visible from repo description. The project is stable and well-maintained despite being a reference archive rather than active software—commit recency and issue management suggest ongoing stewardship.

Minimal technical risk: zero npm dependencies (only Prettier dev tool for formatting), markdown-based content removes runtime failures. Main risks are content accuracy (community-sourced answers may have errors without peer review) and potential LinkedIn legal action if assessments are considered intellectual property. Single maintainer (@ebazhanov as owner, @monkrus as admin) creates knowledge-bus risk if both become unavailable, though 1,586 contributors lower this. No breaking changes are possible given the static content model.

Active maintenance focused on formatting consistency and multilingual content expansion. The .github/workflows/formatting.yml CI pipeline enforces Prettier rules on all markdown files. Recent work includes Hacktoberfest 2025 participation (rules linked in README), community translation additions (Spanish, French, Italian variants visible across all skill directories), and promotion of three playground tools (MD2Practice web app, Skill Assessments Quizzes web app, Kodyfire terminal tool). Discord and Telegram channels enable live contributor coordination.

git clone https://github.com/Ebazhanov/linkedin-skill-assessments-quizzes.git
cd linkedin-skill-assessments-quizzes
npm install
npm run format  # Runs Prettier to validate/fix markdown formatting

No build step required; content is directly readable markdown. To contribute, read CONTRIBUTING.md and CODE_OF_CONDUCT.md, then submit a pull request with your answers (include explanation or reference link per guidelines).

Daily commands: This repository is not a runnable application. Instead:

1. Browse locally: cat accounting/accounting-quiz.md or open any *-quiz.md file in your editor.
2. Format markdown: npm run format (runs Prettier with .prettierrc config).
3. Study online: Use the three official playgrounds: MD2Practice, Skill Assessments Quizzes web app, or Kodyfire CLI.

• README.md — Primary entry point documenting the repository's purpose as a comprehensive reference for LinkedIn skill assessment answers across 13+ domains.
• CONTRIBUTING.md — Essential guide for all contributors on how to maintain consistency, add new quizzes, and follow the repository's conventions.
• .prettierrc — Enforces markdown formatting standards across all 600 quiz files to ensure consistency and readability.
• package.json — Defines the project's build scripts and dependencies; the format script maintains code quality across all quiz markdown files.
• .github/workflows/formatting.yml — CI/CD automation that validates markdown formatting on all pull requests to prevent inconsistent quiz file structure.
• .all-contributorsrc — Configuration for the all-contributors tool that recognizes and credits the 1586+ community contributors to the project.

Add a New Skill Assessment Domain

1. Create a new directory with the skill name in kebab-case (e.g., python-fundamentals) (python-fundamentals/)
2. Create the base English quiz file following the naming convention (python-fundamentals/python-fundamentals-quiz.md)
3. Structure the quiz with numbered questions and answer options in markdown format, matching existing quiz patterns from adobe-photoshop/adobe-photoshop-quiz.md (python-fundamentals/python-fundamentals-quiz.md)
4. Create an images subdirectory if the assessment requires visual references (python-fundamentals/images/)
5. Create multilingual variants (es, fr, it, ch, vi) by copying and translating the base quiz file (python-fundamentals/python-fundamentals-quiz-es.md)
6. Run the format script to ensure consistency with prettier rules (package.json)
7. Submit a pull request and wait for admin review per CONTRIBUTING.md guidelines (CONTRIBUTING.md)

Add Translations for an Existing Assessment

1. Open the English quiz file for the domain (e.g., accounting/accounting-quiz.md) (accounting/accounting-quiz.md)
2. Create a new file with the language suffix (-es, -fr, -it, -ch, -vi) in the same directory (accounting/accounting-quiz-es.md)
3. Translate all questions, answer options, and descriptions while maintaining the markdown structure and image references (accounting/accounting-quiz-es.md)
4. Run prettier format to validate markdown consistency (.prettierrc)
5. Submit a pull request; GitHub Actions will validate formatting automatically (.github/workflows/formatting.yml)

