Postgresql

PostgreSQL Table Design

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/postgresql from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

PostgreSQL Table Design

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Safety, PostgreSQL “Gotchas”, Data Types, Table Types, Row-Level Security, Constraints.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Designing a schema for PostgreSQL
• Selecting data types and constraints
• Planning indexes, partitions, or RLS policies
• Reviewing tables for scale and maintainability
• You are targeting a non-PostgreSQL database
• You only need query tuning without schema changes

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Capture entities, access patterns, and scale targets (rows, QPS, retention).
2. Choose data types and constraints that enforce invariants.
3. Add indexes for real query paths and validate with EXPLAIN.
4. Plan partitioning or RLS where required by scale or access control.
5. Review migration impact and apply changes safely.
6. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
7. Read the overview and provenance files before loading any copied upstream support files.

Imported Workflow Notes

Imported: Instructions

1. Capture entities, access patterns, and scale targets (rows, QPS, retention).
2. Choose data types and constraints that enforce invariants.
3. Add indexes for real query paths and validate with EXPLAIN.
4. Plan partitioning or RLS where required by scale or access control.
5. Review migration impact and apply changes safely.

Imported: Safety

• Avoid destructive DDL on production without backups and a rollback plan.
• Use migrations and staging validation before applying schema changes.

Examples

Example 1: Ask for the upstream workflow directly

Use @postgresql to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @postgresql against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @postgresql for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @postgresql using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

Users

CREATE TABLE users (
  user_id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
  email TEXT NOT NULL UNIQUE,
  name TEXT NOT NULL,
  created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);
CREATE UNIQUE INDEX ON users (LOWER(email));
CREATE INDEX ON users (created_at);

Orders

CREATE TABLE orders (
  order_id BIGINT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
  user_id BIGINT NOT NULL REFERENCES users(user_id),
  status TEXT NOT NULL DEFAULT 'PENDING' CHECK (status IN ('PENDING','PAID','CANCELED')),
  total NUMERIC(10,2) NOT NULL CHECK (total > 0),
  created_at TIMESTAMPTZ NOT NULL DEFAULT now()
);
CREATE INDEX ON orders (user_id);
CREATE INDEX ON orders (created_at);

JSONB

CREATE TABLE profiles (
  user_id BIGINT PRIMARY KEY REFERENCES users(user_id),
  attrs JSONB NOT NULL DEFAULT '{}',
  theme TEXT GENERATED ALWAYS AS (attrs->>'theme') STORED
);
CREATE INDEX profiles_attrs_gin ON profiles USING GIN (attrs);

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Define a PRIMARY KEY for reference tables (users, orders, etc.). Not always needed for time-series/event/log data. When used, prefer BIGINT GENERATED ALWAYS AS IDENTITY; use UUID only when global uniqueness/opacity is needed.
• Normalize first (to 3NF) to eliminate data redundancy and update anomalies; denormalize only for measured, high-ROI reads where join performance is proven problematic. Premature denormalization creates maintenance burden.
• Add NOT NULL everywhere it’s semantically required; use DEFAULTs for common values.
• Create indexes for access paths you actually query: PK/unique (auto), FK columns (manual!), frequent filters/sorts, and join keys.
• Prefer TIMESTAMPTZ for event time; NUMERIC for money; TEXT for strings; BIGINT for integer values, DOUBLE PRECISION for floats (or NUMERIC for exact decimal arithmetic).
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.

Imported Operating Notes

Imported: Core Rules

• Define a PRIMARY KEY for reference tables (users, orders, etc.). Not always needed for time-series/event/log data. When used, prefer BIGINT GENERATED ALWAYS AS IDENTITY; use UUID only when global uniqueness/opacity is needed.
• Normalize first (to 3NF) to eliminate data redundancy and update anomalies; denormalize only for measured, high-ROI reads where join performance is proven problematic. Premature denormalization creates maintenance burden.
• Add NOT NULL everywhere it’s semantically required; use DEFAULTs for common values.
• Create indexes for access paths you actually query: PK/unique (auto), FK columns (manual!), frequent filters/sorts, and join keys.
• Prefer TIMESTAMPTZ for event time; NUMERIC for money; TEXT for strings; BIGINT for integer values, DOUBLE PRECISION for floats (or NUMERIC for exact decimal arithmetic).

