Agent skill

Migrate Postgres Tables To Hypertables

by timescale in timescale/pg-aiguide

A skill your agent uses to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.

Apache-2.0Auto-check: warningsDatabases

Install Migrate Postgres Tables To Hypertables

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add timescale/pg-aiguide --skill migrate-postgres-tables-to-hypertables -a claude-code

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

GitHub CLI
$ gh skill install timescale/pg-aiguide migrate-postgres-tables-to-hypertables --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/timescale/pg-aiguide.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/migrate-postgres-tables-to-hypertables .claude/skills/migrate-postgres-tables-to-hypertables && 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
migrate-postgres-tables-to-hypertables
GitHub stars
1.9k
Used in
1 other repo
Token cost
~3.8k tokens
SKILL.md length
788 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.

  • Works in 3 steps: Optimal Configuration → Migration Planning → Performance Validation
  • Migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation
  • SKILL.md covers Step 1: Optimal Configuration, Step 2: Migration Planning, Step 3: Performance Validation and Success Criteria
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Migrate Postgres Tables To Hypertables is an agent skill from timescale/pg-aiguide. Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. Trigger when user asks to: - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration Prerequisites: Tables already identified as candidates (use find-hypertable-candidates first if needed) Keywords: migrate to…

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires PostgreSQL 15+ with TimescaleDB

It sits in Databases, covering Deployment. It works with PostgreSQL and Model Context Protocol. The repository describes itself as: MCP server and Claude plugin for Postgres skills and documentation. Helps AI coding tools generate better PostgreSQL code. The licence is Apache-2.0.

When your agent uses it

  • Migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation
  • User asks to: - Migrate
  • Blue-green migration
  • In-place conversion

Example prompts

  • “/migrate-postgres-tables-to-hypertables”

Requirements

  • Compatibility (from SKILL.md): Requires PostgreSQL 15+ with TimescaleDB

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Optimal Configuration
  2. Migration Planning
  3. Performance Validation

What it can do on your machine

Read from SKILL.md and the folder at commit 187be00. 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 (its code samples are sql).

    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.

  • Compatibility

    Requires PostgreSQL 15+ with TimescaleDB

    From compatibility in the SKILL.md frontmatter.

Context cost

Migrate Postgres Tables To Hypertables loads about 3.8k tokens when it runs. Until then it costs about 233 tokens; SKILL.md has 788 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~233
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

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: warnings

The automated check found patterns that need a careful read before installing.

  • WarningContains instruction-override wording (e.g. “without asking the user”)SKILL.md:138
    odify the primary key/unique constraint without user permission.

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 timescale/pg-aiguide at commit 187be00, republished under its Apache-2.0 licence (© timescale). 788 words, ~3,758 tokens.

Download SKILL.mdSave it as .claude/skills/migrate-postgres-tables-to-hypertables/SKILL.md (or your agent's skills folder).
name
migrate-postgres-tables-to-hypertables
description
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.
compatibility
Requires PostgreSQL 15+ with TimescaleDB
license
Apache-2.0
metadata.author
tigerdata

PostgreSQL to TimescaleDB Hypertable Migration

Migrate identified PostgreSQL tables to TimescaleDB hypertables with optimal configuration, migration planning and validation.

Prerequisites: Tables already identified as hypertable candidates (use companion "find-hypertable-candidates" skill if needed).

Step 1: Optimal Configuration

Partition Column Selection
sql
-- Find potential partition columns
SELECT column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_name = 'your_table_name'
  AND data_type IN ('timestamp', 'timestamptz', 'bigint', 'integer', 'date')
ORDER BY ordinal_position;

Requirements: Time-based (TIMESTAMP/TIMESTAMPTZ/DATE) or sequential integer (INT/BIGINT)

Should represent when the event actually occurred or sequential ordering.

Common choices:

  • timestamp, created_at, event_time - when event occurred
  • id, sequence_number - auto-increment (for sequential data without timestamps)
  • ingested_at - less ideal, only if primary query dimension
  • updated_at - AVOID (records updated out of order, breaks chunk distribution) unless primary query dimension
Special Case: table with BOTH ID AND Timestamp

When table has sequential ID (PK) AND timestamp that correlate:

sql
-- Partition by ID, enable minmax sparse indexes on timestamp
SELECT create_hypertable('orders', 'id', chunk_time_interval => 1000000);
ALTER TABLE orders SET (
    timescaledb.sparse_index = 'minmax(created_at),...'
);

Sparse indexes on time column enable skipping compressed blocks outside queried time ranges.

