lilmgenius/paperthin

nba

Read the live cycle state and return the single highest-leverage next best action, not a menu.

查看源码

使用方法

  1. 复制安装命令,在自己的智能体环境安装前先检查源码包。
  2. 从原始文档核对所需工具、凭据和支持的宿主。
  3. 说明输入材料与期望交付物,并对照来源检查输出。
npx skills add https://github.com/lilmgenius/paperthin --skill nba

开始前需了解

  • 安装不包含模型订阅、API 额度或访问文件的权限。
  • 仓库星数和安装量反映活跃情况,不等于独立质量或安全测试。
仓库原始内容

按源仓库内容呈现,保留标题、案例、代码、表格、链接以及原文引用的演示图片。

Find the next best action from state, not from vibes.

Goal

nba is the manual re-entry point for a stalled cycle. It reads the current state and returns one action: the cheapest move that clears the binding constraint. It does not execute the action and it does not offer a menu, because a menu recreates the paralysis it exists to resolve.

Workflow

  1. Read the live cycle state: plan gates, re0-memo anti-patterns, priority notes, recent git/file changes, QA evidence, and the current workflow phase.
  2. Locate the project in the cycle: FRAME, BUILD, DRIVE, RE0-MEMO, HATE, RE0-WORK, BUILD AGAIN, or SHIP.
  3. Diagnose the blank-out cause: too many open threads, lost thread, unnamed blocker, closed-loop fatigue, avoidance of the hard step, or a phase that is already done.
  4. Return one action: the cheapest move that clears the most. It may name the right skill for the human to fire, the external input needed, the first nail to test, or the phase transition that is already due.
  5. Frame it as: where you are, next best action, why now, and done when.

Rules

  • One action, not a checklist.
  • State-grounded, never generic. Cite the gate, doc, evidence, or file state that drives the pick.
  • Return the avoided move, not the easy busywork.
  • If no outside truth is entering, the next action is external input, not another internal lap.
  • If the gate is already met, the action is to move phase or ship, not add another surface.
  • Read-only on the project. nba recommends; it does not execute.
  • Do not auto-invoke user-only skills. Tell the human when one is the next move.

Verification

Before finishing:

  1. The recommendation cites the state it read.
  2. There is exactly one next action.
  3. The action has a clear "done when" observable.
  4. A reader can tell why this action beats the other visible options.

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