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.
— Finance & Investment
Bank–GL reconciliation
Joins your bank statement to the GL on the shared reference, computes each difference arithmetically, and uses AI only to explain and age the exceptions.
- Finance & Investment
- Traceable reasoning
- Runs anywhere
2–3 days → ~25 minutes
For a full population, not a sample
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 days per run
— USE CASES
What people use Bank–GL reconciliation 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 Bank–GL reconciliation produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Row-level table processing
Every row of your table is processed on the same basis, however long the table is.
Composed as work product
Findings are written up as a document that reads like professional output rather than raw model text.
Roll-up alongside detail
Detail rows are summarised into the grouped view without losing the underlying lines.
Why this is expensive by hand
In finance & investment work, bank–GL reconciliation is one of those tasks that looks straightforward until you are three documents deep and the details stop agreeing with each other. 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. Get it right and the conclusion holds up in the room; get it rushed and it gets picked apart. Either way it costs roughly 2–3 days 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 structured, review-ready deliverable plus a traceable record of what informed each conclusion. 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. Net effect: 2–3 days down to ~25 minutes, no drift between runs, and every conclusion traceable back to the evidence behind it.
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
Bank–GL reconciliation 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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