Update Quiz Content with Visual Questions

1. Prepare image files (PNG or JPG) for the assessment questions (adobe-photoshop/images/)
2. Add images to the domain's images subdirectory following the naming convention (e.g., 001.png, 002.png) (adobe-photoshop/images/001.png)
3. Update the quiz markdown file to reference images using markdown syntax and number questions accordingly (adobe-photoshop/adobe-photoshop-quiz.md)
4. Ensure all markdown formatting passes prettier validation by running the format script (package.json)
5. Create or update corresponding translated quiz files with the same image references (adobe-photoshop/adobe-photoshop-quiz-es.md)

Maintain Code Quality and Consistency

1. Review the prettier configuration to understand formatting rules applied across all files (.prettierrc)
2. Run the npm format script locally to auto-fix any markdown inconsistencies before committing (package.json)
3. Verify that your changes pass the GitHub Actions formatting workflow (.github/workflows/formatting.yml)
4. Follow contribution guidelines for PR submission and review process (CONTRIBUTING.md)

• Markdown (.md files) — Lightweight, version-control-friendly format for storing Q&A content; renders directly on GitHub; easy for distributed contributors to edit.
• Prettier (npm dependency) — Enforces consistent markdown formatting across 600+ files and multiple languages; prevents formatting drift in pull requests.
• GitHub Actions (CI/CD) — Automated quality checks on every PR; ensures formatting compliance without manual review overhead.
• Git + GitHub — Distributed version control for 1586+ contributors; pull request workflow enables community collaboration at scale.
• Jekyll (_config.yml) — Converts repository markdown into static website for GitHub Pages; minimal configuration for documentation hosting.

• Store quiz content as markdown files rather than a database

  • Why: Enables decentralized contributions, version history tracking, and easy offline access; community-driven updates without server infrastructure.
  • Consequence: Requires manual file organization and no real-time synchronization; scaling to thousands of quizzes depends on file system organization rather than queries.

• Multilingual support via separate -es, -fr, -it, -ch, -vi files per domain

  • Why: Simplifies contribution workflow: translators fork and edit individual files; no complex i18n framework needed.
  • Consequence: File count increases 6x per domain (English + 5 languages); translation consistency must be manually verified; no shared terminology database.

• Embed images in domain subdirectories instead of external CDN

  • Why: Images stay versioned with content; contributors can reference images locally; no external dependencies.
  • Consequence: Repository grows larger; images not optimized for web delivery; no caching layer for static assets.

• Minimal build process (only Prettier formatting)

  • Why: Reduces barrier to contribution; most contributors only need a text editor; fast feedback loop for PRs.
  • Consequence: No linting for content accuracy, no spell-check, no automated testing of Q&A validity; relies entirely on human review.

• Does not provide a real-time quiz-taking platform or LMS integration.
• Does not validate answer correctness; content is reference material only.
• Does not handle user authentication, progress tracking, or certification.
• Not maintained or officially endorsed by LinkedIn.
• Does not generate new quiz content; repository is community-curated snapshots of historical LinkedIn assessments (assessments deprecated as of December 2023).

No environment variables or external services required—this is a static markdown repo. Key gotchas: (1) Prettier formatting is enforced by CI—commits that don't pass .prettierrc rules will fail the GitHub Actions pipeline; run npm run format locally before pushing. (2) Translation completeness varies across skills—some have all 7 language variants, others only English; no bot auto-translates, so incomplete translations will remain until community contributors fill gaps. (3) Image paths are relative—quiz images in {skill}/images/ are referenced via ./images/001.jpg; moving images or restructuring directories will break links. (4) Content accuracy is crowd-verified—no official LinkedIn source of truth exists post-December 2023, so wrong answers may persist until corrected by community. (5) Playground tools are external—this repo is a content source only; the actual quiz UI/interaction lives in separate linkedin-faq repos.

