Senior hire assessment
Grade one candidate's evidence against explicit criteria rather than a hiring panel's memory of the last person they saw.
— Talent & Leadership
Candidate benchmark
Grades one person's evidence against explicit criteria, returning a consistent criterion-by-criterion assessment however many people are run through it.
- Talent & Leadership
- Traceable reasoning
- Runs anywhere
half a day → ~10 minutes
For one complete, review-ready pass
No coding required
- Input
- The person's CV, profile or evidence documents
- Output
- A criterion-by-criterion assessment
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~half a day per run
— USE CASES
What people use Candidate benchmark for
Internal readiness checks
Assess an internal candidate against the benchmark before committing to a move.
Second-opinion reviews
Re-assess a contested judgement against the same criteria with the reasoning visible.
What it works from
- The person's CV, profile or evidence documents
- The criteria or benchmark to assess against
- Any weighting you want applied
What you get back
- A criterion-by-criterion assessment
- Strengths and gaps named against evidence
- Output comparable with every other person assessed
— HOW IT BEHAVES
How Candidate benchmark produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Criteria-based analysis
Material is assessed against explicit criteria you control, so the same standard is applied on every run.
Like-for-like comparison
Items are compared on the same dimensions, which makes differences meaningful rather than impressionistic.
Grounded in your own material
The run works from documents and data you supply, so conclusions are anchored to your evidence rather than to general model knowledge.
Why this is expensive by hand
Assessing one person against a benchmark is where inconsistency creeps in fastest. In practice it shows up as senior hire assessment: grade one candidate's evidence against explicit criteria rather than a hiring panel's memory of the last person they saw. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only half a day of capacity for it.
How this Skill produces it
Skillsize turns that work into a Skill: you supply the material, and what comes back is a criterion-by-criterion assessment, with strengths and gaps named against evidence. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. Net effect: half a day down to ~10 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Who it's for
- Independent consultants codifying their own methodology
- Strategy and transformation teams standardising delivery
- Internal advisory functions under pressure to produce faster
- Operators who need defensible output, not a one-off chat answer
Run it in Skillsize — or export it anywhere
Candidate benchmark 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.
ChatGPTClaudeCopilotAI Products (MCP)
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