Testing a growth thesis
Assesses whether current demand supports a proposed growth opportunity. Provides an evidence-backed view of momentum and the uncertainties that matter to the recommendation.
— Markets & Intelligence
Demand Signal Analyser
Reconciles live market signals for a product, service or category against fresh external research to rule on whether demand is strengthening, softening or shifting elsewhere.
- Markets & Intelligence
- Traceable reasoning
- Runs anywhere
1 week → ~15 minutes
Including the external research pass
No coding required
- Input
- Product, service or category focus
- Output
- Evidence-backed verdict on demand direction
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1 week per run
— USE CASES
What people use Demand Signal Analyser for
Reviewing a softening market
Examines whether weaker signals reflect a broader decline or a shift in demand. Grounds the assessment in corroborating external research rather than isolated indicators.
Understanding shifts in demand
Identifies where demand appears to be moving across segments, markets or alternatives, where the evidence allows. Clarifies what those shifts mean for the product, service or category under review.
Supporting a client recommendation
Provides a clear demand assessment with supporting evidence and caveats. Gives advisory teams a substantiated market perspective for client discussions and strategic recommendations.
What it works from
- Product, service or category focus
- Market scope and demand questions
- Current market signals
- Fresh external research
What you get back
- Evidence-backed verdict on demand direction
- Synthesis of current market signals and external research
- Assessment of demand shifts within the market scope
- Areas of corroboration and conflicting evidence an assessment depends on
— HOW IT BEHAVES
How Demand Signal Analyser produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Live research on the market being watched
Current external sources on the market being watched 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 market being watched are written up as a document that reads like professional output, with each claim tied back to a signal.
Criteria-based analysis of every signal
Each signal is assessed against explicit criteria you control, so the same standard is applied across the market being watched on every run.
Like-for-like comparison of signals
Signals are compared on the same dimensions, which makes differences meaningful rather than impressionistic.
Why this is expensive by hand
Demand is rarely clear from a single indicator: rising interest can coexist with weaker purchasing, while growth in one segment can mask decline elsewhere. Testing a growth thesis is the typical trigger — assesses whether current demand supports a proposed growth opportunity. Provides an evidence-backed view of momentum and the uncertainties that matter to the recommendation. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 1 week of manual work.
How this Skill produces it
Here the same job runs as a Skill. Your material goes in; evidence-backed verdict on demand direction comes out, alongside synthesis of current market signals and external research. 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 week 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
- Strategy and market intelligence teams
- Corporate development and investment teams
- Consultants advising on positioning and timing
- Leaders who need early warning, not hindsight
Run it in Skillsize — or export it anywhere
Demand Signal Analyser 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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