implementation

Graph Node Definition

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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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0 checklist items

Using LangGraph, define a stateful node for a Project Manager agent. The node must import { StateGraph, END } from 'langchain/graphs' and use Claude Opus 4.6.6 to analyze a simulated Slack stream and update the global state with a prioritized task list.

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 already mixes executable detail with instructions, so tune examples and interfaces before rewriting the scaffold.

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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Problem Definition and Requirements Gathering

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