Agent skill

Managing Databases

by rileyhilliard in rileyhilliard/claude-essentials

Guides database architecture for PostgreSQL, DuckDB, Parquet, PGVector, and Neo4j.

MITAuto-check passedDatabases

Install Managing Databases

skills CLI
$ npx skills add rileyhilliard/claude-essentials --skill managing-databases -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rileyhilliard/claude-essentials managing-databases --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rileyhilliard/claude-essentials.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/ce/skills/managing-databases .claude/skills/managing-databases && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
managing-databases
GitHub stars
130
Token cost
~1.6k tokens
SKILL.md length
631 words
Files
11 (incl. references)
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

Guides database architecture for PostgreSQL, DuckDB, Parquet, PGVector, and Neo4j.

  • Works in 5 steps: Run EXPLAIN (ANALYZE, BUFFERS) on the… → Check for sequential scans on large tables → Verify indexes exist on filter/join… → …
  • Designing schemas
  • SKILL.md covers Contents, When to use which database, PostgreSQL quick reference and DuckDB quick reference, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Managing Databases is an agent skill from rileyhilliard/claude-essentials. Guides database architecture for PostgreSQL, DuckDB, Parquet, PGVector, and Neo4j. Use when designing schemas, choosing storage strategies, optimizing queries, configuring vector or graph workloads, or diagnosing performance issues.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `references/duckdb-architecture.md`, `references/duckdb-querying.md` and `references/neo4j-architecture.md`).

It sits in Databases, covering DataFrames. It works with PostgreSQL, DuckDB, Neo4j and pgvector. The licence is MIT.

When your agent uses it

  • Designing schemas
  • Choosing storage strategies
  • Optimizing queries
  • Configuring vector

Example prompts

  • “Use the managing-databases skill to guide database architecture for PostgreSQL, DuckDB, Parquet, PGVector, and Neo4j”
  • “/managing-databases”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Run EXPLAIN (ANALYZE, BUFFERS) on the query
  2. Check for sequential scans on large tables
  3. Verify indexes exist on filter/join columns
  4. Check pg_stat_user_tables for bloat (dead tuples)
  5. Review work_mem if seeing disk sorts

What it can do on your machine

Read from SKILL.md and the folder at commit 3a67a01. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Managing Databases loads about 1.6k tokens when it runs, and up to ~20k if it reads all its reference files. Until then it costs about 63 tokens; SKILL.md has 631 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~63
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~20k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rileyhilliard/claude-essentials at commit 3a67a01, republished under its MIT licence (© rileyhilliard). 631 words, ~1,619 tokens.

Download SKILL.mdSave it as .claude/skills/managing-databases/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
managing-databases
description
Guides database architecture for PostgreSQL, DuckDB, Parquet, PGVector, and Neo4j. Use when designing schemas, choosing storage strategies, optimizing queries, configuring vector or graph workloads, or diagnosing performance issues.

Database Management

Decision guidance for PostgreSQL, DuckDB, Parquet, and Neo4j in hybrid storage architectures.

Contents

  • When to use which database
  • PostgreSQL quick reference
  • DuckDB quick reference
  • Parquet quick reference
  • PGVector quick reference
  • Neo4j quick reference
  • Cross-database conventions
  • Performance debugging checklist

When to use which database

WorkloadUseWhy
Transactional (CRUD, users, sessions)PostgreSQLACID, row-level locking, indexes
Analytical (aggregations, scans)DuckDBColumnar, vectorized, parallel
Data storage/interchangeParquetCompressed, columnar, portable
Metadata + relationshipsPostgreSQLForeign keys, constraints
Ad-hoc explorationDuckDBFast on Parquet, no ETL needed
Time-series with point lookupsPostgreSQL + partitioningPartition pruning + indexes
Time-series analyticsDuckDB on ParquetScan performance
Vector similarity searchPostgreSQL + PGVectorHNSW/IVFFlat indexes, hybrid search
RAG / semantic searchPostgreSQL + PGVectorEmbeddings + metadata in same DB
Graph traversals / relationshipsNeo4jNative graph, index-free adjacency
Pattern matching / fraud detectionNeo4jMulti-hop traversal, path finding
Knowledge graphs / ontologiesNeo4jFlexible schema, relationship-first

