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mlb-faab-sizerMLB FAAB 大小

Agent Skill

mlb-faab-sizer 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

188

周安装

8

GitHub Stars

85

下载量

66
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mlb-faab-sizer(MLB FAAB 大小)
来源仓库:https://github.com/lyndonkl/claude
仓库路径:skills/mlb-faab-sizer
安装命令:
npx skills add https://github.com/lyndonkl/claude --skill mlb-faab-sizer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/lyndonkl/claude --skill mlb-faab-sizer

简介

mlb-faab-sizer 用于处理 GitHub 仓库、Issue 等协作信息,适合预算分配计算。

  • 适用于 Codex、Claude、Cursor、Gemini CLI 中的财务规划支持场景。
  • 使用 npx skills add 命令从 claude 仓库安装。
  • 安装前应核实计算公式版本及历史数据兼容性。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

MLB FAAB Sizer

Table of Contents

Delegation Chain

This skill is a baseball-specific orchestrator. It does NOT compute auction math inline. It composes two domain-neutral sibling skills:

StepWhoResponsibility
1this skillCompute base_value from Yahoo-adjusted projection, pos fit, role certainty, urgency, season pace (with league-inflation calibration)
2this skillClassify target as common_value / private_value / mixed
3auction-winners-curse-haircutReturn adjusted_valuation (Bayesian haircut for common-value)
4this skillEstimate N from opponent profiles
5auction-first-price-shadingReturn shaded_bid ((N-1)/N + distribution + risk adjustment)
6this skillApply baseball guardrails (April 40%, speculation 20%, $1 floor, role-cert floor)
7this skillEmit faab_rec_bid, faab_max_bid, rationale naming both delegations

Invariant: this skill never computes (N-1)/N or a common-value haircut directly. Any change to those primitives is made in the sibling skills.

Example

Scenario: Roki Sasaki called up, likely Dodgers rotation spot. $100 FAAB remaining, week 4.

  1. base_value (this skill): 28 x 0.70 x 0.65 x 1.2 x 0.7 = $10.70
  2. Classify: common_value (headline prospect).
  3. Invoke auction-winners-curse-haircut with (raw=10.70, type=common_value, N=6, dispersion=60) -> adjusted_valuation = $7.39, haircut 31%.
  4. N estimate: 6 opponents have SP need + budget.
  5. Invoke auction-first-price-shading with (true_value=7.39, N=6, dist=log-normal, risk=0.2, budget=100) -> shaded_bid = $7, shade 0.90.
  6. Baseball guardrails: all clear.
  7. Output: faab_rec_bid=$7, faab_max_bid=round($7.39 x 0.90)=$7. Rationale cites both sibling skills.

Full trace in resources/methodology.md and resources/template.md.

Workflow

FAAB Sizing Progress:
- [ ] Step 1: Collect input signals and budget state
- [ ] Step 2: Compute base_value (baseball layering)
- [ ] Step 3: Classify value_type
- [ ] Step 4: Invoke auction-winners-curse-haircut -> adjusted_valuation
- [ ] Step 5: Estimate N from opponent profiles
- [ ] Step 6: Invoke auction-first-price-shading -> shaded_bid
- [ ] Step 7: Apply baseball guardrails
- [ ] Step 8: Emit signal + rationale with delegation trace

Step 1: Collect inputs (ask caller if missing):

  • acquisition_value ($, 1-100 scale) -- from mlb-player-analyzer
  • positional_need_fit (0-100) -- from mlb-waiver-analyst
  • role_certainty (0-100) -- from mlb-player-analyzer
  • FAAB remaining, week number, situation label

Step 2: Compute base_value (baseball-specific layering):

base_value = acquisition_value
           x (positional_need_fit / 100)
           x (role_certainty / 100)
           x urgency_multiplier          [0.7 - 1.4]
           x season_pace_multiplier      [0.6 - 1.4 after inflation calib]

Urgency: 1.4 (closer loses job / prospect called up); 1.2 (new opportunity); 1.0 (steady); 0.8 (wave); 0.7 (speculation).

Pace base: 0.6 (Apr wk 1-4), 1.0 (May-Jun), 1.2 (Jul-Aug), 1.4/0.5 (Sept contending/eliminated). Then multiply by league_inflation_ratio from tracker/faab-log.md (see methodology.md). Min 5 valid rows; else skip calibration and flag low_calibration_data.

