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Agent Skill · 研究检索

mlb-decision-logger(美国职棒大联盟决策记录器)

mlb-decision-logger 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

mlb-decision-logger 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 71,089。

总安装 196周安装 8GitHub Stars 85下载 63核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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-decision-logger(美国职棒大联盟决策记录器)
来源仓库:https://github.com/lyndonkl/claude
仓库路径:skills/mlb-decision-logger
安装命令:
npx skills add https://github.com/lyndonkl/claude --skill mlb-decision-logger
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-decision-logger

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

mlb-decision-logger 用于查找、检索和筛选相关信息,适合决策过程追溯。适用于 Codex、Claude、Cursor、Gemini CLI 中的管理日志分析场景。通过 npx skills add 命令从 claude 仓库安装。使用前需评估日志脱敏策略及审计追踪完整性。

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。

来源仓库内路径:skills/mlb-decision-logger

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/lyndonkl/claude --skill mlb-decision-logger;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

mlb-decision-logger 怎么安装?

在终端执行 npx skills add https://github.com/lyndonkl/claude --skill mlb-decision-logger。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

mlb-decision-logger 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

mlb-decision-logger 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

mlb-decision-logger 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

mlb-decision-logger 用的人多吗?

公开数据是累计安装 196 次,近一周安装 8 次,下载 63 次,GitHub 85 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

mlb-decision-logger 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
mlb-decision-logger

美国职棒大联盟决策记录器 · 本页

19685只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

MLB Decision Logger

Table of Contents

Example

Scenario: Three agents fire in a single morning brief on 2026-04-17. Each agent hands a decision payload to this skill. On Monday 2026-04-21, the coach runs the calibration pass over Friday's lineup decision.

Sequence:

  1. mlb-lineup-optimizer submits a start/sit decision for Junior Caminero.
  2. mlb-waiver-analyst submits a FAAB bid on a two-start streamer.
  3. mlb-streaming-strategist submits a stream pickup.

Skill behavior:

  • Reads the current tail of tracker/decisions-log.md.
  • Auto-assigns decision_id by counting same-day, same-type entries and incrementing (2026-04-17-lineup-01, 2026-04-17-waiver-01, 2026-04-17-stream-01).
  • Validates each payload against the schema in context/frameworks/decision-log-format.md.
  • Appends entries in timestamp order, one at a time, re-reading the tail between writes.
  • Returns the assigned decision_id to the calling agent.

Monday calibration (2026-04-21):

  • Reads all entries where will_verify_on <= 2026-04-21 and outcome_recorded_on is empty.
  • For each entry, agent web-searches the outcome and passes result to this skill.
  • Skill edits the target entry in place (only the three outcome fields) and increments the matching row in tracker/variant-scoreboard.md.
  • Recomputes tilt per agent using the advocate/critic counts over the most recent 20 verified decisions.

Worked entry appended during step 1:

### 2026-04-17T08:30:00Z | lineup | mlb-lineup-optimizer
- **decision_id:** 2026-04-17-lineup-01
- **recommendation:** START Caminero at 3B
- **signals_in:** caminero.daily_quality=78 (matchup=85, form=72, opportunity=80)
- **variants:**
    - advocate -> "Start: strong matchup vs RHP, hitter-friendly park, batting 3rd"
    - critic -> "Sit: 0-for-12 last 3 games, BABIP-driven regression due"
- **dialectical_synthesis:** Advocate wins -- matchup+opportunity outweigh slump. Confidence 0.72.
- **red_team_findings:**
    - severity: 2, likelihood: 3, score: 6, note: "MIA rain watch", mitigation: "Check 1pm forecast"
- **confidence:** 0.72
- **will_verify_on:** 2026-04-18
- **outcome_recorded_on:**
- **outcome:**
- **variant_that_was_right:**

After Monday calibration the last three fields are filled in; the scoreboard row for mlb-lineup-optimizer increments Total decisions, Advocate correct (if 1-for-4 with an RBI counted as success), and Synthesis correct; Tilt is recomputed.

