implementation

Google ADK Agent Setup

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

Linked challenge: Protein Functional Annotation Pipeline

Format

Text-first

Lines

Sections

Linked challenge

Protein Functional Annotation Pipeline

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Initialize a Google ADK agent with Gemini. Define a 'SequenceAlignment' tool that triggers a Modal function. The prompt should instruct the agent to use 'uniprotkb_reviewed:true' when searching for templates.

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

Protein Functional Annotation Pipeline

Build a high-performance protein annotation agent using the Google ADK (Agent Development Kit). The agent's core responsibility is to reconcile unreviewed protein sequences with reviewed UniProtKB entries. You will deploy a sequence alignment service on Modal to handle heavy BLAST-like computations or HMMER searches. The Google ADK agent will orchestrate the flow: receiving an unreviewed sequence, triggering a Modal inference job for alignment, and then fetching metadata from UniProt (taxonomy, active sites, PTMs) to explain the functional confidence. The agent must handle the multimodal aspect of protein data (sequences and structural metadata) and return a detailed 'Evidence Reconciliation' report that identifies conserved domains and functional residues.

Open challenge

Related prompts

Browse library

Problem Definition and Requirements Gathering

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

AI Solution Architecture and Strategy

To create a comprehensive technical architecture and AI strategy that guides the implementation of the solution.

Implementation Plan and MVP Development

To provide a clear, actionable roadmap for implementing the AI solution from concept to deployment.