Lightweight AI Literature Assistant: Dual-Channel Research Workflow Based on Skill Specifications

In current research workflows, literature search, reading, and management consume a large amount of time. As large language models become more capable, using AI to assist with literature reviews and knowledge extraction has become a mainstream need. However, early implementations in the field often relied on MCP (Model Context Protocol) or browser extensions. Although powerful, these approaches often involve high token consumption and complex process scheduling overhead in practice. To address this pain point, we designed and implemented a lightweight literature research assistant Skill that significantly reduces resource usage without sacrificing automation, offering a more practical technical path for everyday research scenarios.

1. Design Motivation: From MCP’s Heavy Protocol to Lightweight Skill Specifications

In many intelligent coding tools and agent platforms, the MCP protocol is widely used for standardized communication between tools. It exposes tool interfaces through independent service processes and provides models with rich context-awareness capabilities. In real deployments, however, we found that introducing the MCP pattern brings two challenges. First, every tool call requires serialization and deserialization, which lengthens the interaction chain. Second, maintaining protocol session state requires the model to generate a large amount of additional context tokens. In long-running, high-frequency literature reading, this overhead can grow rapidly.

By contrast, Skill specifications provide a lighter solution that is closer to an “instruction set.” It is essentially a structured skill description file—a skill manifest that an Agent can load—paired with a set of independent script tool functions. This design reduces tool invocation from inter-process communication to function-level calls, greatly reducing the burden of context encoding. At the same time, because the Skill file is a plain-text description, it can be parsed automatically by any client that supports the specification, without an additional process daemon or protocol gateway. Deployment and migration costs are therefore much lower.

2. Core Architecture: Unified Tool Abstraction and Clean Function Boundaries

The core of this approach is not a single application but a clearly defined set of tool functions. Structurally, the project follows the principle of separating skill definition from implementation.

• Skill orchestration layer: The skill description file serves as the Agent’s behavior contract, defining trigger conditions, execution order, and safety boundaries. For example, the description explicitly states that before write operations such as modifying an entry or deleting a tag, the assistant must generate an operation summary and ask the user for confirmation. This gives the AI autonomy while preserving the final decision for the user.
• Tool implementation layer: This layer provides a set of stateless Python functions that are easy to unit test. These functions cover high-frequency operations across the literature management lifecycle:
  • Search and query: keyword-based metadata search and precise lookup by DOI or title.
  • Full-text and annotation reading: parsing the text layer and annotation layer of PDF files and extracting highlights and notes.
  • Note and tag organization: automatically generating reading summaries, aggregating tags by topic, and batch-updating entry information.

It is worth emphasizing that these tool functions are independent of any specific frontend framework. Regardless of how they are invoked, they return structured data such as JSON, ensuring stable and parseable interaction with language models.

3. Dual-Channel Access: Hosted Environments and Local Standalone Operation

To meet different privacy preferences and usage scenarios, the Skill natively supports two parallel access modes, both of which share exactly the same underlying tool logic.

3.1 Skill Hosted Mode (e.g., Claude Code and Other Clients Supporting Skill Specifications)

In this mode, the Skill is treated as a first-class citizen of the client. Users only need to place the project files in the designated skill loading directory. When the client starts, it automatically recognizes the Skill and injects the corresponding capabilities into the conversation interface. Users can then issue instructions directly in natural language, for example: “Please search for review papers on federated learning from the past three years and extract the core contribution of each paper.” The Agent then automatically orchestrates the task sequence according to the work rules defined in the description file: first calling the search function to obtain a candidate list, then calling the full-text parsing function for highly relevant papers, and finally summarizing the extracted results using a unified template.

The biggest advantages of this mode are zero configuration and strong contextual awareness. The Agent can perform multi-turn in-depth reasoning by combining conversation history with user intent. When it needs to modify literature library data, it first presents a change plan to the user in natural language and executes the actual write operation only after confirmation, effectively reducing the risk of accidental operations.

