implementation

BLACKBOX AI Tool Integration

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

Linked challenge: Build a Workplace RL-Gym with LangGraph and Claude Opus 4.6.6

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Linked challenge

Build a Workplace RL-Gym with LangGraph and Claude Opus 4.6.6

Prompt source

Original prompt text with formatting preserved for inspection.

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No variables

0 checklist items

Write a LangChain tool wrapper for BLACKBOX AI that allows an agent to search a simulated Jira codebase for specific bug reports and return structured JSON summaries.

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

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Purpose

implementation

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

Linked challenge

Build a Workplace RL-Gym with LangGraph and Claude Opus 4.6.6

Inspired by tech labs acquiring Slack and Jira archives to train agents, this challenge tasks you with building a multi-agent simulation gym. You will create a virtual workplace environment where agents must coordinate tasks across simulated productivity tools. Using LangGraph for stateful orchestration, you will design specialized agents for project management, engineering, and communication that can handle long-term task dependencies without relying on vector retrieval. The goal is to evaluate how agents handle interruptible workflows and cross-agent negotiation within a simulated enterprise software stack.

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Related prompts

Browse library

Initial Assessment Prompt

planning

Content Classification Prompt

implementation

Problem Definition and Requirements Gathering

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