Turn uncertain resource-allocation requests into practical action plans using Kelly sizing as a conservative allocation engine. Use when a user needs to decide whether an opportunity is suitable for Kelly, what minimum action package to run, how much resource to cap, when to add or stop, and how to review results. Do not use for pure formula tutoring, guaranteed-return claims, martingale escalation, or final licensed investment, legal, or tax advice.
インストール
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Turn uncertain resource-allocation requests into practical action plans using Kelly sizing as a conservative allocation engine. Use when a user needs to decide whether an opportunity is suitable for Kelly, what minimum action package to run, how much resource to cap, when to add or stop, and how to review results. Do not use for pure formula tutoring, guaranteed-return claims, martingale escalation, or final licensed investment, legal, or tax advice.
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
Use references/intake-contract.md to identify the user's real resource pool, decision question, minimum action unit, review window, and opportunity candidates.
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
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
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
Run scripts/kelly_allocation_report.py for canonical JSON sizing output.
Run scripts/generate_html_report.py when the user wants a polished standalone HTML report or PDF-ready artifact.
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
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
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