• Crowdsourced Verification — This repo relies entirely on 1,586 community members to verify, translate, and maintain quiz accuracy without official LinkedIn sources; understanding how consensus emerges in decentralized knowledge bases helps you evaluate answer confidence and spot gaps.
• Multilingual Content Localization — The repo maintains 7 language variants (EN, ES, FR, IT, DE, HI, VI) of the same quizzes through parallel markdown files; this reveals patterns in technical terminology translation and the challenge of cultural/regional assessment accuracy.
• Markdown-as-Database — Quiz content is stored as flat markdown files parsed by external tools (not a SQL/NoSQL database); this no-backend design reduces hosting cost but requires disciplined formatting (hence the Prettier CI enforcement) to enable reliable parsing.
• Linked Data & Reference Annotations — Answers in this repo are expected to include external reference links (AWS docs, MDN, Stack Overflow) rather than standalone explanations; this makes the repo a curated index of distributed knowledge rather than an authoritative source.
• Static Site Generation & GitHub Pages — The repository uses Jekyll (_config.yml) to serve markdown as static HTML on GitHub Pages; understanding this pattern shows how low-cost, high-availability knowledge bases are built without server-side infrastructure.
• Assessment Archiving & Reverse Engineering — Since LinkedIn sunset skill assessments in Dec 2023, this repo documents a deprecated but historically important credential system through community reconstruction; it illustrates how institutional knowledge can be preserved when original sources disappear.
• Continuous Linting for Content Consistency — The GitHub Actions formatting.yml pipeline enforces Prettier rules on markdown; this demonstrates how automation ensures readability and parsability in content repositories, preventing drift in structure that breaks downstream tools.

• linkedin-faq/md2practice — Official playground web app that parses markdown quiz files from this repo and presents them as interactive practice tests with timer and scoring.
• linkedin-faq/skill-assessments-quizzes — Companion web UI (also official playground) that visualizes quizzes from this repository with a modern interface for studying and reviewing answers.
• linkedin-faq/linkedin-quizzes-kodyfire — Terminal-based CLI tool that generates interactive quiz sessions from this repo's markdown content for developers who prefer command-line study.
• Ebazhanov/linkedin-skill-assessments-quizzes-node — Parallel Node.js/TypeScript-based assessment question database; an alternative structured format for the same quiz content, useful for programmatic access.
• ossu/computer-science — Open-source computer science curriculum that complements this repo by providing foundational learning paths before attempting skill assessments in algorithms, data structures, and systems design.

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Add automated validation workflow for quiz markdown files

The repo contains 100+ markdown quiz files across different languages and topics, but there's no CI workflow to validate content structure (e.g., question/answer format consistency, image references, language-specific formatting). This prevents regressions and ensures quality as contributors add new quizzes.

• [ ] Create .github/workflows/validate-quizzes.yml with a Node.js action
• [ ] Write validation script (e.g., scripts/validate-quiz-structure.js) to check: markdown headings hierarchy, image file existence in /images dirs, answer markers consistency across language variants
• [ ] Test against existing quiz files in accounting/, adobe-acrobat/, adobe-ai/, adobe-illustrator/, adobe-in-design/ directories
• [ ] Document validation rules in CONTRIBUTING.md

Create a quiz template and generator for new skill assessments

Currently, contributors manually create quiz files for each language variant (base, -es, -fr, -it, -ch, -vi). A template + generator script would reduce errors, ensure consistency, and speed up adding new skills. The repo already has 50+ skills with repetitive file structure patterns.

