capital-allocation
Allocate a finite budget or headcount across competing initiatives by return and strategic fit. Use when asked to allocate budget, decide where to invest, build a funding/portfolio plan, or make trade-offs across initiatives under a cap. Produces a capital-allocation plan — initiatives scored by expected return × strategic fit per dollar, a funded/unfunded split against the cap, the cut line, and the reasoning.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/capital-allocation && cp -r /tmp/capital-allocation/plugins/pm-business/skills/capital-allocation ~/.claude/skills/capital-allocationSKILL.md
# 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:
```bash
# 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.Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags — or a results interpretation with ship/iterate/kill recommendation.
Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.
Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.