The Product
One engine, two uses: decide who to hire, and see where your existing teams stand with AI. Here is the whole thing: both modes, every feature, and every role we assess.
One Engine, Two Modes
The same tasks and the same four axes, used on candidates you're considering or on the teams you already have. For internal teams you get a baseline, re-assessments over time, and a team view.
Send invite-token assessments to external candidates and compare them apples-to-apples in an isolated sandbox.
Employees take assessments under their own identity. See each person's baseline, re-assess over time, and view results by team.
Managers assign tasks to team members and see where AI skills are strong and where they need investment.
The Problem
Every professional uses AI daily — the ones you are interviewing and the ones already on your payroll. Hiring processes either ban it or can't read it, and for your existing team nobody is even trying to look.
Every role uses AI now — from coding to writing to analysis. Banning AI in assessments tests the wrong skill.
A finished deliverable tells you nothing about how it was reached. That is true of a take-home from a candidate and equally true of the work your team shipped last sprint.
Without a standard task and a standard rubric there is no fair way to compare two candidates, two teams, or the same person six months apart.
You can't tell whether someone knew which question to ask, or just accepted whatever the model said first — and that is the difference you are hiring and promoting on.
The Platform
Choose from 90+ ready-to-send assessments covering every role you hire for — engineering, product, marketing, design, QA, DevOps, data engineering, finance, legal, and more. Or author your own.
Candidates and employees get a purpose-built workspace with an AI assistant, reference materials, and a timer. Prompts, decisions, and output are all captured.
Get a profile on SkillMark's four dimensions — Designing, Supervising, Reviewing, Ownership, built on Anthropic's AI Fluency framework (Delegation, Description, Discernment, Diligence) — each rated with evidence. Every rating links to the moment in the session that earned it.
Features
Traditional assessments grade the output. SkillMark shows how the person got there with AI.
Designing (Delegation): choosing what to give the AI and what to keep. Supervising (Description): how clearly the work is specified. Reviewing (Discernment): whether AI output is checked and corrected. Ownership (Diligence): whether the result is shipped responsibly and owned. Built on Anthropic's AI Fluency framework.
Screen recording with event timeline overlay. See every keystroke, every prompt, every decision — reviewable at any speed.
Anthropic and OpenAI are both supported. SkillMark isn't tied to a single AI provider, so you see how people work with the AI they actually use.
Every rating is backed by a quote from the session, and every deduction is named and explained. Reviewers see why a score is what it is, not just the number.
Code editor for engineers. Rich text editor for PMs, marketers, designers. Each role gets the right workspace.
Provide CSVs, documents, briefs — candidates analyze real data with AI help, just like the actual job.
Why SkillMark
We're pre-launch. Here's what already works today.
Anthropic and OpenAI are both supported today. SkillMark isn't tied to a single AI provider.
Designing, Supervising, Reviewing, Ownership — SkillMark's four dimensions, built on Anthropic's AI Fluency framework (Delegation, Description, Discernment, Diligence). Each is reported separately so a strong average can't hide a weak axis. An overall score exists only as a summary for sorting.
Code, AI chat, and screen recording on one timeline — every prompt and decision reviewable.
Every rating is backed by quotes from the person's real session, and every deduction is named. Reviewers can see why a score is what it is.
Beyond Engineering
Not just coding. People write PRDs, analyze data, draft emails, review designs, and prepare budgets, all with AI and all observable.
Engineering
19 tasks
Product Management
7 tasks
ML / Data Science
7 tasks
Product Design
6 tasks
Operations
6 tasks
Sales / BDR
6 tasks
Data Engineering
6 tasks
DevOps / SRE
5 tasks
Engineering Management
5 tasks
Finance / FP&A
5 tasks
Marketing
5 tasks
Recruiting / HR
5 tasks
Content / Writing
3 tasks
Analyst / BI
2 tasks
Customer Success
2 tasks
Developer Relations
1 task
Legal / Compliance
1 task
QA / Test Engineering
1 task
Solutions Architect
1 task
The Real Cost
Tokens are cheap. Salaries are not. When a team hands work to AI without judgement, the cost doesn't show up on an invoice. It shows up as rework, longer reviews, and work that isn't as good as it looks. It also grows with headcount, and nothing on your P&L shows it.
Output accepted from AI without checking looks finished. The cost lands later — in review, in QA, or in production — and by then nobody traces it back to the prompt that caused it.
An hour of a skilled person's time costs far more than the AI they use during it. Two people with the same salary and the same model can get very different value out of it.
Two people on the same salary with the same model produce very different work. At ten people that is an annoyance. At two hundred it is a budget line you never approved — and it compounds, because unmeasured habits get taught to every new joiner by the person sitting next to them.
A finished deliverable tells you nothing about how it was reached. Seeing the prompts, the pushback, and the discarded answers shows you where a person's AI skill is strong and where it needs work.
FAQ
Every prompt, decision, and output, before you make an offer or plan your team's next step.
Invite-only access. Book a demo to get started.