• [ ] Create scripts/quiz-template.md as a standardized template with sections, question format, and metadata comments
• [ ] Write scripts/generate-quiz.js that accepts skill name and language codes, creating all variant files with consistent structure
• [ ] Update CONTRIBUTING.md with instructions: 'Run npm run generate-quiz -- --skill=new-skill --languages=en,es,fr,it'
• [ ] Test generator by creating a new sample skill (e.g., power-shell or figma)

Add a data validation and index file for quiz metadata and statistics

The repo has no centralized way to track quiz counts by skill, language coverage, or image completeness. A machine-readable index (JSON or YAML) would help contributors understand coverage gaps and maintainers assess project health. The README mentions 13+ skills but lacks comprehensive metrics.

• [ ] Create scripts/generate-quiz-index.js that scans all skill directories and builds a JSON manifest with: skill name, available languages, question count, image references, last updated date
• [ ] Output to quiz-index.json at repo root with structure: { skills: { 'javascript': { languages: ['en', 'es'], questions: 45, images: 20 }, ... } }
• [ ] Add 'npm run build-index' script to package.json; integrate into formatting.yml workflow to auto-update on PRs
• [ ] Link the generated statistics in README.md (e.g., table showing '98 skills × 6 languages = 588 total quiz files')

• Add missing image references to JavaScript quiz: The file javascript/javascript-quiz.md likely contains [Image not provided] placeholders or broken image paths (e.g., Q1.png referenced but not in javascript/images/); scan the markdown, find broken links, then either extract images from the LinkedIn archive or add placeholder alt text with a GitHub issue reference for future contributors to fill.
• Complete Spanish translations for Python assessments: python/python-quiz-es.md may be incomplete or missing entirely while python/python-quiz.md has 100+ questions; copy the English quiz structure, translate using consistent terminology (e.g., 'decorador' for 'decorator'), and submit a PR with Grammarly-checked content—this unlocks Spanish-speaking learners.
• Add reference links to AWS Lambda answers: Many answers in aws-lambda/aws-lambda-quiz.md lack explanations or external doc references (e.g., 'Correct answer: A' with no 'why'); audit the file, add AWS documentation links (e.g., 'https://docs.aws.amazon.com/lambda/latest/dg/...') or StackOverflow references to the highest-voted questions, and create a PR to improve learning value.

This repository is a educational/reference content repository with minimal security risk. It contains only markdown files, images, and configuration for GitHub workflows with a single dev dependency (Prettier). No hardcoded secrets, database connections, API keys, or sensitive code patterns detected. Primary recommendations focus on dependency management best practices (lock files), GitHub security features, and ensuring markdown rendering safety if content is dynamically processed. The low complexity and lack of executable/sensitive code components result in a strong security posture overall.

• Low · Outdated Prettier Dependency — package.json - devDependencies. The package.json uses prettier ^3.6.2, which allows patch and minor version updates. While prettier is a dev dependency with low risk, it should be regularly updated to receive security patches and bug fixes. Fix: Regularly update prettier to the latest stable version using 'npm update prettier' or specify an exact version if reproducibility is critical.
• Low · No Lock File Detected — Root directory (package.json exists but no lock file provided). No package-lock.json or yarn.lock file is visible in the provided file structure. This could lead to non-deterministic builds if dependencies are updated unexpectedly. Fix: Ensure package-lock.json is committed to the repository to guarantee consistent dependency versions across environments.
• Low · Missing Security Headers in GitHub Configuration — .github/workflows/formatting.yml and GitHub configuration. The GitHub workflows and configuration files do not explicitly show security-focused configurations such as branch protection rules, code scanning, or dependency checking. Fix: Implement GitHub security features: enable branch protection, add GitHub Advanced Security scanning (Dependabot, CodeQL), and require status checks before merge.
• Low · Lack of Input Validation in Markdown Files — All .md files (accounting/, adobe-acrobat/, adobe-ai/, etc.). The repository contains numerous markdown files with quiz content. While markdown is generally safe, if any processing tool dynamically renders this content without sanitization, XSS could occur. Fix: If markdown content is rendered as HTML, use a markdown parser with sanitization (e.g., markdown-it with xss prevention plugin) and never use dangerouslySetInnerHTML equivalents.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.