1. Think About Technical Skills in Two Layers
Technical skills on a fresh-graduate resume can increasingly be examined in two layers.
The first layer consists of conventional technical skills: programming languages, data-analysis methods, engineering and design software, databases, laboratory techniques, manufacturing processes, modeling tools, specialized applications, and other teachable capabilities associated with particular fields of work.
The second layer consists of agentic-AI skills: the ability to work effectively with foundation models, LLM-based applications, AI agents, and tool-using AI systems to complete substantive tasks. Relevant capabilities can include communicating objectives and constraints, supplying useful context, decomposing complex tasks, conducting AI-assisted research and analysis, coordinating multi-step work, evaluating generated outputs, identifying errors, and iterating toward a reliable result.
These layers can coexist within the same candidate. A computer-science graduate might combine Python, SQL, Git, and cloud tools with effective use of coding agents. An engineering graduate might combine CAD, MATLAB, simulation tools, and manufacturing knowledge with AI-assisted technical investigation. A business or humanities graduate may have fewer specialized STEM tools while possessing substantial experience using LLMs and agentic systems for research, information synthesis, document analysis, data-oriented tasks, or structured knowledge work.
The two-layer perspective gives candidates with different educational backgrounds a broader way to investigate their technical capabilities. The same standard still applies to both layers: a resume skill should represent something the candidate can meaningfully use and explain.
An LLM can help begin this investigation through a skill-discovery task instruction:
Examine my education, projects, work, research, extracurricular activities, independent learning, and examples of how I use AI tools. Construct two preliminary inventories: conventional technical skills and agentic-AI skills. For every proposed skill, identify the evidence from my background that supports it, what I have actually used it to accomplish, and any additional information needed before it can be presented confidently on a resume. Do not infer a skill merely because I studied a related subject or used an AI product once.
This initial inventory provides the evidence base for the rest of the section.
2. Establish the Conventional Technical Layer
Conventional technical skills vary substantially by field. A software candidate may present programming languages, frameworks, databases, development environments, and infrastructure tools. An engineering candidate may emphasize design, simulation, programming, instrumentation, or manufacturing methods. A data-oriented candidate may include statistical techniques, programming, databases, visualization, and analytical software.
For example:
Programming: Python, Java, SQL
Data & Visualization: pandas, Excel, Tableau
Databases: PostgreSQL, MySQL
Tools: Git, Docker
An engineering candidate could have:
Design & Simulation: SolidWorks, AutoCAD, ANSYS
Programming & Analysis: MATLAB, Python
Manufacturing: CNC machining, additive manufacturing
The candidate should be able to connect important skills with actual use in coursework, projects, internships, research, competitions, or independent work.
An LLM can perform this evidence reconstruction across a large amount of background information:
Build an evidence map for my conventional technical skills. Search across the information I provide about coursework, projects, internships, research, competitions, and independent work. For each skill, identify where I used it, what I used it to accomplish, how recently or extensively I used it where that information is available, and whether my resume currently provides visible supporting evidence. Identify potentially useful technical capabilities that I may have overlooked.
This type of long-context analysis can uncover skills that candidates use repeatedly without recognizing them as resume-level capabilities.
3. Investigate Agentic-AI Skills as Applied Capabilities
Agentic-AI capability deserves more careful treatment than listing the names of popular AI products. Familiarity with an LLM interface provides limited information by itself. The stronger evidence concerns how the candidate uses AI systems to accomplish work.
A candidate may be able to define a complex task clearly, provide relevant context and source material, establish constraints, ask the system to compare alternative solutions, use agents or tool-enabled models for multi-step work, inspect intermediate results, detect unsupported conclusions, refine outputs through continued interaction, and verify important facts or calculations.
In technical work, these capabilities might appear in AI-assisted programming, debugging, data analysis, technical research, documentation, testing, or exploration of unfamiliar tools. In non-STEM work, they might appear in research synthesis, document analysis, information organization, spreadsheet work, structured writing, market investigation, or other knowledge-intensive tasks.
