AI-Powered Security Training System

[Nanle-code]

Create an AI system that provides personalized security training for smart contract developers:

1. Personalized Learning Paths: Adapt training based on developer skill level and past mistakes
2. Vulnerability Deep Dives: Provide in-depth explanations of vulnerabilities
3. Interactive Challenges: Generate coding challenges to practice security
4. Progress Tracking: Track learning progress and identify knowledge gaps
5. Real-World Examples: Use historical hacks as teaching examples
6. Code Review Training: Practice reviewing code for security issues
7. Certification: Provide certification upon completion of training
8. Team Analytics: Provide analytics on team security knowledge

Requirements:

• Build learning management system
• Implement personalized learning algorithms
• Create interactive coding challenge platform
• Build progress tracking and analytics
• Implement integration with historical hack database
• Create code review training modules
• Build certification system
• Implement team analytics and reporting
• Create integration with development workflow
• Build gamification elements for engagement

Acceptance Criteria:

• Personalized learning paths improve knowledge retention by 40%
• Interactive challenges have completion rate >70%
• Developers show measurable improvement in code security
• Certification is recognized in the industry
• Team analytics provide actionable insights
• Integration with existing development tools
• User satisfaction score >4.5/5

Difficulty: Medium - Requires educational design expertise, ML for personalization

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Maintainer Scope Upgrade

This issue is being expanded into a substantial, production-quality ChainProof enhancement. The implementation should be designed as maintainable platform work, not as a narrow proof of concept. A successful pull request must provide a cohesive user-facing capability, typed internal APIs, robust tests, documentation, and CI-safe behavior.

Expanded Objective

Create a security training system that turns ChainProof findings into guided learning material. The feature should fit the existing monorepo architecture, reuse current scanner/report/CLI patterns where appropriate, and avoid introducing ad hoc subsystems that are difficult to test or maintain.

Required Implementation Depth

This issue is intentionally scoped to require more than 700 lines of meaningful implementation work. The line count expectation applies to purposeful source, tests, fixtures, and documentation that are necessary to deliver the feature. It must not be satisfied through generated output, lockfile churn, formatting-only changes, duplicated boilerplate, or artificial padding.

Expected work includes:

• Curriculum modules derived from vulnerability classes, fixtures, remediation examples, and quizzes.
• CLI/report exports suitable for team onboarding and post-incident learning.
• Tests for content generation boundaries, deterministic templates, and coverage of supported rules.
• Public or internal types/interfaces where they clarify behavior and reduce future integration risk.
• Failure-mode handling for invalid input, missing configuration, unavailable optional dependencies, and degraded execution paths.
• Documentation updates that explain how maintainers and users should operate the new capability in local and CI environments.

Professional Quality Bar

The implementation must be production-ready and reviewable in isolation. Contributors should include clear separation between parsing, analysis, reporting, CLI/action integration, and persistence or provider code where those concerns apply. The code should follow existing ChainProof conventions, keep behavior deterministic in tests, and avoid coupling core analysis to network-only services unless explicitly optional and mocked.

Acceptance Criteria

• The delivered PR contains more than 700 meaningful lines of implementation across source, tests, fixtures, and docs, excluding generated files and lockfile-only changes.
• The feature is integrated into the relevant package entrypoints, CLI commands, report formats, GitHub Action behavior, or documentation as appropriate for this issue.
• Unit tests cover normal operation, edge cases, invalid inputs, and at least one realistic fixture or end-to-end workflow.
• Any optional external service, model provider, database, or platform integration has deterministic mocks or fallbacks so CI does not depend on secrets or network availability.
• User-facing output is documented and stable enough for downstream automation.
• Backward compatibility is preserved unless the PR explicitly documents a migration path and the maintainer approves it.

Mandatory CI and Merge Requirements

A PR resolving this issue must not be merged until all repository CI checks pass. At minimum, reviewers should verify the following from a clean checkout:

• npm ci
• npm run lint
• npm run build --workspaces --if-present
• npm run test:ci --workspace=packages/core
• npm test --workspaces --if-present
• npm run build --workspace=packages/core && npm run docs --workspace=packages/core
• Any package-specific tests, examples, validators, or integration checks introduced by the PR

If the PR adds a GitHub Action, report format, dashboard, persistence layer, or external integration, it must also include CI coverage or a documented local verification command for that path. Known warnings are acceptable only when they are documented and do not hide failures.

Review Expectations

Reviewers should reject PRs that only stub APIs, add superficial wrappers, omit tests for critical behavior, rely on live secrets in CI, or meet the line-count target through non-functional bulk changes. The preferred solution is a focused but complete vertical slice that leaves ChainProof more reliable, easier to operate, and easier to extend.