Academic work demands the simultaneous execution of multiple high-order tasks: locating credible sources, critically interrogating dense literature, synthesizing disparate ideas, and communicating findings with disciplinary precision. The 22 academic skills for AI agents demonstrate how AI skills can be integrated across the entire research lifecycle, from initial discovery and systematic note-taking to drafting, revision, and final polish, streamlining your workflow while preserving scholarly rigor.
What are AI academic skills?
AI academic skills for agents are specialized functions designed to support different stages of academic work, including research, writing, analysis, and knowledge management. Powered by academic-specific skills, AI can better help students and researchers find relevant literature, summarize complex materials, organize information, improve academic writing, manage citations, and analyze data more efficiently.
Unlike general-purpose assistants, AI equipped with academic skills is tailored to specific research and learning scenarios. They streamline repetitive tasks, improve productivity, and allow users to focus more on critical thinking and academic insights.
6 academic skills in Kimi you need to try
Kimi turns specialized academic support into practical skills you can directly use in your workflow. These academic skills can be installed inside Kimi and easily fine-tuned using simple natural language based on your needs. Each skill focuses on a specific research task, making study and writing more flexible and efficient. Here are Kimi academic writing and research skills you can try.
| Skill name | Description |
|---|---|
| deep-research | Conducts in-depth, evidence-based research by gathering, verifying, and synthesizing information from multiple sources into comprehensive findings. |
| sci-paper | Provides structured guidance for writing scientific papers for top conferences and journals, covering drafting, LaTeX formatting, figure design, and manuscript polishing. |
| academic-paper-reviewer | Simulates journal peer review by evaluating originality, methodology, results, and writing quality while providing actionable revision recommendations. |
| cite-style-converter | Converts and validates academic citations across APA, MLA, IEEE, and Harvard styles with batch processing support. |
| regression-modeler | Runs OLS and logistic regression on CSV or Excel datasets, generating coefficients, R², p-values, VIF, and plain-language interpretations. |
| correlation-auditor | Analyzes Pearson and Spearman correlations, computes partial correlations, and identifies potential spurious relationships in research data. |
How to use Kimi's built-in academic skills?
Kimi makes it easy to apply academic skills to your research workflow. Select a built-in skill, describe your task, and let Kimi handle specialized academic work with AI assistance. Follow these steps to get started:
Step 1: Enter a skill command
Type a skill command such as /deep-research in the input box to quickly find and activate the academic skill you need.
Step 2: Start your research task
Enter your requirements or questions, and Kimi will apply the selected skill to assist with tasks such as research, writing, analysis, and information organization.
Example prompt:
Then Kimi automatically searches the web, collects relevant sources, and synthesizes information into a structured research report, helping you explore topics more efficiently.
Step 3: Review and download your document
Review the generated content, make any necessary edits, and download the completed document for further use.
Equip 16 more open-source academic skills
Beyond Kimi's built-in skills, users can also explore open-source academic skills created by the community and other developers. These skills cover various research tasks, from finding papers and analyzing data to writing academic publications, providing more flexible options to enhance your research workflow.
| Skill name | Description | URL |
|---|---|---|
| paper-lookup | Searches across major research databases like PubMed, arXiv, bioRxiv, OpenAlex, and Semantic Scholar to collect papers, metadata, full-text links, and citation details. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/paper-lookup |
| literature-review | Builds structured literature reviews by grouping topics, tracking research trends, and identifying gaps across multiple studies. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/literature-review |
| scientific-writing | Assists in academic writing using the IMRaD structure, improving language, and guiding each section, such as the Introduction, Methods, Results, and Discussion. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/scientific-writing |
| peer-review | Simulates journal peer review by checking logic, methodology, originality, and ethical quality before submission. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/peer-review |
| database-lookup | Provides unified access to scientific databases like PubChem, UniProt, KEGG, COSMIC, ClinicalTrials.gov, and FDA datasets with traceable sources. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/database-lookup |
| scikit-learn | Supports machine learning workflows including classification, regression, clustering, feature engineering, and model evaluation. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/scikit-learn |
| scientific-visualization | Creates publication-ready graphs and figures with proper formatting, statistical annotations, and clear visual presentation. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/scientific-visualization |
| statsmodels | Handles statistical analysis like regression, ANOVA, time series modeling, and hypothesis testing with clear outputs and diagnostics. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/statsmodels |
| experimental-design | Helps design research experiments using methods like randomization, factorial design, blocking, and sample size estimation. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/experimental-design |
| pymc | Enables Bayesian modeling with MCMC sampling, hierarchical models, and uncertainty analysis for complex datasets. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/pymc |
| citation-management | Manages citations in APA, MLA, Chicago, GB/T 7714, and BibTeX formats, with automatic conversion and validation. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/citation-management |
| hypothesis-generation | Generates testable research hypotheses based on literature gaps, emerging trends, and cross-domain connections. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/hypothesis-generation |
| scientific-critical-thinking | Evaluates research quality by checking bias, data reliability, statistical validity, and strength of conclusions. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/scientific-critical-thinking |
| research-grants | Grant proposal writing assistance for foundations, institutions, or commissions. Includes budget planning and reviewer perspective simulation. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/research-grants |
| statistical-power | Calculates sample size and statistical power for study planning, covering t-tests, ANOVA, proportions, correlations, regression, and simulation-based designs. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/statistical-power |
| scientific-brainstorming | Facilitates evidence-aware research ideation with structured discussion, explicit assumptions, adversarial review, and decision logs for early-stage projects. | https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/scientific-brainstorming |
How to use these open-source academic skills with Kimi?