Hybrid pattern example:

  • PostgreSQL: transactional data, relationships, users (metadata)
  • DuckDB + Parquet: analytical content, aggregations, time-series

PostgreSQL quick reference

Use for: Metadata, relationships, OLTP workloads, anything needing ACID.

Key decisions:

  • Partition tables >100M rows or with retention requirements
  • Index columns in WHERE/JOIN clauses, not everything
  • Tune autovacuum for high-churn tables

See references/postgres-architecture.md for maintenance patterns. See references/postgres-querying.md for advanced query techniques.

DuckDB quick reference

Use for: Analytics, aggregations, Parquet queries, data exploration.

Key decisions:

  • Prefer Parquet files over CSV (10-100x faster)
  • Let DuckDB auto-parallelize; don't micro-optimize
  • For remote data, increase threads beyond CPU count

See references/duckdb-architecture.md for storage and parallelism. See references/duckdb-querying.md for DuckDB-specific SQL features.

Parquet quick reference

Use for: Storing analytical data, data interchange, columnar compression.

Key decisions:

  • Target 128MB-1GB file sizes
  • Partition by low-to-moderate cardinality columns (date, region)
  • Sort by columns used in filters for better pruning

See references/parquet-architecture.md for file design. See references/parquet-querying.md for query optimization.

PGVector quick reference

Use for: Similarity search, RAG applications, semantic search, recommendations.

Key decisions:

  • HNSW for low-latency, high-recall (default choice)
  • IVFFlat for memory-constrained or batch-updated data
  • Use iterative scan for filtered queries
  • Consider hybrid search (vector + keyword) for 8-15% accuracy boost

See references/pgvector-architecture.md for index configuration. See references/pgvector-querying.md for hybrid search and filtering.

Show full SKILL.md (285 more words)Show less

Neo4j quick reference

Use for: Graph traversals, relationship-heavy queries, pattern matching, knowledge graphs.

Key decisions:

  • Model around your queries, not your source data
  • Promote properties to nodes when you need to traverse through shared values
  • Use specific relationship types to avoid supernode bottlenecks
  • Bound all variable-length paths ([*1..5], never [*])
  • Use parameters in Cypher for execution plan caching

See references/neo4j-architecture.md for data modeling, indexing, and maintenance. See references/neo4j-querying.md for Cypher optimization and anti-patterns.

Performance debugging checklist

PostgreSQL slow query
  1. Run EXPLAIN (ANALYZE, BUFFERS) on the query
  2. Check for sequential scans on large tables
  3. Verify indexes exist on filter/join columns
  4. Check pg_stat_user_tables for bloat (dead tuples)
  5. Review work_mem if seeing disk sorts
DuckDB slow query
  1. Check if reading CSV instead of Parquet
  2. Verify not doing SELECT * on remote data
  3. Check thread count matches workload
  4. Look for unnecessary type conversions
Parquet slow reads
  1. Verify predicate pushdown is working (check query plan)
  2. Check file sizes (too small = overhead, too large = no parallelism)
  3. Confirm data is sorted by filter columns
  4. Look for high-cardinality partition keys (too many small files)
  1. Verify index exists and is being used (EXPLAIN)
  2. Check ef_search (HNSW) or probes (IVFFlat) settings
  3. Enable iterative scan for filtered queries
  4. Check if IVFFlat recall degraded (rebuild index if heavily updated)
  5. Consider partial indexes for common filters
Neo4j slow query
  1. Run PROFILE on the query, read operators bottom-up
  2. Look for AllNodesScan or NodeByLabelScan (missing index)
  3. Check for CartesianProduct (disconnected MATCH patterns)
  4. Verify parameters are used instead of literals (plan caching)
  5. Check for unbounded variable-length paths
  6. Monitor page_cache.hit_ratio (below 98% = need more page cache memory)

© rileyhilliard, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in plugins/ce/skills/managing-databases of rileyhilliard/claude-essentials.

