migrating-to-db
Expert guidance for migrating Logseq graphs from Markdown (MD) format to the new Database (DB) format. Auto-invokes when users ask about MD to DB migration, converting graphs, import options, data transformation, or compatibility between Logseq versions. Covers migration strategies, common issues, and best practices.
Best use case
migrating-to-db is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Expert guidance for migrating Logseq graphs from Markdown (MD) format to the new Database (DB) format. Auto-invokes when users ask about MD to DB migration, converting graphs, import options, data transformation, or compatibility between Logseq versions. Covers migration strategies, common issues, and best practices.
Expert guidance for migrating Logseq graphs from Markdown (MD) format to the new Database (DB) format. Auto-invokes when users ask about MD to DB migration, converting graphs, import options, data transformation, or compatibility between Logseq versions. Covers migration strategies, common issues, and best practices.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "migrating-to-db" skill to help with this workflow task. Context: Expert guidance for migrating Logseq graphs from Markdown (MD) format to the new Database (DB) format. Auto-invokes when users ask about MD to DB migration, converting graphs, import options, data transformation, or compatibility between Logseq versions. Covers migration strategies, common issues, and best practices.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/migrating-to-db/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How migrating-to-db Compares
| Feature / Agent | migrating-to-db | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Expert guidance for migrating Logseq graphs from Markdown (MD) format to the new Database (DB) format. Auto-invokes when users ask about MD to DB migration, converting graphs, import options, data transformation, or compatibility between Logseq versions. Covers migration strategies, common issues, and best practices.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Migrating to Logseq DB
## When to Use This Skill
This skill auto-invokes when:
- User asks about migrating from Logseq MD to DB version
- Converting markdown graphs to database format
- Import/export between Logseq versions
- Questions about what transfers during migration
- Namespace handling during migration
- Tag-to-class conversion decisions
- Property type inference during import
- User mentions "migrate", "convert", "MD to DB", "markdown to database"
You are an expert in migrating Logseq graphs from MD (Markdown) format to DB (Database) format.
## Migration Overview
### Why Migrate?
| Feature | MD Version | DB Version |
|---------|------------|------------|
| Storage | Markdown files | SQLite database |
| Tags | Page references | Classes with properties |
| Properties | Text strings | Typed values |
| Queries | Limited | Full Datalog |
| Sync | File-based | Real-time (subscription) |
| Performance | File I/O dependent | Optimized queries |
### Current Status (2024-2025)
**Important**: Logseq DB is still in **alpha**. Consider:
- Data loss risk exists
- Some features not yet available (whiteboards)
- Plugin compatibility varies
- Requires subscription for sync
## Pre-Migration Checklist
Before migrating, assess your graph:
### 1. Backup Everything
```bash
# Create timestamped backup
cp -r ~/logseq/my-graph ~/logseq/my-graph-backup-$(date +%Y%m%d)
# Or compress
tar -czvf my-graph-backup.tar.gz ~/logseq/my-graph
```
### 2. Audit Current Structure
**Pages to review:**
- [ ] Namespaced pages (a/b/c) → May become separate pages
- [ ] Pages with same name, different namespaces
- [ ] Template pages
- [ ] Query pages
**Properties to review:**
- [ ] Property formats (key:: value)
- [ ] Multi-value properties
- [ ] Date properties
- [ ] Linked properties ([[page]])
**Tags to review:**
- [ ] Simple tags (#tag)
- [ ] Page tags ([[tag]])
- [ ] Nested tags (#parent/child)
### 3. Identify Migration Decisions
| MD Pattern | DB Options | Decision Needed |
|------------|------------|-----------------|
| `#tag` | Class or page ref | Which tags become classes? |
| `[[page]]` | Node reference | Keep as reference |
| `property:: value` | Typed property | What type? |
| `namespace/page` | Separate page or hierarchy | Flatten or nest? |
## Migration Process
### Step 1: Export from MD Version
1. Open your MD graph in Logseq
2. Go to **Settings** → **Export**
3. Choose **Export to EDN** (for full data)
4. Save the export file
### Step 2: Prepare Import Settings
When importing to DB, you'll choose:
**Tag Handling:**
- **Convert to classes**: Tags become proper classes with inherited properties
- **Keep as references**: Tags remain simple page links
**Namespace Handling:**
- **Flatten**: `a/b/c` → single page "a/b/c"
- **Hierarchical**: Creates page hierarchy
**Property Handling:**
- **Infer types**: Logseq guesses types (number, date, etc.)
