Shrimp Task Manager Research Mode Is Live: Equipping AI with Real Technical Research Capabilities

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From day one, the goal of Shrimp-Task-Manager has never been just to delegate or break down tasks to an AI assistant, but to develop an assistant that possesses true upstream and downstream thinking—just like an engineer.

However, during development, I quickly realized something: before most engineering tasks even begin, what’s truly needed isn’t “execution” — it’s “research.”

That’s why in this update, I introduced the brand-new Research Mode. This article will walk you through its design rationale, practical workflow, and a full experiment I conducted using it.

Background: Why Do We Need “Research Mode”?

In software development, the most common issue isn’t “not knowing what to do,” but rather not knowing which solution is more appropriate.

Here’s a real example: say I want to optimize Laravel search capabilities. The potential options include Scout with Meilisearch, Elasticsearch, Algolia, or even rolling a custom solution with SQL and Redis.

But each option comes with trade-offs, such as:

  • Cost, performance, and maintenance complexity
  • Integration difficulty within the Laravel ecosystem
  • Whether the user experience actually improves

You can’t solve this kind of problem with a single prompt. You also can’t just tell AI to “pick the best one,” because without enough context and exploration, it simply doesn’t know what “best” means.

That’s why I designed a new prompt interface and task flow — Research Mode.

How Research Mode Works

Unlike typical prompt-response interactions, Research Mode works through a multi-stage command-driven process, enabling complete technical research.

The general process looks like this:

  1. Initiate Research Mode
    Use the research_mode tool to start a research session, clearly defining a goal (e.g., “Improve Laravel search performance”).

  2. Initial Exploration
    Shrimp searches the web to learn about commonly used community practices (e.g., Laravel Scout, Redis caching, aggregated search strategies).

  3. Focused Investigation
    It then dives deeper into specific technologies, gathering details on implementation methods, deployment tips, and potential pitfalls.

  4. Compare with Existing Codebase
    It reads the current project code to assess relevance, diagnose weaknesses, and identify applicable improvements.

  5. Generate Conclusions
    Finally, Shrimp produces a structured “Research Report” with actionable insights.

Real-World Test: Researching Best Practices for Laravel Search

To test Research Mode, I used a real task: Improving the structure and performance of an existing Laravel search system.

The process began with broad exploration, gathering popular techniques, then diving deeper into specific technologies like Scout + Meilisearch and Redis caching. It referenced Laravel docs, community articles, and performance benchmarks.

The final research report included:

  • Diagnostic analysis of the current code (e.g., multiple model LIKE queries, no caching, no tracking)
  • Pros and cons of different technical solutions
  • Concrete enhancement recommendations

Integrating with Task Systems: From Research to Execution

Once the research report is complete, it can be immediately converted into executable tasks using plan_task, followed by structure and dependency checks using analyze_task and reflect_task.

The full flow becomes:

  1. research_mode → Build knowledge base and research flow
  2. plan_task → Convert conclusions into actionable tasks
  3. analyze_task → Analyze current project structure and constraints
  4. reflect_task → Refine task granularity and correct any flaws
  5. split_tasks → Output a final structured task board

Tasks are no longer arbitrary checklists — they are evidence-based decisions.

Tool Positioning and Future Direction

With Research Mode added, Shrimp now complements its task-oriented foundation with exploratory thinking.
This is especially helpful for situations where there’s no clear answer, like:

  • Should we adopt this library?
  • Is this architecture suitable for our project?
  • Are there more lightweight alternatives?

Conventional AI tends to guess based on prior knowledge. Shrimp’s Research Mode mimics how an engineer would “dig into the facts before making a decision.”

Usage Tips and Open Source Project

This research workflow is already integrated into the MCP Shrimp Task Manager and works seamlessly within the automated development environment for Cursor IDE.

You can combine it with the task management module — use research_mode to identify problem context and justification before jumping into task decomposition.

:pushpin: Open source project: mcp-shrimp-task-manager

:white_check_mark: For best results, we recommend using claude-4-sonnet for this research mode experience.