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title	contributor	tags
Context Migration	@joembolinas

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:

Session Continuity - Resume conversations across different AI platforms without re-explanation
Agent Handoff - Transfer incomplete tasks to new agents with full progress documentation
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

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

Output Format

Produce a structured markdown document with these sections:

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