Talent & Leadership

Next-Role Architect

Reads a CV, Hogan report and interview notes to map career arc, values, motivators and derailers, researches companies actively hiring for that profile, pulls each hiring company's strategy signals, scores strategic fit and produces a ranked target list with a tailored positioning narrative per company.

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

1–2 weeks ~25 minutes

For a typical multi-document review workflow

No coding required

Input
Assessment, review or performance evidence you already hold
Output
A calibrated view of each role with the rationale shown
Runs in
Skillsize · ChatGPT · Claude · Copilot
Export
SKILL.md · MCP
Time saved
~1–2 weeks per run

— USE CASES

What people use Next-Role Architect for

Calibrated talent decisions

Assess every role against the same defined bar, so the outcome does not depend on who ran the review.

Evidence a committee can question

Every rating traces back to the evidence behind it, so a board or committee can interrogate it.

Consistent development at scale

Produce the same depth of output for the whole population, not only the most visible names.

What it works from

  • Assessment, review or performance evidence you already hold
  • Your competency model, success profile or leadership standard

What you get back

  • A calibrated view of each role with the rationale shown
  • A population-level read a talent committee can act on

— HOW IT BEHAVES

How Next-Role Architect produces its result

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

Whole sets of org data, role documentation and headcount exports at once

All of your org data, role documentation and headcount exports is processed as one set, so patterns across documents surface instead of being read one file at a time.

Live research on the organisation in scope

Current external sources on the organisation in scope are researched during the run rather than recalled from training data, and every source travels with the output.

Composed as work product

Findings on the organisation in scope are written up as a document that reads like professional output, with each claim tied back to a role.

Why this is expensive by hand

Judgements about the organisation in scope are usually made from evidence spread across assessments, reviews and conversations, with the bar shifting between assessors. Calibrated talent decisions is the typical trigger — assess every role against the same defined bar, so the outcome does not depend on who ran the review. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 1–2 weeks of manual work.

How this Skill produces it

Here the same job runs as a Skill. Your material goes in; a calibrated view of each role with the rationale shown comes out, alongside a population-level read a talent committee can act on. 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. Net effect: 1–2 weeks down to ~25 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.

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

Next-Role Architect 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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