implementation

Technical Interview Agent Prompt

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Global Tech Talent Evaluator Agent

Format

Text-first

Lines

Sections

Linked challenge

Global Tech Talent Evaluator Agent

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Using the Claude Agents SDK, create an agent specialized in interviewing Distributed Systems engineers. The agent must use Claude Sonnet 4.6.6 and its extended thinking capability to drill down into candidate answers about CAP theorem and raft consensus.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

Variables

Lists

Code blocks

Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Global Tech Talent Evaluator Agent

With software engineering job openings surging by 30% in 2026, tech firms need more efficient ways to screen global talent while remaining compliant with UK and US transparency laws. You will build a technical recruitment agent using the Claude Agents SDK that conducts simulated voice screenings and technical interviews. This agent will utilize Claude Sonnet 4.6.6 for its high-reasoning capabilities and interaction design. To ensure transparency, you will integrate Alibi Explain to generate post-hoc explanations for why specific candidates were advanced to the next round. The agent will use Retell AI to handle real-time voice interactions with candidates, while BoTorch is employed to optimize the scheduling and ranking of candidates based on multidimensional skill sets. You will also use Llama 3.3 70B as a secondary evaluator to provide a 'second opinion' and reduce model-specific bias.

Open challenge

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To establish a clear understanding of the problem space and define comprehensive requirements for the AI solution.