Pre-shortlist slate review
Check representation across the slate before the shortlist is fixed.
— Talent & Leadership
Reviews representation and evidence across a candidate slate while the process is still open, before the shortlist is fixed.
2–3 days → ~10 minutes
For one complete, review-ready pass
No coding required
— USE CASES
Check representation across the slate before the shortlist is fixed.
Evidence how the slate was reviewed for a client or governance requirement.
Show where the slate narrowed so sourcing can be adjusted in-flight.
— HOW IT BEHAVES
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Results are placed into defined bands, so thresholds decide the outcome instead of individual interpretation.
Material is assessed against explicit criteria you control, so the same standard is applied on every run.
The run works from documents and data you supply, so conclusions are anchored to your evidence rather than to general model knowledge.
Content is restructured into the form your deliverable or downstream system expects.
Slate reviews often happen after the shortlist is set, when it is too late to change anything. In practice it shows up as pre-shortlist slate review: check representation across the slate before the shortlist is fixed. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 2–3 days of senior time whenever it comes round.
Here the same job runs as a Skill. Your material goes in; a representation and evidence review across the slate comes out, alongside where the slate narrows, and against which requirements. 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: 2–3 days down to ~10 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
Diversity slate review 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.
Build a competency framework
Groups and merges skills from role profiles, standards and existing drafts into a single structured, consistently worded competency framework.
Role benchmark research
Researches how a role is defined externally and reshapes the findings into a structured benchmark you can hold your own definition against.
Potential and readiness
Assesses potential and readiness separately against calibrated criteria, showing the supporting evidence behind each bucket for talent review discussions.