Step 3: Classify value_type:

  • common_value: headline prospect, named closer, star off IL (same info for all teams)
  • private_value: handcuff, platoon fit, punt-category-specific (only we weigh this way)
  • mixed: record common/private weight

Default to common_value when uncertain (conservative; triggers haircut).

Step 4: Invoke auction-winners-curse-haircut:

inputs = { raw_valuation: base_value, value_type, n_informed_bidders: N,
           signal_dispersion: 40 (default) }

Consume adjusted_valuation. Preserve classification_rationale for output. Dispersion defaults: 60 for prospects, 30 for established players, 40 otherwise.

Step 5: Estimate N from opponent profiles (teams with positional_need > 50, faab > 20% original, activity >= moderate). Clamp [1, 8]. Defaults: common superstar 6, common role-player 3, private 1-2.

Step 6: Invoke auction-first-price-shading:

inputs = { true_value: adjusted_valuation, n_bidders_estimate: N,
           value_distribution: "log-normal" (MLB default),
           risk_aversion: 0.2 (bump to 0.4 for contending September),
           budget_remaining: faab_remaining }

shaded_bid becomes pre-guardrail faab_rec_bid. Set faab_max_bid = round(adjusted_valuation x 0.90).

Step 7: Apply baseball guardrails (see below). Never silently violate.

Step 8: Emit via mlb-signal-emitter. User-facing rationale MUST name both sibling skills by purpose. Validate with rubric. Minimum 3.5.

Common Patterns

1. Hot common-value call-up (early season): N=5-7, pace 0.6-0.7, haircut ~25-30%, shade ~0.80-0.85. Typical $5-$12 rec.

2. Private-value handcuff: N=1-2, haircut=0 (short-circuit), shade 0.0-0.5. Typical $1-$3 rec.

3. Closer change (mid-season, common-value): N=5-8, urgency 1.4, haircut ~25%, shade ~0.83. Typical $10-$30 rec.

4. September contender stretch target: pace 1.4, risk_aversion bumped to 0.4, shade ~0.88-0.92. Can reach 40-60% of remaining FAAB.

Guardrails

  1. April 40% cap: weeks 1-13, faab_max_bid ≤ 40% of FAAB remaining. Flag april_40pct_cap_triggered.
  2. Speculation 20% cap: if situation=speculation or role_certainty<30, cap at 20%. Flag speculation_20pct_cap_triggered.
  3. $1 floor: if shaded_bid rounds to $0 but positional_need_fit >= 30, bid $1 (rolling-list tiebreak). Otherwise bid $0 and flag zero_bid_preservation.
  4. Role certainty floor: if role_certainty < 20, force faab_rec_bid = $0. Flag role_certainty_floor.
  5. Regression override: if regression_index < -30, cut faab_rec_bid by 30%. Flag regression_luck_discount.
  6. Variant divergence: advocate/critic differ >30% -> take critic. Flag variant_divergence_applied.
  7. Budget floor (post-July): if week >= 14 and remainder < $5, flag budget_floor_near_zero.
  8. Log the decision: every computation via mlb-decision-logger (including $0 bids).

Do NOT duplicate sibling caps: the 0.9 x true_value ceiling is enforced by auction-first-price-shading; the 35% haircut cap is enforced by auction-winners-curse-haircut. Trust them.

Quick Reference

Pipeline:

base_value = acq_value x (pos_fit/100) x (role_cert/100) x urgency x pace_calibrated

adjusted_valuation = auction-winners-curse-haircut(
    raw_valuation=base_value, value_type, n_informed_bidders=N, signal_dispersion)

shaded_bid = auction-first-price-shading(
    true_value=adjusted_valuation, n_bidders_estimate=N,
    value_distribution="log-normal", risk_aversion=0.2, budget_remaining)

faab_rec_bid = round(shaded_bid)          # then baseball guardrails
faab_max_bid = round(adjusted_valuation x 0.90)

Inputs required: acquisition_value, positional_need_fit, role_certainty, FAAB remaining, week, situation label, regression_index (optional).

Outputs: faab_rec_bid, faab_max_bid, value_type, N, adjusted_valuation, shaded_bid, multipliers, guardrail flags, user-facing rationale.

Sibling skills:

  • @skills/auction-first-price-shading/ -- (N-1)/N + distribution + risk-aversion
  • @skills/auction-winners-curse-haircut/ -- Bayesian common-value haircut

Key resources:

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安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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