Rebuilding Instructions, Rules, and Long-Term Memory When Migrating AI Coding Tools

As AI coding tools become part of daily development workflows, their long-term value is often not limited to code generated in a single conversation. Developers also accumulate instructions, rules, project conventions, and working knowledge over years of use. These assets are commonly scattered across user directories, hidden project folders, extension settings, and local databases.

When an account is restricted, a service policy changes, or a team decides to switch tools, the challenge is not simply creating a new account. It is preserving and migrating the complete working context.

Many developers discover during migration that being able to export chat history does not mean global instructions, project rules, and long-term memory can also be transferred intact. Different tools use different directory structures, field names, loading priorities, and storage formats. Even when two tools both support features called “system instructions” or “memory,” one directory cannot be mapped directly to another.

1. Identify the Four Types of Assets to Migrate

The first step in a migration is not copying files. It is creating an inventory of assets. In most AI coding tools, local context can be divided into four categories.

1. Global Instructions

Global instructions apply to every project. They commonly include:

• Preferred response language and output format
• Code style preferences
• Whether tests should be written first
• Error handling and logging conventions
• Constraints for dependencies, directories, and commit messages
• Whether confirmation is required before running commands
• Security boundaries that generated code must follow

Global instructions are usually few in number, but they have the broadest impact. During migration, project-specific content must not be mixed into them. Otherwise, unrelated projects may inherit incorrect constraints.

2. Project Rules

Project rules apply only to a specific repository or directory. Common examples include:

• Build, test, and deployment procedures
• Module boundaries and directory responsibilities
• API naming conventions
• Database change processes
• Frontend component conventions
• Directories that must not be modified
• Checks that must be completed before code is committed

Project rules are more specific than global instructions and usually have higher priority. During migration, preserve the project path associated with each rule and record its applicable scope. Do not merge every project rule into one large text file.

3. Long-Term Memory

Long-term memory contains context formed through repeated collaboration, such as:

• The project’s technology stack and historical background
• Confirmed architectural decisions
• Preferred implementation approaches within the team
• Previously discovered pitfalls
• Reasons certain modules cannot be refactored directly
• Known compatibility limitations

This information is often the most valuable and the easiest to lose. It may not exist as plain text files; it can be stored in local databases, structured caches, or specialized indexes. Even if it can be exported, it must be reviewed to distinguish durable facts from outdated conclusions. It should not be imported into a new environment without review.

4. Skills and Supporting Configuration

Skills can be understood as reusable workflows, such as code review, API design, test generation, documentation organization, or troubleshooting. Other supporting assets may include extension settings, shortcut commands, script templates, and environment variables.

These assets are often tightly coupled to a tool’s own invocation mechanism. Migration requires more than copying text: trigger methods, parameter formats, and permission boundaries must also be reviewed.

2. Why Copying Directories Usually Fails

Many failed migrations begin with a seemingly reasonable idea: point the new tool at the old tool’s configuration directory, or copy the entire directory into the new environment.

This approach has at least four problems.

First, directory structures are incompatible. One tool may use multiple hierarchical text files, while another uses a single configuration file or local database. Even if the file contents are identical, the new tool may not scan those locations.

Second, field semantics differ. A “global rule” in one tool may correspond to a “developer instruction” in another, while “project memory” may only be transferable as standard documentation. Similar names do not guarantee identical loading behavior.

Third, priority models vary. Some tools apply layered overrides across global configuration, user configuration, project configuration, and current-directory configuration. Others append all instructions together. An incorrect migration may create duplicate or conflicting rules, or allow lower-priority content loaded later to override important constraints.

Fourth, there is a risk of privacy leakage. Long-term memory may contain internal domains, access methods, business data structures, customer information, or historical credentials. Importing an entire directory without filtering can expose information to a new synchronization scope where it does not belong.

The correct migration model is therefore not “move a directory.” It is “extract, classify, convert, validate, and reload content.”

3. A Recommended Migration Process

Step 1: Freeze the Old Environment

Before starting, stop making changes to the old configuration and create at least one read-only backup. Retain:

• Original configuration files
• A directory structure inventory
• File modification timestamps
• Copies of local databases or caches
• Currently used environment variables
• A list of installed extensions and skills

Store backups separately and use version control or read-only storage whenever possible. Do not perform bulk replacements in the original directory. If a conversion script fails, recovery may otherwise be difficult.

Step 2: Create an Asset Inventory

Use a simple inventory file to record each item:

Asset Name | Type | Source Path | Scope | Sensitivity | Migration Status

At minimum, the scope should distinguish between global, single-project, single-module, and temporary-task content. Sensitivity can be classified as public, internal, or confidential.

This step turns migration from an ad hoc copy operation into a traceable process. If content is later found to be missing, it becomes easier to determine whether it was not exported or simply not loaded in the new environment.

