implementation

Tool Integration Prompt

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

Linked challenge: Autonomous Security Operator Agents SDK

Format

Text-first

Lines

Sections

Linked challenge

Autonomous Security Operator Agents SDK

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Define a set of tools for the SecurityOperator that includes get_vulnerability_context and apply_fix. Ensure the agent uses the GPT-5.3-Codex reasoning budget effectively to explore the root cause before proposing a solution.

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

Autonomous Security Operator Agents SDK

For general security intelligence, this challenge requires building an autonomous agent capable of identifying and fixing vulnerabilities in real-time. You will use the OpenAI Agents SDK to build a multi-turn operator that utilizes function calling to interface with Proxis AI for engineering diagnostics and Vellum for managing agent prompts and prompt variants. The agent must perform multi-step reasoning using the GPT-5.3-Codex model to simulate a human security engineer navigating complex codebases and Slack-based incident reports.

Open challenge

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Problem Definition and Requirements Gathering

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