Repeatable client analysis
Run the same structured analysis for every client or business unit so quality no longer depends on who picked up the work.
— Product & Tech
Data, AI & technology governance review
Reads data-sharing and customer agreements, AI/model inventory and documentation, governance policies and any DPIA / model risk assessments.
- Product & Tech
- Human review built in
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~25 minutes
For a typical multi-document review workflow
No coding required
- Input
- Documents, exports or uploads you already hold
- Output
- A structured, review-ready deliverable
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Data, AI & technology governance review for
Faster first drafts
Turn raw source material into a working draft in minutes and spend your time on judgement instead of assembly.
Comparable results over time
Produce the same shape of output each cycle so movement is measurable rather than re-argued.
What it works from
- Documents, exports or uploads you already hold
- Your own criteria, framework or house method
What you get back
- A structured, review-ready deliverable
- A traceable record of what informed each conclusion
— HOW IT BEHAVES
How Data, AI & technology governance review produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Consistent classification of decisions
Decisions are sorted into your categories using the same rules each time, which is what makes a large volume of governance and authority documentation readable.
Scored against your criteria
Each decision is assessed against criteria you control and weight, so the same standard applies to every decision in the decision set in scope.
Governance and authority documentation pulled into one schema
Every decision is captured in the same field structure, so records drawn from different documents and sources stay comparable.
Whole sets of governance and authority documentation at once
All of your governance and authority documentation is processed as one set, so patterns across documents surface instead of being read one file at a time.
Live research on the decision set in scope
Current external sources on the decision set in scope are researched during the run rather than recalled from training data, and every source travels with the output.
Branching on what the evidence shows
Thin cases and strong cases among your decisions are handled differently by design, based on what the analysis actually found.
Why this is expensive by hand
Data, AI & technology governance review is routine in name only: the inputs are messy, the standard is unwritten, and two people rarely reach the same answer. Repeatable client analysis is the typical trigger — run the same structured analysis for every client or business unit so quality no longer depends on who picked up the work. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 2–3 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 structured, review-ready deliverable plus a traceable record of what informed each conclusion. 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. The practical effect: 2–3 weeks of manual work becomes a ~25 minutes run, held to an identical standard on the tenth engagement as on the first.
Who it's for
- Independent consultants codifying their own methodology
- Strategy and transformation teams standardising delivery
- Internal advisory functions under pressure to produce faster
- Operators who need defensible output, not a one-off chat answer
Run it in Skillsize — or export it anywhere
Data, AI & technology governance 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.
ChatGPTClaudeCopilotAI Products (MCP)
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