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/postgresql, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Indexing

• B-tree: default for equality/range queries (=, <, >, BETWEEN, ORDER BY)
• Composite: order matters—index used if equality on leftmost prefix (WHERE a = ? AND b > ? uses index on (a,b), but WHERE b = ? does not). Put most selective/frequently filtered columns first.
• Covering: CREATE INDEX ON tbl (id) INCLUDE (name, email) - includes non-key columns for index-only scans without visiting table.
• Partial: for hot subsets (WHERE status = 'active' → CREATE INDEX ON tbl (user_id) WHERE status = 'active'). Any query with status = 'active' can use this index.
• Expression: for computed search keys (CREATE INDEX ON tbl (LOWER(email))). Expression must match exactly in WHERE clause: WHERE LOWER(email) = 'user@example.com'.
• GIN: JSONB containment/existence, arrays (@>, ?), full-text search (@@)
• GiST: ranges, geometry, exclusion constraints
• BRIN: very large, naturally ordered data (time-series)—minimal storage overhead. Effective when row order on disk correlates with indexed column (insertion order or after CLUSTER).

Imported: PostgreSQL “Gotchas”

• Identifiers: unquoted → lowercased. Avoid quoted/mixed-case names. Convention: use snake_case for table/column names.
• Unique + NULLs: UNIQUE allows multiple NULLs. Use UNIQUE (...) NULLS NOT DISTINCT (PG15+) to restrict to one NULL.
• FK indexes: PostgreSQL does not auto-index FK columns. Add them.
• No silent coercions: length/precision overflows error out (no truncation). Example: inserting 999 into NUMERIC(2,0) fails with error, unlike some databases that silently truncate or round.
• Sequences/identity have gaps (normal; don't "fix"). Rollbacks, crashes, and concurrent transactions create gaps in ID sequences (1, 2, 5, 6...). This is expected behavior—don't try to make IDs consecutive.
• Heap storage: no clustered PK by default (unlike SQL Server/MySQL InnoDB); CLUSTER is one-off reorganization, not maintained on subsequent inserts. Row order on disk is insertion order unless explicitly clustered.
• MVCC: updates/deletes leave dead tuples; vacuum handles them—design to avoid hot wide-row churn.

Imported: Data Types

• IDs: BIGINT GENERATED ALWAYS AS IDENTITY preferred (GENERATED BY DEFAULT also fine); UUID when merging/federating/used in a distributed system or for opaque IDs. Generate with uuidv7() (preferred if using PG18+) or gen_random_uuid() (if using an older PG version).
• Integers: prefer BIGINT unless storage space is critical; INTEGER for smaller ranges; avoid SMALLINT unless constrained.
• Floats: prefer DOUBLE PRECISION over REAL unless storage space is critical. Use NUMERIC for exact decimal arithmetic.
• Strings: prefer TEXT; if length limits needed, use CHECK (LENGTH(col) <= n) instead of VARCHAR(n); avoid CHAR(n). Use BYTEA for binary data. Large strings/binary (>2KB default threshold) automatically stored in TOAST with compression. TOAST storage: PLAIN (no TOAST), EXTENDED (compress + out-of-line), EXTERNAL (out-of-line, no compress), MAIN (compress, keep in-line if possible). Default EXTENDED usually optimal. Control with ALTER TABLE tbl ALTER COLUMN col SET STORAGE strategy and ALTER TABLE tbl SET (toast_tuple_target = 4096) for threshold. Case-insensitive: for locale/accent handling use non-deterministic collations; for plain ASCII use expression indexes on LOWER(col) (preferred unless column needs case-insensitive PK/FK/UNIQUE) or CITEXT.
• Money: NUMERIC(p,s) (never float).
• Time: TIMESTAMPTZ for timestamps; DATE for date-only; INTERVAL for durations. Avoid TIMESTAMP (without timezone). Use now() for transaction start time, clock_timestamp() for current wall-clock time.
• Booleans: BOOLEAN with NOT NULL constraint unless tri-state values are required.
• Enums: CREATE TYPE ... AS ENUM for small, stable sets (e.g. US states, days of week). For business-logic-driven and evolving values (e.g. order statuses) → use TEXT (or INT) + CHECK or lookup table.
• Arrays: TEXT[], INTEGER[], etc. Use for ordered lists where you query elements. Index with GIN for containment (@>, <@) and overlap (&&) queries. Access: arr[1] (1-indexed), arr[1:3] (slicing). Good for tags, categories; avoid for relations—use junction tables instead. Literal syntax: '{val1,val2}' or ARRAY[val1,val2].
• Range types: daterange, numrange, tstzrange for intervals. Support overlap (&&), containment (@>), operators. Index with GiST. Good for scheduling, versioning, numeric ranges. Pick a bounds scheme and use it consistently; prefer [) (inclusive/exclusive) by default.
• Network types: INET for IP addresses, CIDR for network ranges, MACADDR for MAC addresses. Support network operators (<<, >>, &&).
• Geometric types: POINT, LINE, POLYGON, CIRCLE for 2D spatial data. Index with GiST. Consider PostGIS for advanced spatial features.
• Text search: TSVECTOR for full-text search documents, TSQUERY for search queries. Index tsvector with GIN. Always specify language: to_tsvector('english', col) and to_tsquery('english', 'query'). Never use single-argument versions. This applies to both index expressions and queries.
• Domain types: CREATE DOMAIN email AS TEXT CHECK (VALUE ~ '^[^@]+@[^@]+$') for reusable custom types with validation. Enforces constraints across tables.
• Composite types: CREATE TYPE address AS (street TEXT, city TEXT, zip TEXT) for structured data within columns. Access with (col).field syntax.
• JSONB: preferred over JSON; index with GIN. Use only for optional/semi-structured attrs. ONLY use JSON if the original ordering of the contents MUST be preserved.
• Vector types: vector type by pgvector for vector similarity search for embeddings.

