Comparing technical candidates
Provides comparable assessments grounded in consistent core questions and a four-level rubric. Quoted evidence helps hiring teams distinguish detailed technical explanations from unsupported claims.
— Talent & Leadership
Assesses how clearly people explain their technical work against a consistent four-level rubric, producing a human-approved report with quoted evidence behind every score.
1–2 weeks → ~15 minutes
For one complete, review-ready pass
No coding required
— USE CASES
Provides comparable assessments grounded in consistent core questions and a four-level rubric. Quoted evidence helps hiring teams distinguish detailed technical explanations from unsupported claims.
Gives leaders an evidence-backed view of how team members explain their technical approaches, decisions and results. The report supports development discussions without presenting verbal evidence as proof of practical proficiency.
Makes the basis for each score explicit through quoted responses and rubric-based judgements. Observable response signals, such as pasted text, writing time and depth under follow-up, inform human review without claiming to detect AI use.
Provides a shared scoring framework for technical explanations, with evidence attached to every rating. Human approval keeps accountability for the assessment with the people making the decision.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
The run pauses for a person to confirm the skills that matter before the deliverable is composed.
Findings on the role or function in scope are written up as a document that reads like professional output, with each claim tied back to a skill.
Each skill is assessed against explicit criteria you control, so the same standard is applied across the role or function in scope on every run.
The run works from role documentation and capability evidence you supply, so conclusions about the role or function in scope are anchored to your evidence rather than general model knowledge.
Technical assessments often rely on assessor judgement, uneven questioning and confident claims that lack supporting detail. Comparing technical candidates is the typical trigger — provides comparable assessments grounded in consistent core questions and a four-level rubric. Quoted evidence helps hiring teams distinguish detailed technical explanations from unsupported claims. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 1–2 weeks of experienced attention.
Here the same job runs as a Skill. Your material goes in; technical competency assessment report comes out, alongside human-approved scores against a four-level rubric. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. In effect, 1–2 weeks of senior time compresses into ~15 minutes — and the output is comparable across clients, quarters and colleagues instead of shaped by whoever ran it.
Technical Competency Assessment exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
Defines credible career routes between a starting role and a target role, with competency criteria, evidence of readiness and development priorities that make progression expectations clear.
Maps realistic career moves across an organisation, showing how roles connect, which competencies support each move and what gaps people need to close.
Assesses how a leadership or project team’s experience and working styles fit its purpose, identifying complementary strengths, gaps and likely friction with practical recommendations for working together.
Defines a role’s purpose, outcomes, capabilities and decision accountabilities in an evidence-grounded blueprint, giving hiring managers and leaders a shared basis for selection and development.
Assesses one candidate against a specific role, producing an evidence-backed fit report with competency scores, strengths, gaps, risks and focused interview questions.
Compares candidates against a shared, role-specific rubric, producing an evidence-backed assessment of their relative fit, key differentiators and trade-offs.