Calibrated talent decisions
Assess every signal against the same defined bar, so the outcome does not depend on who ran the review.
— Talent & Leadership
Performance review synthesis
Turn a cycle of performance reviews — ratings, objectives and manager commentary — into a calibrated, defensible picture: goal attainment, rating-versus-evidence gaps, prior-year movement, manager leniency and next-cycle priorities.
- Talent & Leadership
- Traceable reasoning
- Runs anywhere
3–4 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 signal with the rationale shown
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~3–4 weeks per run
— USE CASES
What people use Performance review synthesis 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 signal with the rationale shown
- A population-level read a talent committee can act on
— HOW IT BEHAVES
How Performance review synthesis 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 market being watched
Each row of your export is processed on the same basis, so no signal is skipped however long the table is.
The pattern charted
Movement across the market being watched is visualised from the computed data, so the trend is legible at a glance.
Composed as work product
Findings on the market being watched are written up as a document that reads like professional output, with each claim tied back to a signal.
Roll-up alongside signal-level detail
Detail rows are summarised into the grouped view of the market being watched without losing the underlying signals.
Why this is expensive by hand
Judgements about the market being watched are usually made from evidence spread across assessments, reviews and conversations, with the bar shifting between assessors. In practice it shows up as calibrated talent decisions: assess every signal against the same defined bar, so the outcome does not depend on who ran the review. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 3–4 weeks of senior time whenever it comes round.
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 signal 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. Net effect: 3–4 weeks down to ~40 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
Performance review synthesis 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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