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上下文迁移

Context Migration

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中文译文

上下文保留与迁移提示词

[ 对于 AGENT.MD，如果 ## SECTION 不适用，则跳过该部分 ]

生成一份全面的上下文产物，保留所有对话上下文、进度、决策和项目结构，以便在 AI 会话、平台或智能体之间无缝延续。该产物充当一个“上下文 U 盘”，使任何 AI 都能立即理解并继续工作，而无需重复说明或丢失上下文。

核心目标

捕获并结构化当前会话中的所有上下文要素，以实现：

  1. 会话延续 - 在不同 AI 平台之间恢复对话，无需重新解释
  2. 智能体交接 - 将未完成的任务移交给新的智能体，并附上完整的进度文档
  3. 项目迁移 - 复制整个项目文化、工作流和治理结构

需要保留的内容类别

对话上下文

  • 初始需求和不断演进的用户故事
  • 头脑风暴期间产生的想法
  • 做出的决策及其完整的理由链
  • 达成的共识及其验证状态
  • 建议和推荐及其支撑上下文
  • 已确立的假设及其当前状态
  • 关键洞察和突破性时刻
  • 作为结构基础的关键要点

进度文档

  • 所有工作流的当前状态
  • 已完成的任务和交付物
  • 待办事项和下一步行动
  • 遇到的阻碍及缓解策略
  • 触及的速率限制及变通解决方案
  • 重要里程碑的时间线

项目架构（如适用）

  • SDLC 方法论和阶段
  • 智能体生态系统（主智能体、子智能体、同级智能体、观察者智能体）
  • 规则、治理策略和战略
  • 仓库结构（.github 工作流、模板）
  • 可复用的提示词表单（史诗拆解、PRD、架构计划、系统设计）
  • 约定模式（提交格式、记忆提示词、日志结构）
  • 指令层级（项目级、冲刺级、史诗级变体）
  • CI/CD 配置（测试、格式化、提交提取）
  • 多智能体编排（提示词链、并行化、路由智能体）
  • 输出格式标准及变体

规则与协议

  • 已确立的指导方针及范围定义
  • 会话期间添加的附加指令
  • 已设定的约束与边界
  • 质量标准与验收标准
  • 用于保持工作不偏离轨道的对齐机制

步骤

  1. 扫描对话历史 - 审查整个线程/会话中的所有交互与上下文
  2. 提取核心要素 - 按上述内容类别识别并归类信息
  3. 记录进度状态 - 记录已完成、进行中和待处理的内容
  4. 保留决策链 - 包含所有重要选择背后的推理
  5. 为可移植性而结构化 - 以普遍可解释的格式组织
  6. 添加交接说明 - 包含面向下一个 AI/智能体/会话的明确指引

输出格式

生成一份结构化 markdown 文档，包含以下部分：

# CONTEXT ARTIFACT: [Session/Project Title]
**Generated**: [Date/Time]
**Source Platform**: [AI Platform Name]
**Continuation Priority**: [Critical/High/Medium/Low]

## SESSION OVERVIEW
[2-3 sentence summary of primary goals and current state]

## CORE CONTEXT
### Original Requirements
[Initial user requests and goals]

### Evolution & Decisions
[Key decisions made, with rationale - bulleted list]

### Current Progress
- Completed: [List]
- In Progress: [List with % complete]
- Pending: [List]
- Blocked: [List with blockers and mitigations]

## KNOWLEDGE BASE
### Key Insights & Agreements
[Critical discoveries and consensus points]

### Established Rules & Protocols
[Guidelines, constraints, standards set during session]

### Assumptions & Validations
[What's been assumed and verification status]

## ARTIFACTS & DELIVERABLES
[List of files, documents, code created with descriptions]

## PROJECT STRUCTURE (if applicable)
### Architecture Overview
[SDLC, workflows, repository structure]

### Agent Ecosystem
[Description of agents, their roles, interactions]

### Reusable Components
[Prompt templates, workflows, automation scripts]

### Governance & Standards
[Instructions hierarchy, conventional patterns, quality gates]

## HANDOFF INSTRUCTIONS
### For Next Session/Agent
[Explicit steps to continue work]

### Context to Emphasize
[What the next AI must understand immediately]

### Potential Challenges
[Known issues and recommended approaches]

## CONTINUATION QUERY
[Suggested prompt for next AI: "Given this context artifact, please continue by..."]

示例

示例 1：会话连续性（头脑风暴交接）

输入：“我们已经为一个移动应用头脑风暴了 2 个小时。我需要切换到 Claude。生成上下文产物。”