3.2 Local Script Standalone Mode

For users who do not want to send literature data to third-party cloud services, the project provides an independent command-line script as an alternative. The script wraps a commercial model API; by modifying environment variables, users can switch to other models with compatible interfaces. Users can start a complete literature assistant session directly in the terminal. The interaction logic in this mode is almost identical to the hosted Skill mode: the user enters a question, and the script converts the question and context into function call requests, performs parameter validation and result parsing locally.

The value of this standalone process mode lies in its high portability. Because the script interacts with model services only through standard HTTP and the tool logic is fully local, it avoids binding to a specific client ecosystem. Even if the AI frontend is replaced in the future, the literature assistant can still be migrated and reused in a new environment as long as that environment supports tool calling.

4. Model-Agnostic Adaptation and Migration Mechanism

In the current large model ecosystem, although models differ in training objectives and areas of strength, most commercial-grade models are compatible with the OpenAI-style Function Calling specification. This commonality provides a natural foundation for adaptation: tool functions are described in JSON Schema format—function names, parameter descriptions, and required field sets. Based on the conversation context, the model determines which function to call and which parameters to pass, and returns a structured call intent. The model does not need to understand the underlying PDF parsing or database query details; it only needs to perform intent recognition and parameter extraction.

Therefore, in actual engineering deployment, switching the underlying model requires only three steps: changing the API endpoint, replacing the authentication key, and adjusting the model name identifier. When switching from a closed-source model to an open-source deployed Qwen series or the lower-cost Kimi series, neither the business code nor the tool functions need to be changed. This model-agnostic design decouples the solution’s lifecycle from industry model iteration and avoids a complete system rewrite caused by the capability limits of a single model.

5. Practical Benefits and Observations

Based on the architecture above, we tested the tool in typical research scenarios, such as literature surveys before defining a research topic and reference checking before paper submission. Overall feedback centered on three dimensions.

• Significantly lower token consumption: Compared with an MCP-proxy approach, this solution reduces token overhead by about one-third when completing the same search tasks. This is mainly due to the removal of protocol handshakes and repeated transmission of tool metadata, making each interaction short and concise.
• Better perceived response speed: Because there is no intermediate process translation, local functions execute almost as fast as scripts running directly on the host machine. Retrieving a full PDF and returning key passages usually takes seconds, delivering an experience close to instant question answering.
• Improved concurrency and stability: The lightweight process model breaks the single-point-of-failure bottleneck often found in external services. During batch literature import or large-scale tag cleanup, the independent execution of the tool script helps ensure that the main task is not interrupted by exceptions in subtasks.

6. Future Directions: Deeper Command-Line Integration

As an initial attempt at a technical approach, the current version has validated the feasibility of “Skill specification plus unified toolset” in literature management. The next step focuses on deeper integration with downstream parts of the ecosystem. For example, referencing command-line interface approaches, we aim to achieve the following:

• Text summaries generated in literature notes are directly synchronized as Markdown cards.
• Hook functions enable automated PDF download, renaming, and categorization.
• Explore personalized recommendation models based on search results to provide researchers with proactive intelligence updates.

In addition, the tool function layer still has room for expansion, such as OCR support for scanned PDFs and integration with vector databases to provide graph-based representation of citation relationships. Completing these capabilities will further raise the upper limit of the solution in deep research workflows.

7. Conclusion

In the wave of AI-assisted research, we do not necessarily need the most complex protocol; often, simpler and purer abstractions can deliver the best user experience and operational efficiency. The practice of this literature research assistant Skill demonstrates that, through good layered design and widely compatible interface specifications, developers can bypass heavy middleware and build a low-cost, easy-to-migrate, and fully featured AI literature assistant. We are opening it up and continuing to iterate, and we welcome interested peers to build on it for their own scenarios and jointly expand the boundaries of this toolchain.