Quarterly portfolio review
Produces an exception briefing highlighting companies that sit well outside portfolio norms, with comparisons and rankings to support a focused performance discussion.
— Finance & Investment
Portfolio anomaly watch
Reads a single performance export across a whole portfolio to identify the companies behaving unusually against their own history and their peers, explaining where each trend is heading and which movements deserve attention now.
- Finance & Investment
- Traceable reasoning
- Runs anywhere
1–2 weeks → ~15 minutes
For a full population, not a sample
No coding required
- Input
- Portfolio KPI data by company and reporting period
- Output
- Quarterly portfolio exception briefing
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1–2 weeks per run
— USE CASES
What people use Portfolio anomaly watch for
Emerging financial pressure
Highlights unusual cash runway, burn and profitability measures alongside recent trends, helping teams identify companies that merit closer financial scrutiny.
Near-term performance outlook
Projects where a headline metric may land over the next few periods based on observed trends, giving reviewers a directional outlook rather than a guaranteed forecast.
Peer performance comparison
Ranks companies on individual measures and compares performance across relevant groups, helping reviewers assess unusual results in their portfolio or sector context.
What it works from
- Portfolio KPI data by company and reporting period
- Company identities and sector classifications
- Revenue, revenue growth, gross margin and EBITDA measures
- Cash runway, burn and headcount measures
What you get back
- Quarterly portfolio exception briefing
- Company-level KPI anomaly findings
- Portfolio and sector peer comparisons
- Company rankings by financial and operating measure implied by the available data
— HOW IT BEHAVES
How Portfolio anomaly watch 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 reporting period in scope
Each row of your export is processed on the same basis, so no line item is skipped however long the table is.
Composed as work product
Findings on the reporting period in scope are written up as a document that reads like professional output, with each claim tied back to a line item.
Roll-up alongside line item-level detail
Detail rows are summarised into the grouped view of the reporting period in scope without losing the underlying line items.
Why this is expensive by hand
Portfolio reviews can bury important exceptions beneath a broad set of company metrics. Quarterly portfolio review is the typical trigger — produces an exception briefing highlighting companies that sit well outside portfolio norms, with comparisons and rankings to support a focused performance discussion. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 1–2 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 quarterly portfolio exception briefing plus company-level KPI anomaly findings. 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: 1–2 weeks of manual work becomes a ~15 minutes run, held to an identical standard on the tenth engagement as on the first.
Who it's for
- Finance directors and FP&A teams
- Investment, deal and corporate development teams
- Controllers and reporting managers
- Consultants building or reviewing business cases
Run it in Skillsize — or export it anywhere
Portfolio anomaly watch 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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