输出：

# CONTEXT ARTIFACT: FitTrack Mobile App Planning
**Generated**: 2026-01-07 14:30
**Source Platform**: Google Gemini
**Continuation Priority**: High

## SESSION OVERVIEW
Brainstormed fitness tracking mobile app for busy professionals. Decided on minimalist design with AI coaching. Ready for technical architecture phase.

## CORE CONTEXT
### Original Requirements
- Target users: Working professionals 25-40, limited gym time
- Must sync with Apple Watch and Fitbit
- Budget: $50k for MVP
- Timeline: 3 months to launch

### Evolution & Decisions
- ✓ Name: "FitTrack Pro" (rejected: "QuickFit", "PowerHour")
- ✓ Core feature: 15-min AI-personalized workouts (not generic plans)
- ✓ Monetization: Freemium ($9.99/mo premium)
- ✓ Tech stack: React Native (for iOS+Android simultaneously)

### Current Progress
- Completed: Feature prioritization, user personas, monetization model
- In Progress: None yet
- Pending: Technical architecture, database schema, API design
- Blocked: None

## KNOWLEDGE BASE
### Key Insights & Agreements
- Users want "smart short" over "long complete" - brevity is premium feature
- AI coaching must feel conversational, not robotic commands
- Social features deferred to v2 to avoid scope creep

(... rest of structure follows)

## CONTINUATION QUERY
"Given this context artifact for FitTrack Pro app planning, please design the technical architecture including database schema, API endpoints, and third-party integration strategy for Apple Watch and Fitbit."

示例 2：Agent 交接（限流自动化）

输入：“浏览器 Agent 在抓取竞品定价时触发了速率限制。生成交接文档。”

输出：

# CONTEXT ARTIFACT: Competitor Pricing Automation (Incomplete)
**Generated**: 2026-01-07 09:15
**Source Platform**: Browser Agent v2.1
**Continuation Priority**: Critical

## SESSION OVERVIEW
Automated scraping of 50 competitor websites for pricing comparison. Completed 32/50 before rate limiting. Need immediate continuation to meet Friday deadline.

## CORE CONTEXT
### Original Requirements
- Scrape pricing for "wireless earbuds under $100" from 50 e-commerce sites
- Extract: product name, price, rating, review count
- Output: Single CSV for analysis
- Deadline: Friday 5pm

### Evolution & Decisions
- ✓ Added retry logic after initial failures on JS-heavy sites
- ✓ Switched to headless Chrome (from requests library) for better compatibility
- ✓ Implemented 3-second delays between requests per domain
- ✓ User added instruction: "Skip sites requiring login"

### Current Progress
- Completed: 32/50 sites successfully scraped (2,847 products)
- In Progress: None (halted at rate limit)
- Pending: 18 sites remaining (list in "Continuation Query" below)
- Blocked: Rate limited on domains: amazon.com, walmart.com, target.com (need 2-hour cooldown)

## KNOWLEDGE BASE
### Established Rules & Protocols
- Respect robots.txt without exception
- Max 1 request per 3 seconds per domain
- Skip products with no reviews (noise in data)
- Handle pagination up to 5 pages max per site

### Challenges & Mitigations
- Challenge: Dynamic pricing (changes during scraping)
  Mitigation: Timestamp each entry
- Challenge: Anti-bot CAPTCHAs on 3 sites
  Mitigation: User approved manual data entry for these 3
- Challenge: Rate limiting
  Mitigation: Implement exponential backoff, rotate user agents

## CONTINUATION QUERY
"Continue pricing scraping automation. 18 sites remaining: [bestbuy.com, newegg.com, ebay.com...]. Use existing CSV 'pricing_data_partial.csv' (2,847 entries). Rate-limited domains need 2hr wait. Prioritize non-blocked sites first. Apply all established rules: 3-sec delays, skip no-reviews, 5-page pagination limit. Deliver final CSV by Friday 5pm."

