Delivery capacity planning
Establish whether committed work can actually be delivered by the people available.
— Organisation & Workforce
Capacity planning
Models demand against supply to compute the capacity gap, showing where and when a delivery shortfall will appear.
- Organisation & Workforce
- Deterministic scoring
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~40 minutes
For a full population, not a sample
No coding required
- Input
- Demand data: projects, volumes or forecasts
- Output
- A computed capacity gap by period
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Capacity planning for
Pipeline resourcing
Model capacity against a pipeline before commitments are made.
Recruitment timing
Show when a shortfall becomes real so hiring starts early enough to matter.
What it works from
- Demand data: projects, volumes or forecasts
- Team, role and availability evidence
- Utilisation assumptions and horizon
What you get back
- A computed capacity gap by period
- Where the shortfall sits, by team or skill
- The hiring or reallocation implication
— HOW IT BEHAVES
How Capacity planning produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Calculated, not estimated
The figures are computed by formula in the run, so the arithmetic is identical every time and can be checked.
Numbers pulled from documents
Metrics buried in narrative documents are extracted as data you can compute with.
Anchored to your org map
Analysis runs against your actual structure and reporting lines rather than an assumed org shape.
Live external research
Current external sources are researched during the run rather than recalled from training data, and the sources travel with the output.
Applied to every item
The same analysis is executed per item across the whole population, not on a sample.
Branching on the evidence
The path taken depends on what the analysis found, so thin cases and strong cases are handled differently by design.
Why this is expensive by hand
Capacity planning 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 delivery capacity planning: establish whether committed work can actually be delivered by the people available. 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 computed capacity gap by period plus where the shortfall sits, by team or skill. The judgement is built in — how items are broken up, what standard they are held to, and where the run stops for a human review. The practical effect: 2–3 weeks of manual work becomes a ~40 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
Capacity planning 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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