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
Product roadmap realism check
Reads the roadmap, the backlog, engineering capacity data and shipped-on-time history. Extracts committed items and timelines, computes available capacity against claimed scope, scores roadmap realism, and flags items with no owner or no dependency coverage.
- Product & Tech
- Deterministic scoring
- 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 Product roadmap realism check 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 Product roadmap realism check produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Scored against your criteria
Each process step is assessed against criteria you control and weight, so the same standard applies to every process step in the end-to-end process.
Calculated per process step, not estimated
The figures behind each process step are computed by formula during the run, so the arithmetic is identical every time and can be checked line by line.
Numbers lifted out of process documentation and run data
Figures buried in narrative process documentation and run data are extracted as data you can compute and compare with.
Process documentation and run data pulled into one schema
Every process step is captured in the same field structure, so records drawn from different documents and sources stay comparable.
Whole sets of process documentation and run data at once
All of your process documentation and run data is processed as one set, so patterns across documents surface instead of being read one file at a time.
Live research on the end-to-end process
Current external sources on the end-to-end process are researched during the run rather than recalled from training data, and every source travels with the output.
Why this is expensive by hand
Product roadmap realism check is routine in name only: the inputs are messy, the standard is unwritten, and two people rarely reach the same answer. In practice it shows up as 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. It is the kind of work that decides whether a recommendation survives scrutiny — and the kind that quietly eats 2–3 weeks of senior time whenever it comes round.
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. In effect, 2–3 weeks of senior time compresses into ~25 minutes — and the output is comparable across clients, quarters and colleagues instead of shaped by whoever ran 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
Product roadmap realism check 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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