示例 3：项目迁移（完整文化转移）

（输入上下文：包含 SDLC、智能体、治理的整个项目仓库）

输出：（展示结构的精简示例——真实输出将是全面的）

# CONTEXT ARTIFACT: "SmartInventory" Project Culture & Architecture
**Generated**: 2026-01-07 16:00
**Source Platform**: GitHub Copilot + Multi-Agent System
**Continuation Priority**: Medium (onboarding new AI agent framework)

## SESSION OVERVIEW
Enterprise inventory management system using AI-driven development culture. Need to replicate entire project structure, agent ecosystem, and governance for new autonomous AI agent setup.

## PROJECT STRUCTURE
### SDLC Framework
- Methodology: Agile with 2-week sprints
- Phases: Epic Planning → Development → Observer Review → CI/CD → Deployment
- All actions AI-driven: code generation, testing, documentation, commit narrative generation

### Agent Ecosystem
**Main Agents:**
- DevAgent: Code generation and implementation
- TestAgent: Automated testing and quality assurance
- DocAgent: Documentation generation and maintenance

**Observer Agent (Project Guardian):**
- Role: Alignment enforcer across all agents
- Functions: PR feedback, path validation, standards compliance
- Trigger: Every commit, PR, and epic completion

**CI/CD Agents:**
- FormatterAgent: Code style enforcement
- ReflectionAgent: Extracts commits → structured reflections, dev storylines, narrative outputs
- DeployAgent: Automated deployment pipelines

**Sub-Agents (by feature domain):**
- InventorySubAgent, UserAuthSubAgent, ReportingSubAgent

**Orchestration:**
- Multi-agent coordination via .ipynb notebooks
- Patterns: Prompt chaining, parallelization, router agents

### Repository Structure (.github)

.github/ ├── workflows/ │ ├── epic_breakdown.yml │ ├── epic_generator.yml │ ├── prd_template.yml │ ├── architectural_plan.yml │ ├── system_design.yml │ ├── conventional_commit.yml │ ├── memory_prompt.yml │ └── log_prompt.yml ├── AGENTS.md（智能体注册表） ├── copilot-instructions.md（项目级规则） └── sprints/ ├── sprint_01_instructions.md └── epic_variations/


### Governance & Standards
**Instructions Hierarchy:**
1. `copilot-instructions.md` - Project-wide immutable rules
2. Sprint instructions - Temporal variations per sprint
3. Epic instructions - Goal-specific invocations

**Conventional Patterns:**
- Commits: `type(scope): description` per Conventional Commits spec
- Memory prompt: Session state preservation template
- Log prompt: Structured activity tracking format

(... sections continue: Reusable Components, Quality Gates, Continuation Instructions for rebuilding with new AI agents...)

说明

  • 通用性：结构必须能被任何 AI 平台（ChatGPT、Claude、Gemini 等）解析

  • 完整性与简洁性的平衡：在全面上下文与可读性之间取得平衡——使用嵌套章节来呈现深层细节

  • 版本控制：包含时间戳和来源平台，以便在多次交接中追踪上下文的演变

  • 行动导向：始终以清晰的「延续查询」结尾——即下一个 AI 应使用的确切提示词

  • 项目规模适配：对于完整的项目迁移（案例 3），大幅扩展「项目结构」章节，同时保持其他章节简洁

  • 失败记录：明确记录哪些做法没有奏效以及原因——这能防止下一个 AI 重蹈覆辙

  • 规则保留：当规则/协议在会话过程中被确立时，包含其为何需要被制定的背景

  • 假设验证：将假设标记为「已验证」、「待验证」或「已失效」，以保持清晰

    • 适用于 GEMINI / GEMINI-CLI / ANTIGRAVITY

以下为极简版本：

GEMINI.md "# 跨平台的 Gemini AI 智能体

workflow/agent/sample.toml "# antigravity 提示词模板

MEMORY.md "# Gemini 记忆

会话：2026-01-07 | Sprint 01（剩余 7 天）| Epic EPIC-001（45%）
进行中：TASK-001-03 库存 CRUD API（GET/POST 已完成，PUT/DELETE 待完成）
决策：PostgreSQL + JSONB，RESTful /api/v1/，pytest 测试
下一步：完成 PUT/DELETE 端点，最终确定 schema"

English original

Context Preservation & Migration Prompt

[ for AGENT.MD pass THE ## SECTION if NOT APPLICABLE ]

Generate a comprehensive context artifact that preserves all conversational context, progress, decisions, and project structures for seamless continuation across AI sessions, platforms, or agents. This artifact serves as a "context USB" enabling any AI to immediately understand and continue work without repetition or context loss.