Do not use the following data types

• DO NOT use timestamp (without time zone); DO use timestamptz instead.
• DO NOT use char(n) or varchar(n); DO use text instead.
• DO NOT use money type; DO use numeric instead.
• DO NOT use timetz type; DO use timestamptz instead.
• DO NOT use timestamptz(0) or any other precision specification; DO use timestamptz instead
• DO NOT use serial type; DO use generated always as identity instead.

Imported: Table Types

• Regular: default; fully durable, logged.
• TEMPORARY: session-scoped, auto-dropped, not logged. Faster for scratch work.
• UNLOGGED: persistent but not crash-safe. Faster writes; good for caches/staging.

Imported: Row-Level Security

Enable with ALTER TABLE tbl ENABLE ROW LEVEL SECURITY. Create policies: CREATE POLICY user_access ON orders FOR SELECT TO app_users USING (user_id = current_user_id()). Built-in user-based access control at the row level.

Imported: Constraints

• PK: implicit UNIQUE + NOT NULL; creates a B-tree index.
• FK: specify ON DELETE/UPDATE action (CASCADE, RESTRICT, SET NULL, SET DEFAULT). Add explicit index on referencing column—speeds up joins and prevents locking issues on parent deletes/updates. Use DEFERRABLE INITIALLY DEFERRED for circular FK dependencies checked at transaction end.
• UNIQUE: creates a B-tree index; allows multiple NULLs unless NULLS NOT DISTINCT (PG15+). Standard behavior: (1, NULL) and (1, NULL) are allowed. With NULLS NOT DISTINCT: only one (1, NULL) allowed. Prefer NULLS NOT DISTINCT unless you specifically need duplicate NULLs.
• CHECK: row-local constraints; NULL values pass the check (three-valued logic). Example: CHECK (price > 0) allows NULL prices. Combine with NOT NULL to enforce: price NUMERIC NOT NULL CHECK (price > 0).
• EXCLUDE: prevents overlapping values using operators. EXCLUDE USING gist (room_id WITH =, booking_period WITH &&) prevents double-booking rooms. Requires appropriate index type (often GiST).

Imported: Partitioning

• Use for very large tables (>100M rows) where queries consistently filter on partition key (often time/date).
• Alternate use: use for tables where data maintenance tasks dictates e.g. data pruned or bulk replaced periodically
• RANGE: common for time-series (PARTITION BY RANGE (created_at)). Create partitions: CREATE TABLE logs_2024_01 PARTITION OF logs FOR VALUES FROM ('2024-01-01') TO ('2024-02-01'). TimescaleDB automates time-based or ID-based partitioning with retention policies and compression.
• LIST: for discrete values (PARTITION BY LIST (region)). Example: FOR VALUES IN ('us-east', 'us-west').
• HASH: for even distribution when no natural key (PARTITION BY HASH (user_id)). Creates N partitions with modulus.
• Constraint exclusion: requires CHECK constraints on partitions for query planner to prune. Auto-created for declarative partitioning (PG10+).
• Prefer declarative partitioning or hypertables. Do NOT use table inheritance.
• Limitations: no global UNIQUE constraints—include partition key in PK/UNIQUE. FKs from partitioned tables not supported; use triggers.

