Ask people to name technical skills for a resume and most will start listing programming languages — which explains why nurses, accountants, and warehouse leads routinely undersell themselves. Technical skills are not a software-industry category. They are any tool, system, method, or procedure specific enough that an employer can screen for it by name: an EHR platform, an ERP module, a certified welding process, a bid-management system. Every field has them, every field screens on them, and this guide maps them across fifteen fields so you can see — and claim — the ones you already have.
What counts as a technical skill in 2026 (hint: broader than code)
A working definition: a technical skill is a capability tied to a nameable tool, system, or method, where competence can be checked by someone else who has it. "Epic charting" qualifies. "Computer skills" does not — it names nothing checkable. The nameability matters for a practical reason: recruiters search databases and skim skills sections for specific terms. "Proficient with modern logistics software" matches no search; "SAP TM" and "route optimization" do.
The category has also widened. Spreadsheet modeling, CRM administration, video conferencing hosting at scale, query-writing against a warehouse, prompt-driven AI workflows — none of these are "IT jobs," yet all are technical skills in the screening sense. If your role touches a named system daily, that system belongs in your vocabulary of claims. The distinction from behavioral strengths — and why each is proven differently — is covered in our hard skills versus soft skills guide; this article stays on the hard side of that line.
Technical skills lists for 15 fields
Use your field's list as a memory prompt, then keep only what the target job would screen for.
1. Software engineering. Languages (Python, TypeScript, Java, Go, C#), frameworks (React, Django, Spring), Git, CI/CD pipelines, Docker, Kubernetes, cloud platforms (AWS, GCP, Azure), testing frameworks, observability tooling. Engineering resumes have extra conventions — ordering, depth signals, project links — detailed in our software engineer resume guide.
2. Data and analytics. SQL, Python or R, Excel (pivot tables, LOOKUP/INDEX-MATCH, Power Query), Tableau, Power BI, Looker, dbt, statistical testing, experiment design, data cleaning, warehouse platforms (Snowflake, BigQuery). See the data analyst resume guide for how to order tools against a posting.
3. Nursing and allied health. EHR systems (Epic, Cerner, Meditech), medication administration, IV therapy, telemetry, wound care, triage protocols, BLS/ACLS/PALS certifications, HIPAA-compliant documentation, specimen collection.
4. Accounting and finance. ERP systems (NetSuite, SAP, Oracle), QuickBooks, month-end close, reconciliations, GAAP/IFRS reporting, variance analysis, financial modeling, Power BI or Tableau for finance, payroll systems (ADP, Gusto), audit workpapers.
5. Marketing. Google Ads, Meta Ads, GA4, SEO tooling (Ahrefs, Semrush), marketing automation (HubSpot, Marketo, Klaviyo), CMS platforms (WordPress, Webflow), A/B testing tools, UTM governance, attribution reporting.
6. Sales and revenue operations. Salesforce, HubSpot CRM, outreach sequencing tools (Outreach, Salesloft), lead scoring, quote/CPQ tools, forecasting methodology, data enrichment platforms, call-recording analysis tools (Gong, Chorus).
7. Project and program management. Jira, Asana, Monday, Microsoft Project, Gantt and critical-path planning, agile ceremonies, Scrum/Kanban, risk registers, RAID logs, budget tracking, resource planning, stakeholder RACI mapping.
8. Design and creative. Figma, Adobe Creative Cloud (Photoshop, Illustrator, InDesign, Premiere, After Effects), prototyping, design systems, accessibility standards (WCAG), typography, print production specs, 3D tools (Blender, Cinema 4D).
9. Education. Learning management systems (Canvas, Google Classroom, Moodle), curriculum mapping, IEP/504 documentation systems, formative assessment platforms, gradebook administration, SMART Board and classroom tech, data-driven instruction tooling.
10. Human resources. HRIS platforms (Workday, BambooHR, ADP), applicant tracking systems (Greenhouse, Lever, iCIMS), payroll and benefits administration, compensation benchmarking tools, FMLA/ADA compliance processes, engagement survey platforms.
11. Legal and compliance. E-discovery platforms (Relativity), contract lifecycle management tools, legal research databases (Westlaw, LexisNexis), matter management systems, redlining workflows, privacy frameworks (GDPR, CCPA) as applied processes.
12. Manufacturing and skilled trades. CNC programming, GD&T blueprint reading, PLC troubleshooting, TIG/MIG welding certifications, CMMS maintenance systems, Lean/5S implementation, SPC quality tooling, OSHA 10/30, equipment-specific certifications.
13. Logistics and supply chain. WMS platforms, TMS routing systems, demand forecasting, inventory cycle counting, EDI transactions, customs documentation, fleet telematics, forklift and hazmat certifications.
14. Customer support. Ticketing systems (Zendesk, Freshdesk, Intercom, ServiceNow), knowledge-base authoring, macros and workflow automation, CSAT/NPS tooling, live chat and chatbot escalation flows, remote-access diagnostics.
15. Administrative and office management. Microsoft 365 (including Excel beyond basics and SharePoint administration), Google Workspace administration, calendar systems at executive scale, expense platforms (Concur, Expensify), e-signature workflows (DocuSign), records-retention systems, travel management tools.
