Calibrated talent decisions
Assess every risk against the same defined bar, so the outcome does not depend on who ran the review.
— Talent & Leadership
Attrition risk radar
Upload one HRIS export with a row per employee and a column recording whether they left or stayed (e.g. 'Left' = Yes/No).
- 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 risk 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 Attrition risk radar 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 risk with the rationale shown
- A population-level read a talent committee can act on
— HOW IT BEHAVES
How Attrition risk radar 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 risk universe in scope
Each row of your export is processed on the same basis, so no risk is skipped however long the table is.
Composed as work product
Findings on the risk universe in scope are written up as a document that reads like professional output, with each claim tied back to a risk.
Roll-up alongside risk-level detail
Detail rows are summarised into the grouped view of the risk universe in scope without losing the underlying risks.
Why this is expensive by hand
Judgements about the risk universe 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 risk 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 1–2 weeks of experienced attention.
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 risk with the rationale shown plus 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: 1–2 weeks of manual work becomes a ~15 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
Attrition risk radar 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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Upload performance data with a row per employee and a labelled outcome column (e.g. 'Top performer' = Yes/No, or 'Promoted').
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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.
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Reads a CV, Hogan report and performance notes to establish growth appetite and development needs, takes an internal role list (pasted, uploaded or researched) and ranks the available internal moves with a development rationale explaining why each one stretches the right capability.