Imported: Special Considerations

Update-Heavy Tables

• Separate hot/cold columns—put frequently updated columns in separate table to minimize bloat.
• Use fillfactor=90 to leave space for HOT updates that avoid index maintenance.
• Avoid updating indexed columns—prevents beneficial HOT updates.
• Partition by update patterns—separate frequently updated rows in a different partition from stable data.

Insert-Heavy Workloads

• Minimize indexes—only create what you query; every index slows inserts.
• Use COPY or multi-row INSERT instead of single-row inserts.
• UNLOGGED tables for rebuildable staging data—much faster writes.
• Defer index creation for bulk loads—>drop index, load data, recreate indexes.
• Partition by time/hash to distribute load. TimescaleDB automates partitioning and compression of insert-heavy data.
• Use a natural key for primary key such as a (timestamp, device_id) if enforcing global uniqueness is important many insert-heavy tables don't need a primary key at all.
• If you do need a surrogate key, Prefer BIGINT GENERATED ALWAYS AS IDENTITY over UUID.

Upsert-Friendly Design

• Requires UNIQUE index on conflict target columns—ON CONFLICT (col1, col2) needs exact matching unique index (partial indexes don't work).
• Use EXCLUDED.column to reference would-be-inserted values; only update columns that actually changed to reduce write overhead.
• DO NOTHING faster than DO UPDATE when no actual update needed.

Safe Schema Evolution

• Transactional DDL: most DDL operations can run in transactions and be rolled back—BEGIN; ALTER TABLE...; ROLLBACK; for safe testing.
• Concurrent index creation: CREATE INDEX CONCURRENTLY avoids blocking writes but can't run in transactions.
• Volatile defaults cause rewrites: adding NOT NULL columns with volatile defaults (e.g., now(), gen_random_uuid()) rewrites entire table. Non-volatile defaults are fast.
• Drop constraints before columns: ALTER TABLE DROP CONSTRAINT then DROP COLUMN to avoid dependency issues.
• Function signature changes: CREATE OR REPLACE with different arguments creates overloads, not replacements. DROP old version if no overload desired.

Imported: Generated Columns

• ... GENERATED ALWAYS AS (<expr>) STORED for computed, indexable fields. PG18+ adds VIRTUAL columns (computed on read, not stored).

Imported: Extensions

• pgcrypto: crypt() for password hashing.
• uuid-ossp: alternative UUID functions; prefer pgcrypto for new projects.
• pg_trgm: fuzzy text search with % operator, similarity() function. Index with GIN for LIKE '%pattern%' acceleration.
• citext: case-insensitive text type. Prefer expression indexes on LOWER(col) unless you need case-insensitive constraints.
• btree_gin/btree_gist: enable mixed-type indexes (e.g., GIN index on both JSONB and text columns).
• hstore: key-value pairs; mostly superseded by JSONB but useful for simple string mappings.
• timescaledb: essential for time-series—automated partitioning, retention, compression, continuous aggregates.
• postgis: comprehensive geospatial support beyond basic geometric types—essential for location-based applications.
• pgvector: vector similarity search for embeddings.
• pgaudit: audit logging for all database activity.

Imported: JSONB Guidance

• Prefer JSONB with GIN index.
• Default: CREATE INDEX ON tbl USING GIN (jsonb_col); → accelerates:
  • Containment jsonb_col @> '{"k":"v"}'
  • Key existence jsonb_col ? 'k', any/all keys ?\|, ?&
  • Path containment on nested docs
  • Disjunction jsonb_col @> ANY(ARRAY['{"status":"active"}', '{"status":"pending"}'])
• Heavy @> workloads: consider opclass jsonb_path_ops for smaller/faster containment-only indexes:
  • CREATE INDEX ON tbl USING GIN (jsonb_col jsonb_path_ops);
  • Trade-off: loses support for key existence (?, ?|, ?&) queries—only supports containment (@>)
• Equality/range on a specific scalar field: extract and index with B-tree (generated column or expression):
  • ALTER TABLE tbl ADD COLUMN price INT GENERATED ALWAYS AS ((jsonb_col->>'price')::INT) STORED;
  • CREATE INDEX ON tbl (price);
  • Prefer queries like WHERE price BETWEEN 100 AND 500 (uses B-tree) over WHERE (jsonb_col->>'price')::INT BETWEEN 100 AND 500 without index.
• Arrays inside JSONB: use GIN + @> for containment (e.g., tags). Consider jsonb_path_ops if only doing containment.
• Keep core relations in tables; use JSONB for optional/variable attributes.
• Use constraints to limit allowed JSONB values in a column e.g. config JSONB NOT NULL CHECK(jsonb_typeof(config) = 'object')