- **All as text**: Everything stays as strings
### Step 3: Create New DB Graph
1. Create new DB-based graph in Logseq
2. Use **Import** feature
3. Select your exported data
4. Configure migration options
5. Review and confirm
### Step 4: Post-Migration Validation
```clojure
;; Check page count matches
[:find (count ?p)
:where [?p :block/tags ?t]
[?t :db/ident :logseq.class/Page]]
;; Check for orphaned blocks
[:find (pull ?b [:block/title])
:where [?b :block/title _]
(not [?b :block/page _])
(not [?b :block/tags ?t]
[?t :db/ident :logseq.class/Page])]
;; Verify properties migrated
[:find ?prop-name (count ?b)
:where [?b ?prop _]
[?p :db/ident ?prop]
[?p :block/title ?prop-name]
[(clojure.string/starts-with? (str ?prop) ":user.property")]]
```
## Common Migration Issues
### Issue 1: Lost Property Types
**Symptom**: Numbers/dates stored as strings
**Solution**: Manually update property types
```clojure
;; In DB, update property type
{:db/ident :user.property/rating
:logseq.property/type :number} ; was :default
```
### Issue 2: Tag/Class Confusion
**Symptom**: Tags didn't become proper classes
**Solution**: Convert pages to classes
1. Open the tag page
2. Add `#Tag` to make it a class
3. Define properties on the class
### Issue 3: Broken References
**Symptom**: `[[page]]` links not working
**Cause**: Page names changed during migration
**Solution**: Use find/replace or query to identify broken refs
```clojure
[:find ?ref-text
:where
[?b :block/title ?title]
[(re-find #"\[\[.*?\]\]" ?title) ?ref-text]
(not [_ :block/title ?ref-text])]
```
### Issue 4: Namespace Flattening
**Symptom**: `project/tasks` and `project/notes` merged
**Solution**: Pre-migration, rename pages to avoid conflicts
### Issue 5: Query Compatibility
**Symptom**: Old queries don't work
**Reason**: Different attribute names
| MD Attribute | DB Attribute |
|--------------|--------------|
| `:block/content` | `:block/title` |
| `:block/name` | `:block/title` |
| `:page/tags` | `:block/tags` |
## Migration Strategies
### Strategy 1: Big Bang Migration
- Migrate entire graph at once
- Best for: Small graphs, simple structure
- Risk: All-or-nothing
### Strategy 2: Parallel Operation
- Keep MD graph active
- Create DB graph for new content
- Gradually move old content
- Best for: Large graphs, active use
### Strategy 3: Selective Migration
- Export specific pages/areas
- Import into new DB graph
- Best for: Messy graphs needing cleanup
## Best Practices
### Before Migration
1. **Clean up your graph**
- Remove unused pages
- Standardize property names
- Fix broken links
2. **Document your structure**
- List all tags and their purposes
- Document property meanings
- Map namespaces
3. **Plan your classes**
- Which tags become classes?
- What properties do they need?
- Define inheritance hierarchy
### During Migration
1. **Start small** - Test with a subset
2. **Compare counts** - Pages, blocks, properties
3. **Check critical pages** - Most important content first
4. **Verify queries** - Update and test all queries
### After Migration
1. **Don't delete MD graph** - Keep as backup
2. **Monitor for issues** - Note problems for feedback
3. **Update workflows** - Adapt to new features
4. **Explore new capabilities** - Classes, typed properties
## Feature Comparison
### Available in DB Version
- ✅ Typed properties (number, date, checkbox)
- ✅ Class inheritance
- ✅ Property schemas
- ✅ Full Datalog queries
- ✅ Real-time collaboration (Pro)
- ✅ Library view
### Not Yet Available (Alpha)
- ⏳ Whiteboards
- ⏳ Some plugins
- ⏳ Full export options
- ⏳ Advanced templates
### Different Behavior
- 📝 Tags = Classes (more powerful but different)
- 📝 Sync requires subscription
- 📝 File access limited (SQLite, not .md)
## Resources
- [Logseq DB Documentation](https://github.com/logseq/docs/blob/master/db-version.md)
- [DB Unofficial FAQ](https://discuss.logseq.com/t/logseq-db-unofficial-faq/32508)
- [Migration Feedback Thread](https://discuss.logseq.com/t/logseq-db-changelog/30013)Related Skills
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