Calibrated talent decisions
Assess every person against the same defined bar, so the outcome does not depend on who ran the review.
— Talent & Leadership
Employer brand & competitor benchmark
Export public employer reviews for your company and two or three competitors you hire against — Glassdoor, Indeed, Kununu or similar — into one table with Company, Source, Date, Rating and the Review text (paste pros and cons into one cell).
- Talent & Leadership
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- Assessment, review or performance evidence you already hold
- Output
- A calibrated view of each person with the rationale shown
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Employer brand & competitor benchmark for
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 person with the rationale shown
- A population-level read a talent committee can act on
— HOW IT BEHAVES
How Employer brand & competitor benchmark produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Live research on the talent population
Current external sources on the talent population are researched during the run rather than recalled from training data, and every source travels with the output.
Every row of the talent population
Each row of your export is processed on the same basis, so no person is skipped however long the table is.
The pattern charted
Movement across the talent population is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the talent population are written up as a document that reads like professional output, with each claim tied back to a person.
Roll-up alongside person-level detail
Detail rows are summarised into the grouped view of the talent population without losing the underlying people.
Why this is expensive by hand
Judgements about the talent population 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 person against the same defined bar, so the outcome does not depend on who ran the review. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 2–3 weeks of experienced attention.
How this Skill produces it
Skillsize turns that work into a Skill: you supply the material, and what comes back is a calibrated view of each person with the rationale shown, with a population-level read a talent committee can act on. 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: 2–3 weeks of manual work becomes a ~40 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
Employer brand & competitor 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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