An LLM can help reconstruct this capability from actual usage:
Interview me about how I use LLMs and agentic-AI tools for academic, professional, technical, and personal projects. Concentrate on substantive tasks I have completed. Ask about the objectives I gave the system, the context and constraints I supplied, how I decomposed or delegated work, whether tools or external information were involved, how I evaluated intermediate outputs, what errors or limitations I encountered, how I corrected them, and how I verified the final result. Use my answers to identify demonstrable agentic-AI capabilities. Keep ordinary product familiarity separate from capabilities supported by repeated or substantial use.
The result may reveal that “ChatGPT” or another product name is an incomplete description of the underlying skill. The more informative capability could involve AI-assisted research, coding-agent workflows, structured analysis with LLMs, tool-using agents, or another form of applied AI work.
Terminology in this area is evolving quickly. Candidates should therefore favor descriptions they can explain concretely and avoid presenting fashionable terminology as established expertise without sufficient evidence.
4. Examine How the Two Layers Reinforce Each Other
The two layers become particularly informative when conventional knowledge and agentic-AI capability interact.
A candidate with programming knowledge may use an AI coding agent to explore an unfamiliar codebase, generate candidate implementations, investigate errors, construct tests, and review documentation. Their programming knowledge helps them evaluate the generated code and recognize inappropriate solutions.
A mechanical-engineering graduate might use an LLM to investigate simulation errors, compare modeling approaches, interpret documentation, or generate scripts for processing experimental results. Their engineering knowledge provides the conceptual basis for judging whether the AI-assisted work makes sense.
A non-STEM graduate can also use agentic AI to extend technical capability. Someone with little previous programming experience may use an LLM as a tutor and development assistant while learning Python, SQL, spreadsheet automation, or data visualization. Through repeated practice and completed projects, some of those capabilities may eventually become genuine conventional technical skills.
The distinction between AI-assisted acquisition and demonstrated capability remains important. A candidate who completed a data project with extensive AI assistance may have developed real analytical and programming skills. The appropriate resume claims depend on what the candidate now understands, can reproduce, can modify, and can explain.
An LLM can help examine this boundary:
Below are several projects and tasks that I completed with substantial AI assistance. For each one, separate the capabilities supplied primarily by the AI system from the capabilities I personally demonstrated or developed. Ask me questions about what I can now explain, reproduce, modify, troubleshoot, or perform in a new context. Based on my answers, identify which conventional technical skills and agentic-AI skills I can reasonably claim and which still require further learning or evidence.
This form of self-audit is particularly useful for fresh graduates because AI-assisted learning can produce rapid progress while making the boundary of independent capability harder to judge.
5. Select and Organize Skills for the Target Role
The final Technical Skills section does not need to reproduce the complete two-layer inventory. It should select capabilities that provide useful information for the intended application.
A STEM-oriented position may justify substantial conventional technical detail with a smaller set of relevant AI capabilities. Some knowledge-work positions may give greater significance to AI-assisted research, analysis, automation, or information processing. Other employers may have limited interest in explicit AI terminology. The balance depends on the work, available evidence, and information elsewhere in the resume.
An LLM can compare the candidate’s verified skill inventory with one or several target positions:
Compare my verified conventional technical skills and agentic-AI capabilities with these target job descriptions. Identify which capabilities are directly relevant, which provide useful supporting evidence, and which add little to these applications. Examine recurring employer terminology and requirements while keeping my actual evidence as the constraint. Then propose two Technical Skills structures with different emphasis and explain what each version makes most visible to a potential employer.
The resulting section might integrate AI within existing categories:
Programming & Data: Python, SQL, pandas, Tableau
Development Tools: Git, Docker
AI-Assisted Development: coding agents, LLM-assisted debugging and testing
Another candidate may have enough AI-related evidence for a distinct category:
Data Analysis: Excel, SQL, Tableau
AI & Agentic Tools: LLM-assisted research, document analysis, tool-using AI workflows
These are illustrative structures. The terminology should reflect the candidate’s actual capabilities and the conventions of the target field.
Product names can be included when familiarity with a particular platform is relevant. Capability-oriented wording can provide more durable information when the underlying method transfers across products.
6. Calibrate Proficiency and Avoid AI-Inflated Skill Claims
Both technical layers require careful claim calibration.
Traditional proficiency labels such as Beginner, Intermediate, and Advanced can be difficult to interpret consistently. Star ratings and percentage bars create additional ambiguity. Evidence from actual work often provides a more meaningful basis for deciding whether a skill deserves inclusion.