Workflow

Copy this checklist and track progress for each invocation:

Decision Logger Progress:
- [ ] Step 1: Determine mode (append vs calibrate)
- [ ] Step 2: Read current log tail
- [ ] Step 3: Validate payload against schema
- [ ] Step 4: Serialize write (append or in-place edit)
- [ ] Step 5: Update scoreboard (calibration mode only)
- [ ] Step 6: Return decision_id and confirmation

Step 1: Determine mode

Two supported modes. Exactly one fires per invocation.

  • append -- calling agent passes a complete decision payload. Skill creates a new entry.
  • calibrate -- calling agent passes an existing decision_id plus outcome fields. Skill edits that entry in place and updates the scoreboard.

See resources/methodology.md for the full decision tree.

Step 2: Read current log tail

Always re-read the log immediately before any write. This is how concurrent writes from parallel agents are serialized without a real file lock.

  • Read the last ~80 lines of tracker/decisions-log.md.
  • Parse the last entry's timestamp and decision_id.
  • If another agent has written since this invocation started, note new decision_id numbers used today.

See resources/methodology.md for the full serialization protocol.

Step 3: Validate payload against schema

Check every required field. Reject (do not write) if any are missing or malformed. The authoritative schema is in context/frameworks/decision-log-format.md; resources/template.md mirrors it verbatim.

  • timestamp_iso8601 is UTC ISO 8601 (YYYY-MM-DDTHH:MM:SSZ).
  • decision_type in enum: lineup | waiver | stream | trade | category-plan | playoff-push | add-drop | ad-hoc.
  • emitted_by is a known agent name.
  • recommendation starts with an action verb (START, SIT, ADD, DROP, BID $X, ACCEPT, COUNTER, REJECT, STREAM, HOLD).
  • signals_in references at least one signal by name.
  • variants has both advocate and critic entries (or explicit n/a for bootstrap).
  • confidence in [0.00, 1.00].
  • will_verify_on is a date, end of week N, or end of season.
  • For calibrate mode: outcome in {happened, did not happen, partial}, variant_that_was_right in {advocate, critic, both, neither}.

Step 4: Serialize write

  • append: assign decision_id using format {YYYY-MM-DD}-{decision_type}-{NN} where NN is last_same_type_same_day_index + 1, zero-padded to 2 digits. Append the formatted entry plus a trailing separator line.
  • calibrate: locate the target entry by decision_id, replace only the three outcome fields, preserve everything else byte-for-byte.
  • Never overwrite the full file. Never reorder entries. Never edit fields other than the three outcome fields.

See resources/methodology.md.

Step 5: Update scoreboard (calibration mode only)

  • Read tracker/variant-scoreboard.md.
  • Find the row for the entry's emitted_by agent.
  • Increment Total decisions by 1.
  • Increment Advocate correct, Critic correct, or both (for variant_that_was_right = both). Increment Synthesis correct when the synthesized recommendation matched reality.
  • Recompute Tilt per the rules in resources/methodology.md.
  • Write updated table back; leave all other text unchanged.

Step 6: Return decision_id and confirmation

  • Return assigned decision_id (append) or confirmation of calibrated fields (calibrate).
  • If validation failed, return structured error with field name and reason; do not write.
  • Validate the final output using resources/evaluators/rubric_mlb_decision_logger.json. Minimum standard: average score 3.5+, no criterion below 2.

Common Patterns

Pattern 1: Parallel agent append (morning brief)

Coach launches lineup-optimizer, waiver-analyst, and streaming-strategist in one run. Each completes its variant pair, synthesizes, and calls this skill. Skill processes them serially in arrival order, re-reading the log tail between each write, assigning unique decision_ids per type.

Pattern 2: Monday calibration pass

Coach opens the log, filters for entries where will_verify_on <= today and outcome_recorded_on is empty, and for each one: web-searches the outcome, builds the calibration payload, calls this skill in calibrate mode. Each call edits one entry and updates one scoreboard row.

Pattern 3: Trade decision (on-demand, deferred verification)

mlb-trade-analyzer fires when a trade offer arrives. The decision is REJECT, ACCEPT, or COUNTER. will_verify_on is typically end of week N or end of season because trade value plays out over weeks. Skill logs normally; the trade stays "open" on the calibration queue until its verify date.

