Calibrated talent decisions
Assess every person against the same defined bar, so the outcome does not depend on who ran the review.
— Talent & Leadership
Absence & burnout early warning
Upload an absence or engagement export with a row per absence event or per employee-month. Outlier detection flags the teams and individuals sitting well outside the norm, trend projection shows where absence rates land if the current direction continues, and a cohort comparison contrasts the flagged population against everyone else.
- Talent & Leadership
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~15 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
- ~1–2 weeks per run
— USE CASES
What people use Absence & burnout early warning 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 Absence & burnout early warning produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
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.
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. 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
As a Skill, the work is already sequenced. You bring the evidence, and the run produces a calibrated view of each person with the rationale shown plus 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. 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.
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
Absence & burnout early warning 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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