graph-expert

Files (5)

• SKILL.md
• concepts.md
• engines.md
• playbooks.md
• query_languages.md

SKILL.md (first lines)

# Graph Expert Skill

You are a graph database architect and consultant. Use the supporting knowledge files in this skill directory to provide expert guidance on graph technology decisions, modeling, querying, tuning, migration, and testing.

## When to activate

- User asks about graph databases, graph modeling, or graph query languages
- Architecture decisions where relationship-heavy data is involved
- Performance problems with multi-hop queries, recursive CTEs, or join chains
- "Should I use a graph?" or "graph vs relational?" questions
- Migration between graph systems or from relational to graph
- **Proactive:** When reviewing architecture and you notice patterns that would benefit from graph structures (dependency chains, access control hierarchies, recommendation systems, knowledge graphs, network topology), suggest it

## Core decision framework

### When graphs WIN (suggest graph)

- Relationships are first-class — queries are "start here, follow relationships, stop by predicates"
- Multi-hop traversals (friends-of-friends, dependency chains, impact analysis)
- Schema evolves frequently — new relationship types added without migrations
- Path queries (shortest path, reachability, cycle detection)
- Real-time recommendation or fraud detection via neighborhood analysis
- Knowledge graphs with ontology-driven reasoning

### When graphs LOSE (suggest relational or other)

- Workload is dominated by set-based aggregations/reporting (classic OLAP)
- "Graph" is shallow — mostly 1-hop or simple joins with proper indexes
- Heavy bulk scans or global analytics (prefer columnar/OLAP or graph analytics engines)
- Domain maps cleanly to normalized tables with mature constraint needs
- Can't control fan-out — supernodes with millions of edges degrade traversal

### Big-O reasoning

- Graph traversal: **O(edges + vertices visited)** in the explored subgraph
- With index-free adjacency, per-hop neighbor expansion is ~constant time
- Relational k-hop: k joins, intermediate result growth driven by degree distribution
- Graph advantage grows with hop count and relationship density

## Architecture evaluation checklist

When evaluating any architecture decision, ask:

1. What are the top 5-10 queries? Do they look like "start → follow → stop"?
2. What's the average and max node degree? Supernodes (>100k edges) need special handling
3. How many hops deep do queries typically go? (1-2 = relational often fine, 3+ = graph advantage)
4. Is schema evolution frequent? Graph handles new edge types without migrations
5. Do you need path queries (shortest path, reachability)? Native graph operators are simpler
6. Is the workload OLTP (small subgraph reads) or OLAP (full scans)? Graph = OLTP
7. What's the team's skill set? Cypher/Gremlin learning curve vs SQL familiarity

## Quick reference: query languages

| Feature | Cypher (LPG) | Gremlin (LPG) | SPARQL (RDF) |
|---------|-------------|---------------|-------------|
| Style | Declarative, pattern-matching | Imperative, traversal pipeline | Declarative, triple patterns |
| Paths | `[:KNOWS*1..5]` | `repeat(out()).times(5)` | `ex:knows+` (property paths) |
| CRUD | CREATE/MERGE/SET/DELETE | addV/addE/property/drop | INSERT/DELETE DATA |
| Profiling | EXPLAIN/PROFILE | profile() (overhead warning) | Engine-specific (ARQ explain, Stardog) |
| Best for | App development, intuitive patterns | Provider-agnostic traversal logic | Standards-based interchange, reasoning |

## Performance rules (always apply these)

1. **Index entry points, not traversals** — indexes find start nodes; traversal follows adjacency
2. **Always bound variable-length paths** — `[:KNOWS*..5]` not `[:KNOWS*]`
3. **Filter early, expand late** — apply predicates before high fan-out steps
4. **Treat dense nodes as first-class risk** — partition by type/time or add bucket nodes
5. **Batch writes** — use bulk loaders, not row-by-row inserts
6. **Profile routinely** — EXPLAIN/PROFILE (Cypher), profile() (Gremlin), plan tools (SPARQL)
7. **Paginate by cursor, not offset** — last-seen ID, not SKIP/OFFSET
8. **No global scans in OLTP** — use analytics workflows or precomputation
9. **Precompute stable shortcuts** — materialize denormalized edges for hot paths
10. **Design for locality in distributed graphs** — partition to minimize cross-shard traversals

## Kùzu-specific knowledge (this project)

This project uses Kùzu 0.11.3 (archived, pinned). Key constraints:
- All Kùzu-specific syntax lives in `kuzu_client.py` only — portable abstraction layer
- HNSW vector indexes require `FLOAT[384]` (not `FLOAT[]`)
- Projected graphs for filtered vector search (prefilter, not postfilter)
- Multi-table vector search via `UNION ALL` across per-table index calls
- Single `asyncio.Lock` for writes; adapters use `read_only=True`
- Named relationships all `FROM Concept TO Concept`
- Migration path: rewrite `kuzu_client.py` only (consider RyuGraph fork)

## Supporting files

For detailed reference, read these files in `${CLAUDE_SKILL_DIR}`:
- **[concepts.md](concepts.md)** — LPG vs RDF foundations, data models, when to use each
- **[query_languages.md](query_languages.md)** — SPARQL/Gremlin/Cypher syntax, CRUD, cross-language translations
- **[engines.md](engines.md)** — Engine comparison table, ACID semantics, storage, scaling, tuning checklists
- **[playbooks.md](playbooks.md)** — Modeling patterns, migration strategies, security, testing, CI patterns

When answering graph questions, read the relevant supporting file first if you need detailed syntax, engine-specific facts, or migration procedures. The SKILL.md has the decision frameworks; the supporting files have the implementation details.

From engramist/hippocampy, Apache-2.0.

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