Pattern 4: Bootstrap / ad-hoc entry

For team initialization or meta-decisions (changing a weighting, amending a framework), decision_type = ad-hoc or bootstrap, variants = n/a, variant_that_was_right = neither. Skill accepts this as a special shape.

Guardrails

  1. Append only. Never rewrite the log. Never reorder entries. The only edit allowed is filling in the three outcome fields on an existing entry during calibration. Any other mutation is a bug.
  2. Re-read before every write. Parallel agents share one file. Reading the tail immediately before writing is the serialization primitive. Do not cache the tail from earlier in the run.
  3. Validate before writing. A malformed entry poisons downstream calibration. Reject with a structured error; do not attempt partial writes or auto-fix missing fields.
  4. decision_id is deterministic. Same day, same type, sequential NN. If another agent has already used 2026-04-17-lineup-01, this agent gets 02. Never reuse an id.
  5. Outcome-editing is surgical. When filling outcome fields, replace only the three target lines. Preserve timestamps, decision_id, recommendation, variants, synthesis, red-team, confidence, and will_verify_on byte-for-byte. If you have to reformat to write, you are doing it wrong.
  6. Scoreboard updates are idempotent per decision. Each decision_id contributes exactly one row increment. If a calibration call is retried, re-check whether the entry already has outcome_recorded_on populated; if so, do not double-count.
  7. Tilt needs sample size. Report neutral when Total decisions < 10 for the agent. Only switch to advocate+ / critic+ once both sample size and margin are met. See resources/methodology.md for exact thresholds.
  8. Agents never write to the log directly. Every MLB agent routes through this skill. If the coach finds a malformed entry not produced here, treat it as a bug and log it under tracker/calibration-review.md.

Quick Reference

Mode inputs:

  • append needs: timestamp_iso8601, decision_type, emitted_by, recommendation, signals_in, variants (advocate + critic), dialectical_synthesis, red_team_findings, confidence, will_verify_on.
  • calibrate needs: decision_id, outcome_recorded_on, outcome, variant_that_was_right.

decision_id format:

{YYYY-MM-DD}-{decision_type}-{NN}
e.g., 2026-04-17-lineup-01

Tilt thresholds (per agent, over last 20 verified decisions):

ConditionTilt
Total < 10 verifiedneutral
Advocate correct > Critic correct by 10 to 20 pct pointsadvocate+
Advocate correct > Critic correct by 20+ pct pointsadvocate++
Critic correct > Advocate correct by 10 to 20 pct pointscritic+
Critic correct > Advocate correct by 20+ pct pointscritic++
Within 10 pct pointsneutral

Files touched:

  • ~/Documents/Projects/yahoo-mlb/tracker/decisions-log.md (append, or surgical edit of outcome fields)
  • ~/Documents/Projects/yahoo-mlb/tracker/variant-scoreboard.md (row increments, tilt recompute)
  • ~/Documents/Projects/yahoo-mlb/tracker/calibration-review.md (written only when high-confidence decision was wrong)

Files read:

  • ~/Documents/Projects/yahoo-mlb/context/frameworks/decision-log-format.md (schema of record)
  • ~/Documents/Projects/yahoo-mlb/tracker/decisions-log.md (tail, for serialization and id assignment)
  • ~/Documents/Projects/yahoo-mlb/tracker/variant-scoreboard.md (for calibration updates)

Key resources:

Inputs required:

  • From calling agent (append): complete decision payload as above.
  • From calling agent (calibrate): decision_id and verified outcome fields.

Outputs produced:

  • Append mode: assigned decision_id, confirmation that entry is in the log.
  • Calibrate mode: confirmation of which entry was updated and the scoreboard row that changed.
  • On validation failure: structured error (field name, reason, offending value), no write performed.

平台分布

Codex

37.91%
按下载量换算24

Claude

29.53%
按下载量换算19

Cursor

19.33%
按下载量换算12

Gemini CLI

10.52%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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