These open-source skills can be accessed through their URLs and installed and used with Kimi, allowing users to explore additional capabilities for different academic workflows. This makes it easier to extend Kimi's functionality and customize support for research and writing tasks.
Step 1: Enter a prompt
Open Kimi and provide a prompt with the URL of the open-source academic skill you want to install.
Example prompt:
Step 2: Let AI install the skill automatically
Kimi automatically downloads and configures the skill based on the provided URL. Once the installation is complete, the skill will be available in your Kimi workspace.
Step 3: Use the skill
Click "Update" to add the skill to Kimi, then select "Try it" to activate it. Once activated, enter your research request, such as finding relevant papers, retrieving academic sources, or exploring a specific research topic.
Take a step further: personalize your own academic skills
Kimi also lets you go one step further by turning your own documents into custom academic skills. This helps you convert notes, guides, or research material into ready-to-use tools that fit your personal study or writing style. Here is a simple way to create your own academic skills in Kimi.
Step 1: Access the document to the skills tool
Open Kimi Skills and click on "Document to skills" to start building your custom skill.
Step 2: Upload the files
Upload your academic materials, such as lecture notes, research papers, assignment guidelines, or writing frameworks, that you want to turn into a reusable skill. Kimi will read and structure the content so it can be used as a working tool.
Step 3: Create and use your skills
Once processed, your new skill becomes ready to use for real tasks like writing essays, summarising papers, or generating structured notes from similar inputs.
You can also edit it anytime or export it as a file for later use in other projects.
Expert practices for using academic skills
Expert use of AI academic skills is not just about knowing tools, but using them in a smart and structured way. When applied with clear planning, these skills can save time and improve research quality. The right approach helps you get more accurate and useful results from AI. Here are some tips for using these skills:
Start with a clear research objective
Define your research question, audience, and expected output before using any academic skill. This gives the AI a clear direction for what you want. As a result, responses become more focused and relevant to your study and overall academic project goals.
Provide high-quality reference materials
Upload trusted research papers, datasets, or notes related to your topic. Strong input data helps the AI produce more accurate summaries and insights. It also improves the quality of citations and explanations for stronger academic credibility and research depth.
Break complex projects into smaller tasks
Do not try to complete an entire thesis or large project in one step. Divide the work into stages like research, outlining, drafting, and editing. This makes the process easier to control and improves overall accuracy and consistency in results.
Verify citations and research findings
Always check AI-generated references and factual claims using sources. This helps avoid errors and keeps your work academically honest. Careful verification ensures your final output is reliable, valid, and suitable for publication or submission purposes.
Use specialized skills for specific tasks
Select dedicated academic skills for writing, analysis, citation, or data handling instead of general prompts. Each skill is designed for a specific purpose and works more effectively in that area. This improves precision, efficiency, and overall quality in your academic work.
Conclusion
AI is changing the way research and learning are done, making complex academic tasks more manageable and structured. With the right approach, even difficult work becomes easier to organize and complete. Modern tools now bring flexibility to every stage of study and writing. These academic skills for AI agents help turn scattered effort into a more focused and efficient workflow. Explore Kimi to experience how these skills can support your academic journey, and try it today.
FAQ
/deep-research, /literature-review, and /paper-lookup are designed to support literature review tasks. They can help gather relevant papers, compare research findings, summarize key information, and identify potential research gaps, making it easier to navigate large amounts of academic literature.