knowledge-consolidation

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Knowledge Consolidation

知识整合

Persist valuable knowledge from AI conversations into structured documents.

将AI对话中有价值的知识持久化保存为结构化文档。

When to Invoke

触发时机

Proactive (AI self-invoke):

Starting new session after substantial prior work
Accumulated debugging/architecture/pattern knowledge worth preserving
Resolved non-trivial issues with reusable insights

Skip when:

Simple Q&A with no novel insights
Trivial changes with nothing worth preserving

主动触发（AI自动调用）：

在完成大量前期工作后开启新会话
积累了值得留存的调试/架构/模式相关知识
解决了具有可复用见解的复杂问题

以下情况跳过触发：

无新颖见解的简单问答
无留存价值的微小变更

Workflow

工作流程

Step 1: Detect AI IDE Type

步骤1：检测AI IDE类型

Check project root for AI IDE indicator directory:

Environment Indicator	AI Type	Storage Path
`.trae/` dir or TraeAI/TraeCN env	`trae-cn`	`.trae/knowledges/`
`.claude/` dir or Claude Code env	`claude-code`	`.claude/knowledges/`
`.cursor/` dir	`cursor`	`.cursor/knowledges/`
`.windsurf/` dir	`windsurf`	`.windsurf/knowledges/`

环境标识	AI类型	存储路径
`.trae/` 目录或TraeAI/TraeCN环境	`trae-cn`	`.trae/knowledges/`
`.claude/` 目录或Claude Code环境	`claude-code`	`.claude/knowledges/`
`.cursor/` 目录	`cursor`	`.cursor/knowledges/`
`.windsurf/` 目录	`windsurf`	`.windsurf/knowledges/`

Step 2: Identify Consolidation Candidates

步骤2：筛选知识整合候选内容

Scan conversation for knowledge worth preserving:

Debug sessions with root cause analysis
Architecture decisions with rationale
Code patterns discovered or established
Configuration knowledge (build, env, tooling)
API integration insights
Workflow improvements
Lessons learned from issues

扫描会话内容，筛选值得留存的知识：

包含根因分析的调试会话
带有决策依据的架构方案
已发现或确立的代码模式
配置相关知识（构建、环境、工具链）
API集成见解
工作流程优化方案
从问题中总结的经验教训

Step 3: Classify Knowledge Type

步骤3：知识类型分类

Select appropriate type based on content:

Type	When to Use
`debug`	Bug fixes, crash analysis, error resolution
`architecture`	System design, module structure, dependencies
`pattern`	Reusable code patterns, idioms, best practices
`config`	Build settings, environment, tooling setup
`api`	API design, integration, protocol details
`workflow`	Development processes, procedures
`lesson`	Post-mortems, retrospectives, project insights
`reference`	Technical specs, schema docs, standards

See knowledge-types.md for detailed guidance on each type.

根据内容选择合适的类型：

类型	使用场景
`debug`	漏洞修复、崩溃分析、错误排查
`architecture`	系统设计、模块结构、依赖关系
`pattern`	可复用代码模式、编程范式、最佳实践
`config`	构建设置、环境配置、工具链搭建
`api`	API设计、集成实现、协议细节
`workflow`	开发流程、操作规范
`lesson`	事后复盘、经验总结、项目洞察
`reference`	技术规范、Schema文档、标准约定

各类型的详细说明请参考knowledge-types.md。

Step 4: Generate Document Path

步骤4：生成文档路径

Run path generation script:

bash

{skill_root}/scripts/get-knowledge-path.sh \
  -r <project_root> -a <ai_type> -t <type> -n <filename>

Output format:

{project_root}/{ai_path}/knowledges/{YYYYMMDD}_{seq}_{type}_{filename}.md

运行路径生成脚本：

bash

{skill_root}/scripts/get-knowledge-path.sh \
  -r <project_root> -a <ai_type> -t <type> -n <filename>

输出格式：

{project_root}/{ai_path}/knowledges/{YYYYMMDD}_{seq}_{type}_{filename}.md

Step 5: Write Knowledge Document

步骤5：撰写知识文档

Create document using template:

markdown

undefined

使用模板创建文档：

markdown

undefined

{Title}

{标题}

Type: {type} Date: {YYYY-MM-DD} Context: {Brief context}

类型: {type} 日期: {YYYY-MM-DD} 背景概述: {简短背景}

Summary

内容摘要

{2-3 sentence summary}

{2-3句话的摘要}

Background

详细背景

{Situation/problem/context}

{场景/问题/背景}

Details

详细内容

{Technical content, code snippets, analysis}

{技术内容、代码片段、分析过程}

Key Takeaways

核心要点

{Bullet points of actionable insights}

{可执行见解的要点列表}

Resources

资源列表

Resource	Purpose
`scripts/get-knowledge-path.sh`	Generate timestamped file path
`references/knowledge-types.md`	Detailed type selection guide
`assets/knowledge.md.template`	Document template
`examples/debug-session.md`	Complete workflow example

资源路径	用途
`scripts/get-knowledge-path.sh`	生成带时间戳的文件路径
`references/knowledge-types.md`	详细的类型选择指南
`assets/knowledge.md.template`	知识文档模板
`examples/debug-session.md`	完整工作流程示例