Step 3: Clean and Sanitize Content

Sensitive information should be handled before format conversion. Review items such as:

• Access tokens and secrets
• Database connection strings
• Internal service addresses
• User data samples
• Real names and contact details
• Paths valid only in the old environment
• Identifiers tied to a specific account or device

Replace sensitive values with placeholders where appropriate:

DATABASE_URL=<REDACTED>
INTERNAL_API=<INTERNAL_ENDPOINT>
PROJECT_OWNER=<TEAM_MEMBER>

If a memory item cannot be safely sanitized, migrate only its abstract conclusion rather than its raw details. For example, replace “a specific customer field mapping” with: “Customer field mappings have historical compatibility constraints; review migration documentation before making changes.”

Step 4: Convert Content Into an Intermediate Format

Avoid converting directly from the old format to the new format. First create a tool-independent intermediate layer using structured text or a common data format:

{
  "scope": "project",
  "name": "Testing Requirements",
  "priority": 80,
  "content": "After modifying business logic, add unit tests and run the full test suite.",
  "source": "legacy-config",
  "sensitive": false
}

The intermediate format should include at least:

• Scope
• Content
• Priority
• Source
• Last updated time
• Sensitivity marker
• Whether manual review is required

With this layer in place, future tool migrations do not require parsing the original legacy format again. Only a new import adapter is needed.

Step 5: Reorganize Content by Layer

Migration should resolve duplication and conflict rather than preserve every item unchanged.

For example:

• Coding style requirements that apply everywhere belong in global instructions.
• Build commands for one repository belong in project rules.
• Historical reasons a module cannot be changed casually belong in long-term memory.
• Operational procedures such as post-change checks belong in skills or workflows.

For duplicate rules, retain one authoritative version and place only project-specific differences at the project level. For conflicting rules, use the current reality of the relevant project as the deciding factor, and document the reason for the decision in the migration record.

Step 6: Import in Batches and Validate

Do not import everything at once. A recommended sequence is:

1. Import a small set of global instructions.
2. Validate them with a non-critical project.
3. Import that project’s rules.
4. Verify that the tool understands and follows them.
5. Import long-term memory.
6. Restore skills and supporting configuration last.

Prepare fixed validation prompts, such as:

• Ask the tool to summarize the current project’s technology stack.
• Ask it to explain a core rule.
• Ask it to generate code that follows team conventions.
• Ask it to list checks that must be run after a change.
• Ask it to identify directories that must not be modified directly.

If key constraints are missing from the response, the imported content may not be active or its priority may be incorrect.

4. Capabilities Needed in a Migration Tool

If you need to build a script or internal migration tool, it should support at least the following capabilities.

1. Read-Only Scanning

By default, the tool should only read the old directories and should not modify them. Scan results should show the number, size, and type of files, as well as potentially sensitive content.

2. Incremental Migration

Use file digests or modification timestamps to identify changes. Convert only new or updated content to avoid reprocessing all data during every migration run.

3. Conflict Detection

When multiple sources contain rules with the same name or constraints that contradict each other, produce a conflict report instead of silently overwriting content.

4. Rollback Support

Create a snapshot of the target environment before import so that it can be restored if migration fails.

5. Audit Logging

Record the source, conversion result, import time, and error reason for every asset. Logs must not include plaintext secrets or sensitive content.

6. Manual Approval

For credentials, internal addresses, or high-priority rules, automated import should pause and require user confirmation.

5. Reducing the Cost of Future Migrations

The most effective approach is to manage context assets like code from now on.

Global instructions and project rules should be split into short, single-purpose files. Long-term memory should be reviewed regularly to remove obsolete content and annotate when it was created and under which conditions it applies. Important architectural conclusions should also be documented within the project rather than existing only in a tool’s internal memory.

A regular export process can also help:

• Export rules and instructions weekly.
• Review long-term memory monthly.
• Update the migration baseline after major architecture changes.
• Run a fixed regression test set after every tool upgrade.
• Store sanitized intermediate formats in controlled version management.

If a team uses cloud environments such as Baidu AI Cloud, sanitized configuration backups can be stored in object storage according to organizational security requirements, with access controls, audit logs, and lifecycle policies. Regardless of the infrastructure used, follow the principle of least privilege and avoid uploading raw sensitive configuration.

Conclusion

Migrating AI coding tools is not fundamentally about copying configuration files into a new directory. It is an exercise in governing context assets. What needs protection is the developer’s accumulated working methods, project knowledge, and decision history.

A reliable migration approach has four characteristics: clear asset boundaries, sanitized content, a tool-independent intermediate format, and a process that can be validated and rolled back. After completing a migration, global instructions, project rules, and long-term memory should gradually be separated from private tool storage and managed as independent, auditable, reusable knowledge assets.

With this approach, future platform changes, account policy adjustments, or runtime environment transitions no longer leave developers with only scattered files. Instead, they retain a complete, stable, and continuously evolving development context system.