Shrivu Shankar

Hiring engineers in an AI-first world requires more than traditional coding interviews. Shrivu Shankar built The Interview Box, a browser-based sandbox that lets candidates use real AI tools, work on realistic codebases, and be evaluated on how they actually build, not just how they solve isolated problems.

Workflows

11 workflows

People

Interview Box

Product Development

Prompt to Demo Portal

When you’re iterating on Abnormal’s customer-facing portal UI, review friction is real. A PR might contain the “right” code, but most reviewers still can’t easily answer the simplest question: what does it look like? The Prompt to Demo Portal is Shrivu’s quick solution to collapse the process into a single Slack prompt that generates both the change and a demo video.

Product Development

Constitutional Markdown

Shrivu Shankar built Constitutional Markdown to help Abnormal operate as an AI-initiated org, where agents do the first pass on design and implementation. The feature packages Abnormal’s architecture, security, and legal intent into a single, agent-friendly interface.

Personal Productivity

Nora Zoom Notes

Meeting notes should be immediate, readable, and useful, but most tools make them slow, gated, or easy to ignore. Shrivu Shankar built Nora Zoom Notes to eliminate friction by pairing Spinach recordings with Nora’s AI reasoning, producing polished notes and diagrams that arrive instantly and are easy to reuse anywhere.

Product Development

Sigma CLI

Sigma is widely used across Abnormal, but its UI-heavy workflows made even small analytics changes slow and painful. Shrivu Shankar built Sigma CLI to bypass the interface entirely, connecting Claude Code directly to Sigma’s underlying data and unlocking fast, AI-driven analytics.

Product Development

Nora PR Analyzer

As AI writes more of Abnormal’s code, context quality becomes the limiting factor. Shrivu Shankar built NoraPR Analyzer to close that gap by analyzing how AI-written code differs from human-written code and automatically generating the documentation needed to make AI better next time.

Product Development

Nora PR 'V2'

As Abnormal accelerates toward an AI-initiated engineering culture, tools must support fast, reliable, and scalable code generation. Shrivu Shankar redesigned NoraPR from the ground up, fixing naming confusion, speeding up CI performance, and introducing a local mode that encourages AI-driven development while still providing guardrails.

Product Development

Nora Deep Research

Nora Deep Research V3 connects unstructured data across Gong, Salesforce, Jira, and more to help teams instantly uncover insights, size product impact, and even map customer pain directly to code changes.

Product Development

Claude Code Feedback Loop

AI agents already generate a large share of Abnormal’s code, but when they hit errors in CI environments, the problems often go unnoticed. Shrivu Shankar built the Claude Code Feedback Loop, a system that pipes agent logs into an LLM, extracts failure patterns, and feeds fixes back into development.

Product Development

Nora Tech Plan

Traditional tech design docs (TDDs) were built for humans to read, not for AI to act on. Shrivu Shankar reimagines that process with Nora Tech Plan, generating documents that both capture architectural intent and serve as direct inputs for AI code generation.

Product Development

Claude Code for On-Call Ops

When a service breaks in the middle of the night, engineers often wake up to a vague page that simply says “Service is broken.” To address this, Shrivu Shankar developed Claude Code for On-Call Ops, a tool that automatically connects to CloudWatch and Prometheus, executes the correct queries, and generates a clear diagnostic report. The result: engineers can respond quickly without having to juggle multiple dashboards or query syntaxes.

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Shrivu Shankar

Workflows

Interview Box

Prompt to Demo Portal

Constitutional Markdown

Nora Zoom Notes

Sigma CLI

Nora PR Analyzer

Nora PR 'V2'

Nora Deep Research

Claude Code Feedback Loop

Nora Tech Plan

Claude Code for On-Call Ops

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