Agentic AI introduces another source of uncertainty because sophisticated tools can allow candidates to complete tasks that exceed their unaided technical knowledge. Successful completion of an AI-assisted programming task does not automatically establish broad programming expertise. The candidate’s ability to understand, evaluate, modify, troubleshoot, and reuse the resulting work helps determine what skill has actually been developed.
A focused LLM task instruction can test both layers:
Audit the technical-skill claims I am considering for my resume. For conventional skills, examine the tasks I have completed, recency of use, independence, and supporting evidence. For agentic-AI skills, examine the complexity and recurrence of my AI-assisted work, how I communicate tasks and constraints, how I evaluate outputs, how I handle errors, and how I verify results. Identify claims whose wording may imply greater capability than my evidence supports. Ask targeted questions before recommending a stronger or weaker formulation.
The candidate should be comfortable discussing the listed capabilities during interviews and, where relevant, demonstrating them in technical assessments or practical work.
7. Use the Complete Resume to Test the Technical Profile
A Technical Skills section gains credibility when important capabilities are supported elsewhere in the resume. Projects, research, internships, coursework, competitions, and other experiences can show how conventional technologies and agentic-AI capabilities were applied.
This cross-section analysis can reveal several useful patterns. A strong skill may be missing from the Technical Skills section despite appearing repeatedly in projects. A listed technology may have almost no supporting evidence. AI-assisted work may be described vaguely even though it involved sophisticated reasoning and tool use. A candidate may also be giving AI tools credit for work that would be more accurately described through the underlying technical or analytical capability.
An LLM can construct a two-layer evidence map:
Read my complete resume and supporting project notes. Create a map connecting every conventional technical skill and agentic-AI skill I propose to the experiences that demonstrate it. Identify important capabilities with strong evidence that are currently difficult to find, listed skills with weak supporting evidence, inconsistent terminology, and cases where the relationship between my own technical contribution and AI assistance is unclear. Recommend only changes that materially improve the accuracy or usefulness of the technical profile.
For candidates with several resume versions, the LLM can compare them and explain how each version changes the apparent balance between domain expertise, conventional technical capability, and AI-enabled working capability.
8. Construct a Technical Skills Section That Can Evolve
Technical capabilities change throughout a graduate’s early career. Agentic-AI tools also change rapidly, and their increasing capabilities may alter which tasks require specialized technical knowledge and how people acquire that knowledge.
A useful Technical Skills section therefore represents the candidate’s current demonstrable capabilities while remaining easy to revise as stronger evidence develops.
A comprehensive LLM task instruction could be:
Review my Technical Skills section through two layers: conventional technical capability and agentic-AI capability. Use my complete resume, supporting experience notes, and target job description as context.
For conventional skills, evaluate selection, evidence, relevance, terminology, categorization, recency, and implied proficiency. For agentic-AI skills, evaluate whether the section describes substantive capabilities or only product familiarity, what kinds of tasks I have completed with AI systems, how I manage context and constraints, how I evaluate and verify outputs, and whether my claimed level of capability is supported.
Examine interactions between the two layers. Identify conventional skills I have genuinely developed or strengthened through AI-assisted learning and work, while distinguishing them from tasks that remain heavily dependent on AI-generated solutions I cannot independently understand or adapt.
Compare the complete skill set with the target role and the rest of my resume. Prioritize the issues that materially affect accuracy, relevance, or clarity. Then generate several concise Technical Skills structures with different reasonable emphases, explain their trade-offs, and identify any claims that require further verification or stronger evidence before inclusion.
The two-layer model gives fresh graduates a broader way to investigate technical capability. STEM graduates may already possess substantial domain-specific skills and can examine how agentic AI extends the way they apply them. Graduates from other disciplines can identify substantive AI-enabled capabilities and may use these systems to accelerate learning in programming, data analysis, automation, and other technical areas.
The resulting skills still need to be genuine. AI can expand access to technical learning, reduce barriers to complex tools, provide rapid assistance, and enable individuals to undertake tasks that previously required considerably more specialized support. A resume should capture the capabilities the candidate has actually developed through that environment. Conventional technical knowledge and agentic-AI capability can then form complementary parts of a fresh graduate’s evolving technical profile.