Skillsize

— Talent & Leadership

Candidate ↔ Job Spec match

Extracts skills from a candidate and a job spec and runs a deterministic match, producing a coverage figure with a written fit verdict.

  • Talent & Leadership
  • Traceable reasoning
  • Runs anywhere
Preview methodology

30–45 minutes → ~3 minutes

For one complete, review-ready pass

No coding required

Input
The candidate CV or profile
Output
A deterministic skills match with coverage figure
Runs in
Skillsize · ChatGPT · Claude · Copilot
Export
SKILL.md · MCP
Time saved
~30–45 minutes per run

— USE CASES

What people use Candidate ↔ Job Spec match for

Single-candidate fit assessment

Get a computed match against the spec with the covered and missing skills named.

Hiring manager conversations

Discuss fit against extracted requirements rather than general enthusiasm.

Internal applications

Assess an internal candidate against a spec on the same basis as external ones.

What it works from

  • The candidate CV or profile
  • The job specification
  • Any weighting across requirements

What you get back

  • A deterministic skills match with coverage figure
  • Covered and missing requirements named
  • A written verdict report

— HOW IT BEHAVES

How Candidate ↔ Job Spec match produces its result

The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.

Capability read from the evidence

Skills and requirements are read out of CVs, applications and interview notes as structured data rather than inferred from job titles.

Composed as work product

Findings on the candidate pool are written up as a document that reads like professional output, with each claim tied back to a candidate.

Like-for-like comparison of candidates

Candidates are compared on the same dimensions, which makes differences meaningful rather than impressionistic.

Grounded in your own CVs, applications and interview notes

The run works from CVs, applications and interview notes you supply, so conclusions about the candidate pool are anchored to your evidence rather than general model knowledge.

Why this is expensive by hand

Every talent & leadership team needs candidate ↔ Job Spec match — and almost none of them do it the same way twice. Single-candidate fit assessment is the typical trigger — get a computed match against the spec with the covered and missing skills named. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 30–45 minutes of manual work.

How this Skill produces it

Here the same job runs as a Skill. Your material goes in; a deterministic skills match with coverage figure comes out, alongside covered and missing requirements named. 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. The practical effect: 30–45 minutes of manual work becomes a ~3 minutes run, held to an identical standard on the tenth engagement as on the first.

Who it's for

  • CHROs and talent management leads
  • Succession and leadership development teams
  • Executive assessors and coaches
  • Consultants running talent reviews

Run it in Skillsize — or export it anywhere

Candidate ↔ Job Spec match 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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