Capital Allocation

Capital Allocation Skill

Allocating capital is the core executive job: a fixed pot, more good ideas than money, and the need to say no on the record. This skill scores initiatives by expected return and strategic fit per unit of cost, allocates against the cap (honouring must-funds), and makes the cut line explicit — so funding is a defensible portfolio choice, not the loudest voice in the room.

Required Inputs

Ask for these only if they aren't already provided:

• The cap — the total budget or headcount to allocate, and the period.
• The initiatives — each with its cost, expected return (revenue, savings, or a strategic value), and strategic fit.
• Constraints — anything that must be funded (compliance, keep-the-lights-on) or can't be partially funded.
• The objective — what you're optimising: near-term return, strategic positioning, or a balance.

Output Format

Capital Allocation: [pot], [period]

1. Objective & cap — what you're optimising and the total available.

2. Scored initiatives — a table; score = expected value × strategic fit, normalised per unit cost:

| Initiative | Cost | Expected return | Strategic fit (1–5) | Score / $ | Must-fund? | |---|---|---|---|---|---|

3. The allocation — funded vs. unfunded against the cap, with budget utilisation. Must-funds first, then highest score/$ until the cap binds.

4. The cut line — the marginal initiative that just missed, and what it would take to fund it (the most useful number for the debate).

5. Rationale & trade-offs — why the portfolio is balanced this way, what's deliberately not funded, and the reversibility of each bet.

6. Re-evaluation triggers — what would change the allocation mid-period (a bet pays off early, a must-fund grows).

Programmatic Helper

scripts/capital_allocate.py (stdlib only) does the allocation deterministically — must-funds first, then by score-per-cost until the cap binds — and reports the cut line:

# items.json: [{"name":"Mobile revamp","cost":300,"expected_return":900,"strategic_fit":5,"must_fund":false}, ...]
python3 scripts/capital_allocate.py items.json --budget 1000
python3 scripts/capital_allocate.py items.json --budget 1000 --json

Quality Checks

• [ ] Initiatives are scored on expected return AND strategic fit, not return alone
• [ ] Score is expressed per unit of cost, so cheap-good beats expensive-good fairly
• [ ] Must-funds are honoured before discretionary allocation
• [ ] The cut line is explicit — the marginal initiative and what it'd take to fund it
• [ ] What's deliberately not funded is stated, with the trade-off

Anti-Patterns

• [ ] Do not allocate by last year's split or by who argues hardest — score the portfolio
• [ ] Do not rank by absolute return — a $900 return on $300 beats $1000 on $900; use return per dollar
• [ ] Do not ignore strategic fit — the highest-ROI initiative can still be off-strategy
• [ ] Do not hide the cut line — the initiatives that just missed are the real decision, and the team deserves to see it
• [ ] Do not treat estimates as facts — expected returns are usually [hunch]/[external]; flag the confidence

Based On

Portfolio capital-allocation practice — expected-value × strategic-fit scoring per unit cost, against a hard constraint.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: mohitagw15856
• Source: mohitagw15856/pm-claude-skills
• License: MIT
• Homepage: https://mohitagw15856.github.io/pm-claude-skills/

Install and usage instructions live in the source repository linked above.