Core Objectives

Capture and structure all contextual elements from current session to enable:

  1. Session Continuity - Resume conversations across different AI platforms without re-explanation
  2. Agent Handoff - Transfer incomplete tasks to new agents with full progress documentation
  3. Project Migration - Replicate entire project cultures, workflows, and governance structures

Content Categories to Preserve

Conversational Context

  • Initial requirements and evolving user stories
  • Ideas generated during brainstorming sessions
  • Decisions made with complete rationale chains
  • Agreements reached and their validation status
  • Suggestions and recommendations with supporting context
  • Assumptions established and their current status
  • Key insights and breakthrough moments
  • Critical keypoints serving as structural foundations

Progress Documentation

  • Current state of all work streams
  • Completed tasks and deliverables
  • Pending items and next steps
  • Blockers encountered with mitigation strategies
  • Rate limits hit and workaround solutions
  • Timeline of significant milestones

Project Architecture (when applicable)

  • SDLC methodology and phases
  • Agent ecosystem (main agents, sub-agents, sibling agents, observer agents)
  • Rules, governance policies, and strategies
  • Repository structures (.github workflows, templates)
  • Reusable prompt forms (epic breakdown, PRD, architectural plans, system design)
  • Conventional patterns (commit formats, memory prompts, log structures)
  • Instructions hierarchy (project-level, sprint-level, epic-level variations)
  • CI/CD configurations (testing, formatting, commit extraction)
  • Multi-agent orchestration (prompt chaining, parallelization, router agents)
  • Output format standards and variations

Rules & Protocols

  • Established guidelines with scope definitions
  • Additional instructions added during session
  • Constraints and boundaries set
  • Quality standards and acceptance criteria
  • Alignment mechanisms for keeping work on track

Steps

  1. Scan Conversational History - Review entire thread/session for all interactions and context
  2. Extract Core Elements - Identify and categorize information per content categories above
  3. Document Progress State - Capture what's complete, in-progress, and pending
  4. Preserve Decision Chains - Include reasoning behind all significant choices
  5. Structure for Portability - Organize in universally interpretable format
  6. Add Handoff Instructions - Include explicit guidance for next AI/agent/session

Output Format

Produce a structured markdown document with these sections:

# CONTEXT ARTIFACT: [Session/Project Title]
**Generated**: [Date/Time]
**Source Platform**: [AI Platform Name]
**Continuation Priority**: [Critical/High/Medium/Low]

## SESSION OVERVIEW
[2-3 sentence summary of primary goals and current state]

## CORE CONTEXT
### Original Requirements
[Initial user requests and goals]

### Evolution & Decisions
[Key decisions made, with rationale - bulleted list]

### Current Progress
- Completed: [List]
- In Progress: [List with % complete]
- Pending: [List]
- Blocked: [List with blockers and mitigations]

## KNOWLEDGE BASE
### Key Insights & Agreements
[Critical discoveries and consensus points]

### Established Rules & Protocols
[Guidelines, constraints, standards set during session]

### Assumptions & Validations
[What's been assumed and verification status]

## ARTIFACTS & DELIVERABLES
[List of files, documents, code created with descriptions]

## PROJECT STRUCTURE (if applicable)
### Architecture Overview
[SDLC, workflows, repository structure]

### Agent Ecosystem
[Description of agents, their roles, interactions]

### Reusable Components
[Prompt templates, workflows, automation scripts]

### Governance & Standards
[Instructions hierarchy, conventional patterns, quality gates]

## HANDOFF INSTRUCTIONS
### For Next Session/Agent
[Explicit steps to continue work]

### Context to Emphasize
[What the next AI must understand immediately]

### Potential Challenges
[Known issues and recommended approaches]

## CONTINUATION QUERY
[Suggested prompt for next AI: "Given this context artifact, please continue by..."]

Examples

Example 1: Session Continuity (Brainstorming Handoff)

Input: "We've been brainstorming a mobile app for 2 hours. I need to switch to Claude. Generate context artifact."

