跳到正文
cccskills
免费在 GitHub 公开

plan-change

Turn a feature request into a minimal implementation plan before coding. Identify which files need changes, explain why, define the smallest required interface, and flag broader effects or expanded permissions for the implementer.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Turn a feature request into a minimal, file-level implementation plan before any code.

查看安装方法

适用场景

  • Plan a feature before coding
  • Identify files affected by a change
  • Review scope and permission changes
  • Prepare a handoff for implementation

包含的文件(1)

  • SKILL.md463 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

plan-change

Produce an implementation plan for a requested change.

  1. Restate the goal in one sentence.
  2. List the files to touch and why.
  3. Name the smallest interface that satisfies it.
  4. Flag anything that ripples beyond three files or widens a permission.

Hand the plan to the implementer; do not write code in this step.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

中文简介正在准备中。当前显示英语简介。

  • Sharing stored patterns across multiple AgentDB instances
  • Filtering semantic search by date or category
  • Separating knowledge and conversation databases
ruvnet/RuView9.9万2026年10月19日 更新

Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.

中文简介正在准备中。当前显示英语简介。

  • Train agents from historical experience
  • Build decision-making for games and navigation
  • Combine experience from multiple agents for training
ruvnet/RuView9.9万2026年10月19日 更新

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

中文简介正在准备中。当前显示英语简介。

  • Add conversation history to a chat system
  • Remember user preferences across sessions
  • Reuse patterns from successful interactions
ruvnet/RuView9.9万2026年10月19日 更新

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

中文简介正在准备中。当前显示英语简介。

  • Reduce memory use in an AgentDB application
  • Tune search speed while preserving accuracy
  • Plan settings for millions of vectors
ruvnet/RuView9.9万2026年10月19日 更新

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

中文简介正在准备中。当前显示英语简介。

  • Build answers grounded in retrieved documents
  • Add meaning-based search to a knowledge base
  • Find documents with similar content
ruvnet/RuView9.9万2026年10月19日 更新

browser

免费

Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.

中文简介正在准备中。当前显示英语简介。

  • Automating login flows
  • Filling and submitting web forms
  • Extracting product names, prices, and links
ruvnet/RuView9.9万2026年10月19日 更新

查看 ruvnet 的全部技能

报告此技能的问题