Yao Kelly Skill

Use This Skill For

• turn "should I invest, bet, or allocate, and how much?" into a practical resource allocation plan
• decide whether the user's problem is actually suitable for Kelly-style sizing
• translate a percentage into a minimum action package with owner, metric, review window, add condition, and stop condition
• start with incomplete input, give a provisional view early, then ask only the minimum high-impact follow-up questions
• size a single bet or opportunity, or conservatively split a pool across several opportunities
• keep a round-by-round log for the current case and an append-only change log for future edits to this skill

Do Not Route Here

• pure formula tutoring, homework solving, or generic finance education
• requests for guaranteed returns, sure-win systems, or martingale-style escalation
• final licensed investment, legal, or tax advice
• leverage sizing with no bounded downside model

Default Workflow

1. Use references/intake-contract.md to identify the user's real resource pool, decision question, minimum action unit, review window, and opportunity candidates.
2. If the input is incomplete, read references/multi-turn-kelly-loop.md:
   • ask only 1-3 questions that can materially change the result
   • recalculate decision_readiness after every round
   • stop asking when the threshold is met or the action class is already stable
3. Decide whether Kelly is suitable:
   • use it when downside is bounded, the opportunity can be tested or repeated, and probabilities can be approximated
   • switch to a test-first or risk-review answer when the decision is irreversible, one-off, or has unbounded downside
4. Use references/kelly-sizing-playbook.md to choose the formula path:
   • binary opportunity: standard Kelly closed form
   • scenario-based opportunity: maximize E[log(1 + f * r)]
   • multiple opportunities: compute standalone Kelly first, then apply fractional Kelly, dependence haircuts, and total exposure scaling
5. Run scripts/kelly_allocation_report.py for canonical JSON sizing output.
6. Run scripts/generate_html_report.py when the user wants a polished standalone HTML report or PDF-ready artifact.
7. Use references/output-contract.md to produce a practical allocation report:
   • resource snapshot
   • fit assessment
   • minimum action packages
   • Kelly sizing cap
   • add, stop, and review conditions
8. Use references/logging-contract.md to maintain:
   • the case round log for the current user request
   • the append-only iteration log in history/CHANGELOG.md whenever this skill package changes
9. Apply references/safety-and-scope.md before finalizing.

Core Rules

• default to fractional Kelly, not full Kelly
• never make the formula the main product; the main product is a resource allocation action plan
• mark each key number as observed, estimated, or assumed
• if correlation across opportunities is unknown, shrink exposure instead of assuming independence
• if the edge is negative, fragile, or mostly assumption-driven, recommend no allocation, observe, or run a cheap test first
• always translate the final fraction into the smallest next action the user can actually do
• include add, stop, and review conditions so the allocation can improve after real feedback
• stop asking once more questions are unlikely to change the action class
• every future edit to this skill must append a dated note to history/CHANGELOG.md

Output Contract

• deliver a Kelly application report, not just a formula
• prefer HTML + JSON when the user wants a report artifact; use JSON as the audit source and HTML as the readable hand-back
• the report must include:
  • recommendation summary and action class
  • Kelly fit assessment
  • current capital or resource base, protected reserve, risk budget, and translated amount
  • minimum action package per opportunity
  • full Kelly fraction and conservative Kelly execution cap
  • add, stop, and review conditions
  • formula path and key assumptions
  • why the questioning stopped
  • round log