Output:

# CONTEXT ARTIFACT: FitTrack Mobile App Planning
**Generated**: 2026-01-07 14:30
**Source Platform**: Google Gemini
**Continuation Priority**: High

## SESSION OVERVIEW
Brainstormed fitness tracking mobile app for busy professionals. Decided on minimalist design with AI coaching. Ready for technical architecture phase.

## CORE CONTEXT
### Original Requirements
- Target users: Working professionals 25-40, limited gym time
- Must sync with Apple Watch and Fitbit
- Budget: $50k for MVP
- Timeline: 3 months to launch

### Evolution & Decisions
- ✓ Name: "FitTrack Pro" (rejected: "QuickFit", "PowerHour")
- ✓ Core feature: 15-min AI-personalized workouts (not generic plans)
- ✓ Monetization: Freemium ($9.99/mo premium)
- ✓ Tech stack: React Native (for iOS+Android simultaneously)

### Current Progress
- Completed: Feature prioritization, user personas, monetization model
- In Progress: None yet
- Pending: Technical architecture, database schema, API design
- Blocked: None

## KNOWLEDGE BASE
### Key Insights & Agreements
- Users want "smart short" over "long complete" - brevity is premium feature
- AI coaching must feel conversational, not robotic commands
- Social features deferred to v2 to avoid scope creep

(... rest of structure follows)

## CONTINUATION QUERY
"Given this context artifact for FitTrack Pro app planning, please design the technical architecture including database schema, API endpoints, and third-party integration strategy for Apple Watch and Fitbit."

Example 2: Agent Handoff (Rate-Limited Automation)

Input: "Browser agent hit rate limit while scraping competitor pricing. Generate handoff doc."

Output:

# CONTEXT ARTIFACT: Competitor Pricing Automation (Incomplete)
**Generated**: 2026-01-07 09:15
**Source Platform**: Browser Agent v2.1
**Continuation Priority**: Critical

## SESSION OVERVIEW
Automated scraping of 50 competitor websites for pricing comparison. Completed 32/50 before rate limiting. Need immediate continuation to meet Friday deadline.

## CORE CONTEXT
### Original Requirements
- Scrape pricing for "wireless earbuds under $100" from 50 e-commerce sites
- Extract: product name, price, rating, review count
- Output: Single CSV for analysis
- Deadline: Friday 5pm

### Evolution & Decisions
- ✓ Added retry logic after initial failures on JS-heavy sites
- ✓ Switched to headless Chrome (from requests library) for better compatibility
- ✓ Implemented 3-second delays between requests per domain
- ✓ User added instruction: "Skip sites requiring login"

### Current Progress
- Completed: 32/50 sites successfully scraped (2,847 products)
- In Progress: None (halted at rate limit)
- Pending: 18 sites remaining (list in "Continuation Query" below)
- Blocked: Rate limited on domains: amazon.com, walmart.com, target.com (need 2-hour cooldown)

## KNOWLEDGE BASE
### Established Rules & Protocols
- Respect robots.txt without exception
- Max 1 request per 3 seconds per domain
- Skip products with no reviews (noise in data)
- Handle pagination up to 5 pages max per site

### Challenges & Mitigations
- Challenge: Dynamic pricing (changes during scraping)
  Mitigation: Timestamp each entry
- Challenge: Anti-bot CAPTCHAs on 3 sites
  Mitigation: User approved manual data entry for these 3
- Challenge: Rate limiting
  Mitigation: Implement exponential backoff, rotate user agents

## CONTINUATION QUERY
"Continue pricing scraping automation. 18 sites remaining: [bestbuy.com, newegg.com, ebay.com...]. Use existing CSV 'pricing_data_partial.csv' (2,847 entries). Rate-limited domains need 2hr wait. Prioritize non-blocked sites first. Apply all established rules: 3-sec delays, skip no-reviews, 5-page pagination limit. Deliver final CSV by Friday 5pm."

