capital-allocation-0b0c97 — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited capital-allocation-0b0c97 (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
Ask for these only if they aren't already provided:
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).
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 --jsonPortfolio capital-allocation practice — expected-value × strategic-fit scoring per unit cost, against a hard constraint.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.