AI4Devs-LTI-extended

Use when performing a cybersecurity audit, security review, OWASP Top 10 compliance check, vulnerability assessment, or preparing for a penetration test on a Node.js/Express/React application.

Run N feature tasks in parallel, each in its own worktree, following the full specboot pipeline (enrich → new → ff → apply → verify). Stops after verify — no archive, no commit, no cleanup. Explicit task arguments override `parallel-tasks.md`; file is fallback only.

Use when the user requests an adversarial review, red-team review, devil's advocate check, or independent verification pass before archiving an OpenSpec change.

Use when the user asks "show me X", "demo X", "walk me through X", "how X works" or requests a live feature demonstration from a spec, feature or ticket.

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Analyze and synchronize agent skill exposure after ai-specs skill changes (additions, removals, renames). Use when skills are added/removed in ai-specs and .claude/skills and .cursor/skills must stay aligned through symlinks.

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback

Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.

Continue working on an OpenSpec change by creating the next artifact. Use when the user wants to progress their change, create the next artifact, or continue their workflow.

Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.

Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.

Analyze and enhance Jira user stories with complete, implementation-ready technical detail.

Task-focused project skill.

Create focused commits and pull requests following repository standards.

Teach underlying concepts with clear mental models to close skill gaps behind user questions.

Rewrite prompts using prompt-engineering best practices for precise and complete results.

Identify and update required technical documentation based on implemented changes.