yao-kelly-skill

Files (28)

• README.md
• SKILL.md
• interface.yaml · agents/
• CHANGELOG.md · history/
• README.md · logs/
• manifest.json
• intake-contract.md · references/
• kelly-sizing-playbook.md · references/
• logging-contract.md · references/
• multi-turn-kelly-loop.md · references/
• output-contract.md · references/
• safety-and-scope.md · references/
• design-summary.md · reports/
• engineering-hours-brief.json · reports/
• engineering-hours-output.json · reports/
• engineering-hours-report.html · reports/
• example-brief.json · reports/
• example-html-report.html · reports/
• example-output.json · reports/
• internet-product-sprint-brief.json · reports/
• internet-product-sprint-output.json · reports/
• internet-product-sprint-report.html · reports/
• marketing-budget-brief.json · reports/
• marketing-budget-output.json · reports/
• marketing-budget-report.html · reports/
• append_iteration_log.py · scripts/
• generate_html_report.py · scripts/
• kelly_allocation_report.py · scripts/

SKILL.md (first lines)

# 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

From yaojingang/yao-open-skills, MIT.

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