Calibrated talent decisions
Assess every skill against the same defined bar, so the outcome does not depend on who ran the review.
— Talent & Leadership
Training Needs Analysis
Extracts the skills a team holds from their CVs or LinkedIn profiles, extracts the skills the strategy demands of them, then researches named courses and modules across LinkedIn Learning, Coursera, edX and provider catalogues to build a prioritised learning plan with on-the-job interventions.
- Talent & Leadership
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~25 minutes
For a typical multi-document review workflow
No coding required
- Input
- Assessment, review or performance evidence you already hold
- Output
- A calibrated view of each skill 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 Training Needs Analysis 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 skill with the rationale shown
- A population-level read a talent committee can act on
— HOW IT BEHAVES
How Training Needs Analysis produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Coverage across the role or function in scope, both directions
A coverage pass shows which skills are supported by evidence and which are not — including requirements with nothing behind them and material that supports nothing.
The gap named and sized
What the role or function in scope requires and what it currently has are set against each other skill by skill, so each gap is quantified rather than described in general terms.
Capability read from the evidence
Skills and requirements are read out of role documentation and capability evidence as structured data rather than inferred from job titles.
Whole sets of role documentation and capability evidence at once
All of your role documentation and capability evidence is processed as one set, so patterns across documents surface instead of being read one file at a time.
Live research on the role or function in scope
Current external sources on the role or function in scope are researched during the run rather than recalled from training data, and every source travels with the output.
Composed as work product
Findings on the role or function in scope are written up as a document that reads like professional output, with each claim tied back to a skill.
Why this is expensive by hand
Judgements about the role or function in scope 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 skill against the same defined bar, so the outcome does not depend on who ran the review. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 1–2 weeks of capacity for it.
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 skill with the rationale shown, with a population-level read a talent committee can act on. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. Net effect: 1–2 weeks down to ~25 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
Training Needs Analysis 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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