  • SKILL.md
  • references/duckdb-architecture.md
  • references/duckdb-querying.md
  • references/neo4j-architecture.md
  • references/neo4j-querying.md
  • references/parquet-architecture.md
  • references/parquet-querying.md
  • references/pgvector-architecture.md
  • references/pgvector-querying.md
  • references/postgres-architecture.md
  • references/postgres-querying.md

Open the folder on GitHubat commit 3a67a01

Compare with similar skills

Managing Databases next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Managing Databases compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Managing Databases this skillrileyhilliard/claude-essentials130—~1.6kAutomated safety check: PassMIT
Ops Telemetry Queryboundless-xyz/boundless193—~3.8kAutomated safety check: PassApache-2.0
Langchain4j Vector Stores Configurationgiuseppe-trisciuoglio/developer-kit3551 repos~2.7kAutomated safety check: NotesMIT
Altimate Data Warehouse DelegateAltimateAI/data-engineering-skills127—~1.4kAutomated safety check: PassMIT
Gaik ToolkitGAIK-project/gaik-toolkit100—~5.7kAutomated safety check: PassMIT
Neo4j Aura Graph Analytics Skillneo4j-contrib/neo4j-skills114—~4.6kAutomated safety check: NotesMIT

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Questions about Managing Databases

What does Managing Databases do?

Guides database architecture for PostgreSQL, DuckDB, Parquet, PGVector, and Neo4j. Managing Databases is an agent skill from rileyhilliard/claude-essentials. Guides database architecture for PostgreSQL, DuckDB, Parquet, PGVector, and Neo4j.

When should I use Managing Databases?

Managing Databases fits situations like: designing schemas; choosing storage strategies; optimizing queries; configuring vector.

How do I install Managing Databases in Claude Code?

Run `npx skills add rileyhilliard/claude-essentials --skill managing-databases -a claude-code`. Or copy the skill folder (plugins/ce/skills/managing-databases in rileyhilliard/claude-essentials) into .claude/skills/managing-databases in your project. Claude Code loads it when a task matches its description.

How do I install Managing Databases in Codex?

Run `npx skills add rileyhilliard/claude-essentials --skill managing-databases -a codex`. Or copy the skill folder (plugins/ce/skills/managing-databases in rileyhilliard/claude-essentials) into .agents/skills/managing-databases in your project. Codex loads it when a task matches its description.

Can I use Managing Databases in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rileyhilliard/claude-essentials --skill managing-databases -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/managing-databases, .gemini/skills/managing-databases, .github/skills/managing-databases and .opencode/skills/managing-databases in your project.

What does Managing Databases need to run?

SKILL.md names no scripts, command-line tools or credentials: Managing Databases is instructions for the agent only.

Does Managing Databases access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Managing Databases safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Managing Databases use?

Managing Databases is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Managing Databases use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 19k tokens, read only when the agent opens those files.

What are the alternatives to Managing Databases?

Skills that share tags, products or a category with Managing Databases: Ops Telemetry Query (boundless-xyz/boundless, 193 stars), Langchain4j Vector Stores Configuration (giuseppe-trisciuoglio/developer-kit, 355 stars), Altimate Data Warehouse Delegate (AltimateAI/data-engineering-skills, 127 stars) and Gaik Toolkit (GAIK-project/gaik-toolkit, 100 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Managing Databases?

rileyhilliard (a GitHub user) maintains it in rileyhilliard/claude-essentials, which has 130 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on August 18, 2026.

Source: rileyhilliard/claude-essentials on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.