Formatting options: categorized lists, proficiency levels, plain lists
Three formats handle nearly every case, and the choice depends on how many skills survive your relevance filter:
Plain list — one comma-separated line or a short block. Right when you have 6-10 skills in a single domain. Parses cleanly, scans in two seconds:
Epic, telemetry, IV therapy, triage, wound care, BLS, ACLS.
Categorized list — two to four labeled rows. Right when 10-15 skills span distinct tool families:
Analytics: SQL, Python, dbt, Snowflake. Visualization: Tableau, Looker. Experimentation: A/B testing, Optimizely.
Annotated list — skills with a parenthetical depth or context note, used sparingly for your two or three flagship tools: Excel (advanced: Power Query, VBA), Salesforce (admin-certified).
What to skip: skill bars, star ratings, percentage graphics. A parser extracts nothing from a shaded rectangle, and no human knows what 80% of Photoshop means. Keep the section in plain text, out of tables and text boxes, and it will survive any parser it meets. How many total skills to carry, and where the section sits on the page, follows the same selection logic as any skills list — the relevance-evidence filter applies unchanged to technical inventories.
The honesty scale: familiar vs proficient vs expert
Proficiency labels are optional, but if you use them, use them as commitments rather than decoration. A workable three-level scale:
- Familiar — you have used it for real work but would need documentation open. You can read it, navigate it, or modify existing work in it. Interview implication: you can discuss concepts but should not be handed a live exercise.
- Proficient — you work in it independently at normal professional speed. This is the unstated default when no label appears, which is why labeling everything "proficient" adds nothing.
- Expert — others come to you with their problems in this tool; you could teach it, and you know its failure modes. Claim it rarely. A resume with six "expert" labels has redefined the word.
Two honesty rules follow. First, never let a label outrun a live test: if a 20-minute practical exercise would embarrass the claim, step it down a level. Second, decade-old competence has an expiry — "SQL (expert, 2015)" is really "familiar, formerly expert," and saying something like "returning to daily use after..." in an interview lands far better than being surprised by rust in a screen-share.
Version specificity helps credibility too. "SAP" spans a dozen modules; "SAP FICO" tells the screener you have actually been inside it.
Keeping technical skills current: what to retire each year
A technical skills section needs an annual pruning pass, because stale tools do quiet damage — they date you, they dilute the strong claims around them, and occasionally they invite an interview question you no longer want.
A simple yearly ritual: for each listed skill ask, would the next job I want screen for this by name? Retire what fails, with three exceptions worth keeping: legacy systems still common in your target industry (plenty of enterprises run older ERP versions), skills that establish depth of career ("15 years across three generations of a platform" is a story, not staleness — though that story belongs in a bullet, not the skills line), and anything a specific target posting mentions.
Retired does not mean deleted from your records. Keep a private master list of everything you have ever used; tailoring becomes a matter of promotion and demotion rather than recollection.
AI tools as technical skills: how to list them credibly
AI tooling is now a legitimate technical-skill category — and one of the easiest to list badly. "AI" alone is not a skill, and "ChatGPT" by itself claims little more than owning a browser. Credible listings share two properties: they name the workflow, not just the tool, and they are backed by an outcome-bearing bullet.
Weak: AI tools, ChatGPT, prompt engineering.
Stronger, in the skills line: AI-assisted analysis (Claude, Copilot) integrated into reporting workflow. And in a bullet: Built a prompt library and review checklist for AI-drafted support macros, cutting first-draft time roughly in half while keeping every response human-approved.
The bullet is doing the persuading — it shows judgment about where the tool fits and where the human stays. That framing also preempts the reasonable skepticism some hiring managers hold: what they distrust is unreviewed output, so evidence of a review step is itself the skill. If your role genuinely does not use AI tooling yet, do not force it; a padded AI claim fails interviews exactly the way any padded claim does.
Frequently asked questions
How many technical skills should I list?
Eight to twelve for most roles, up to fifteen for deeply technical positions when organized into categories. The binding constraint is not space — it is that every listed skill must survive both a relevance check against the posting and a possible interview probe.
Should I list Microsoft Office as a technical skill?
Not as "Microsoft Office." Baseline Word and Outlook are assumed. Advanced Excel is the exception — pivot tables, Power Query, complex modeling are genuine screening criteria in many fields, so name the capabilities, not the suite.
Where should the technical skills section go on my resume?
High on the page — just under the summary — when tools are a primary screening criterion (engineering, data, nursing, accounting). Lower, after experience, when your bullets carry the stronger evidence. Either way, the flagship tools should also appear inside bullets doing real work.
Do I need to match the exact tool the posting names if I know a competitor?
List your real tool, and where honest, signal transferability: HubSpot CRM (5 years); familiar with Salesforce concepts and data model. Recruiters searching for the named tool will still find the family term, and you have made no claim you cannot defend.
See how your tools section reads to a screener
Run your resume through Workplacea's free resume checker — it shows which technical skills parse cleanly, which appear nowhere in your bullets, and how your section compares against a pasted job description. No invented proficiency, just what the page actually supports.