reading-logseq-data
Expert in reading data from Logseq DB graphs via HTTP API or CLI. Auto-invokes when users want to fetch pages, blocks, or properties from Logseq, execute Datalog queries against their graph, search content, or retrieve backlinks and relationships. Provides the logseq-client library for operations.
Best use case
reading-logseq-data 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 in reading data from Logseq DB graphs via HTTP API or CLI. Auto-invokes when users want to fetch pages, blocks, or properties from Logseq, execute Datalog queries against their graph, search content, or retrieve backlinks and relationships. Provides the logseq-client library for operations.
Expert in reading data from Logseq DB graphs via HTTP API or CLI. Auto-invokes when users want to fetch pages, blocks, or properties from Logseq, execute Datalog queries against their graph, search content, or retrieve backlinks and relationships. Provides the logseq-client library for operations.
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 "reading-logseq-data" skill to help with this workflow task. Context: Expert in reading data from Logseq DB graphs via HTTP API or CLI. Auto-invokes when users want to fetch pages, blocks, or properties from Logseq, execute Datalog queries against their graph, search content, or retrieve backlinks and relationships. Provides the logseq-client library for operations.
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/reading-logseq-data/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How reading-logseq-data Compares
| Feature / Agent | reading-logseq-data | 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 in reading data from Logseq DB graphs via HTTP API or CLI. Auto-invokes when users want to fetch pages, blocks, or properties from Logseq, execute Datalog queries against their graph, search content, or retrieve backlinks and relationships. Provides the logseq-client library for operations.
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
# Reading Logseq Data
## When to Use This Skill
This skill auto-invokes when:
- User wants to read pages or blocks from their Logseq graph
- Fetching properties or metadata from Logseq entities
- Executing Datalog queries against the graph
- Searching for content in Logseq
- Finding backlinks or references
- User mentions "get from logseq", "fetch page", "query logseq"
**Client Library**: See `{baseDir}/scripts/logseq-client.py` for the unified API.
## Available Operations
| Operation | Description |
|-----------|-------------|
| `get_page(title)` | Get page content and properties |
| `get_block(uuid)` | Get block with children |
| `search(query)` | Full-text search across graph |
| `datalog_query(query)` | Execute Datalog query |
| `list_pages()` | List all pages |
| `get_backlinks(title)` | Find pages linking to this one |
| `get_graph_info()` | Get current graph metadata |
## Quick Examples
### Get a Page
```python
from logseq_client import LogseqClient
client = LogseqClient()
page = client.get_page("My Page")
print(f"Title: {page['title']}")
print(f"Properties: {page['properties']}")
```
### Execute Datalog Query
```python
# Find all books with rating >= 4
results = client.datalog_query('''
[:find (pull ?b [:block/title :user.property/rating])
:where
[?b :block/tags ?t]
[?t :block/title "Book"]
[?b :user.property/rating ?r]
[(>= ?r 4)]]
''')
for book in results:
print(f"{book['block/title']}: {book['user.property/rating']} stars")
```
### Search Content
```python
# Search for mentions of "project"
results = client.search("project")
for block in results:
print(f"Found in: {block['page']}")
print(f"Content: {block['content'][:100]}...")
```
## Datalog Query Patterns
### Find All Pages
```clojure
[:find (pull ?p [:block/title])
:where
[?p :block/tags ?t]
[?t :db/ident :logseq.class/Page]]
```
### Find Blocks with Tag
```clojure
[:find (pull ?b [*])
:where
[?b :block/tags ?t]
[?t :block/title "Book"]]
```
### Find by Property
```clojure
[:find ?title ?author
:where
[?b :block/title ?title]
[?b :user.property/author ?author]
[?b :block/tags ?t]
[?t :block/title "Book"]]
```
### Find Tasks by Status
```clojure
[:find (pull ?t [:block/title :logseq.property/status])
:where
[?t :block/tags ?tag]
[?tag :db/ident :logseq.class/Task]
[?t :logseq.property/status ?s]
[?s :block/title "In Progress"]]
```
### Find Backlinks
```clojure
[:find (pull ?b [:block/title {:block/page [:block/title]}])
:in $ ?page-title
:where
[?p :block/title ?page-title]
[?b :block/refs ?p]]
```
### Aggregations
```clojure
;; Count books per author
[:find ?author (count ?b)
:where
[?b :block/tags ?t]
[?t :block/title "Book"]
[?b :user.property/author ?author]]
```
## Using the Client Library
### Initialization
```python
from logseq_client import LogseqClient
# Auto-detect backend
client = LogseqClient()
# Force specific backend
client = LogseqClient(backend="http")
# Custom URL/token
client = LogseqClient(
url="http://localhost:12315",
token="your-token"
)
```
### Error Handling
```python
try:
page = client.get_page("Nonexistent Page")
except client.NotFoundError:
print("Page doesn't exist")
except client.ConnectionError:
print("Cannot connect to Logseq")
except client.AuthError:
print("Invalid token")
```
### Batch Operations
```python
# Get multiple pages efficiently
pages = ["Page1", "Page2", "Page3"]
results = [client.get_page(p) for p in pages]
# Or use a single query
query = '''
[:find (pull ?p [*])
:in $ [?titles ...]
:where
[?p :block/title ?titles]]
'''
results = client.datalog_query(query, [pages])
```
## Performance Tips
1. **Use specific queries** - Don't fetch more than needed
2. **Prefer pull syntax** - `(pull ?e [:needed :fields])` vs `[*]`
3. **Put selective clauses first** - Filter early in query
4. **Use parameters** - Pass values via `:in` clause
5. **Batch when possible** - Multiple items in one query
## CLI Fallback
If HTTP API unavailable, the client falls back to CLI:
```python
# CLI mode (automatic if HTTP fails)
client = LogseqClient(backend="cli", graph_path="/path/to/graph")
# Query still works the same way
results = client.datalog_query("[:find ?title :where [?p :block/title ?title]]")
```
## Output Formats
### Raw (default)
Returns Python dicts/lists directly from API.
### Normalized
```python
# Get normalized output
page = client.get_page("My Page", normalize=True)
# Returns: {"title": "...", "uuid": "...", "properties": {...}, "blocks": [...]}
```
## Reference Materials
- See `{baseDir}/references/read-operations.md` for all operations
- See `{baseDir}/templates/query-template.edn` for query patternsRelated Skills
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