Example 3: Project Migration (Full Culture Transfer)

(Input context: Entire project repository with SDLC, agents, governance)

Output: (Condensed example showing structure - real output would be comprehensive)

# CONTEXT ARTIFACT: "SmartInventory" Project Culture & Architecture
**Generated**: 2026-01-07 16:00
**Source Platform**: GitHub Copilot + Multi-Agent System
**Continuation Priority**: Medium (onboarding new AI agent framework)

## SESSION OVERVIEW
Enterprise inventory management system using AI-driven development culture. Need to replicate entire project structure, agent ecosystem, and governance for new autonomous AI agent setup.

## PROJECT STRUCTURE
### SDLC Framework
- Methodology: Agile with 2-week sprints
- Phases: Epic Planning → Development → Observer Review → CI/CD → Deployment
- All actions AI-driven: code generation, testing, documentation, commit narrative generation

### Agent Ecosystem
**Main Agents:**
- DevAgent: Code generation and implementation
- TestAgent: Automated testing and quality assurance
- DocAgent: Documentation generation and maintenance

**Observer Agent (Project Guardian):**
- Role: Alignment enforcer across all agents
- Functions: PR feedback, path validation, standards compliance
- Trigger: Every commit, PR, and epic completion

**CI/CD Agents:**
- FormatterAgent: Code style enforcement
- ReflectionAgent: Extracts commits → structured reflections, dev storylines, narrative outputs
- DeployAgent: Automated deployment pipelines

**Sub-Agents (by feature domain):**
- InventorySubAgent, UserAuthSubAgent, ReportingSubAgent

**Orchestration:**
- Multi-agent coordination via .ipynb notebooks
- Patterns: Prompt chaining, parallelization, router agents

### Repository Structure (.github)

.github/ ├── workflows/ │ ├── epic_breakdown.yml │ ├── epic_generator.yml │ ├── prd_template.yml │ ├── architectural_plan.yml │ ├── system_design.yml │ ├── conventional_commit.yml │ ├── memory_prompt.yml │ └── log_prompt.yml ├── AGENTS.md (agent registry) ├── copilot-instructions.md (project-level rules) └── sprints/ ├── sprint_01_instructions.md └── epic_variations/


### Governance & Standards
**Instructions Hierarchy:**
1. `copilot-instructions.md` - Project-wide immutable rules
2. Sprint instructions - Temporal variations per sprint
3. Epic instructions - Goal-specific invocations

**Conventional Patterns:**
- Commits: `type(scope): description` per Conventional Commits spec
- Memory prompt: Session state preservation template
- Log prompt: Structured activity tracking format

(... sections continue: Reusable Components, Quality Gates, Continuation Instructions for rebuilding with new AI agents...)

Notes

  • Universality: Structure must be interpretable by any AI platform (ChatGPT, Claude, Gemini, etc.)

  • Completeness vs Brevity: Balance comprehensive context with readability - use nested sections for deep detail

  • Version Control: Include timestamps and source platform for tracking context evolution across multiple handoffs

  • Action Orientation: Always end with clear "Continuation Query" - the exact prompt for next AI to use

  • Project-Scale Adaptation: For full project migrations (Case 3), expand "Project Structure" section significantly while keeping other sections concise

  • Failure Documentation: Explicitly capture what didn't work and why - this prevents next AI from repeating mistakes

  • Rule Preservation: When rules/protocols were established during session, include the context of WHY they were needed

  • Assumption Validation: Mark assumptions as "validated", "pending validation", or "invalidated" for clarity

    • FOR GEMINI / GEMINI-CLI / ANTIGRAVITY

Here are ultra-concise versions:

GEMINI.md "# Gemini AI Agent across platform

workflow/agent/sample.toml "# antigravity prompt template

MEMORY.md "# Gemini Memory

Session: 2026-01-07 | Sprint 01 (7d left) | Epic EPIC-001 (45%)
Active: TASK-001-03 inventory CRUD API (GET/POST done, PUT/DELETE pending)
Decisions: PostgreSQL + JSONB, RESTful /api/v1/, pytest testing
Next: Complete PUT/DELETE endpoints, finalize schema"

同类提示词

更多「Agent Workflows」→