Use when: ID correlates with time (newer records have higher IDs), need ID-based lookups, time queries also common

Chunk Interval Selection
sql
-- Ensure statistics are current
ANALYZE your_table_name;

-- Estimate index size per time unit
WITH time_range AS (
    SELECT
        MIN(timestamp_column) as min_time,
        MAX(timestamp_column) as max_time,
        EXTRACT(EPOCH FROM (MAX(timestamp_column) - MIN(timestamp_column)))/3600 as total_hours
    FROM your_table_name
),
total_index_size AS (
    SELECT SUM(pg_relation_size(indexname::regclass)) as total_index_bytes
    FROM pg_stat_user_indexes
    WHERE schemaname||'.'||tablename = 'your_schema.your_table_name'
)
SELECT
    pg_size_pretty(tis.total_index_bytes / tr.total_hours) as index_size_per_hour
FROM time_range tr, total_index_size tis;

Target: Indexes of recent chunks < 25% of RAM Default: IMPORTANT: Keep default of 7 days if unsure Range: 1 hour minimum, 30 days maximum

Example: 32GB RAM → target 8GB for recent indexes. If index_size_per_hour = 200MB:

  • 1 hour chunks: 200MB chunk index size × 40 recent = 8GB ✓
  • 6 hour chunks: 1.2GB chunk index size × 7 recent = 8.4GB ✓
  • 1 day chunks: 4.8GB chunk index size × 2 recent = 9.6GB ⚠️ Choose largest interval keeping 2+ recent chunk indexes under target.
Primary Key/ Unique Constraints Compatibility
sql
-- Check existing primary key/ unique constraints
SELECT conname, pg_get_constraintdef(oid) as definition
FROM pg_constraint
WHERE conrelid = 'your_table_name'::regclass AND contype = 'p' OR contype = 'u';

Rules: PK/UNIQUE must include partition column

Actions:

  1. No PK/UNIQUE: No changes needed
  2. PK/UNIQUE includes partition column: No changes needed
  3. PK/UNIQUE excludes partition column: ⚠️ ASK USER PERMISSION to modify PK/UNIQUE

Example: user prompt if needed:

"Primary key (id) doesn't include partition column (timestamp). Must modify to PRIMARY KEY (id, timestamp) to convert to hypertable. This may break application code. Is this acceptable?" "Unique constraint (id) doesn't include partition column (timestamp). Must modify to UNIQUE (id, timestamp) to convert to hypertable. This may break application code. Is this acceptable?"

If the user accepts, modify the constraint:

sql
BEGIN;
ALTER TABLE your_table_name DROP CONSTRAINT existing_pk_name;
ALTER TABLE your_table_name ADD PRIMARY KEY (existing_columns, partition_column);
COMMIT;

If the user does not accept, you should NOT migrate the table.

IMPORTANT: DO NOT modify the primary key/unique constraint without user permission.

Compression Configuration

For detailed segment_by and order_by selection, see "setup-timescaledb-hypertables" skill. Quick reference:

segment_by: Most common WHERE filter with >100 rows per value per chunk

  • IoT: device_id
  • Finance: symbol
  • Analytics: user_id or session_id
sql
-- Analyze cardinality for segment_by selection
SELECT column_name, COUNT(DISTINCT column_name) as unique_values,
       ROUND(COUNT(*)::float / COUNT(DISTINCT column_name), 2) as avg_rows_per_value
FROM your_table_name GROUP BY column_name;

order_by: Usually timestamp DESC. The (segment_by, order_by) combination should form a natural time-series progression.

  • If column has <100 rows/chunk (too low for segment_by), prepend to order_by: order_by='low_density_col, timestamp DESC'

sparse indexes: add minmax on the columns that are used in the WHERE clauses but are not in the segment_by or order_by. Use minmax for columns used in range queries.

sql
ALTER TABLE your_table_name SET (
    timescaledb.enable_columnstore,
    timescaledb.segmentby = 'entity_id',
    timescaledb.orderby = 'timestamp DESC'
    timescaledb.sparse_index = 'minmax(value_1),...'
);

-- Compress after data unlikely to change (adjust `after` parameter based on update patterns)
CALL add_columnstore_policy('your_table_name', after => INTERVAL '7 days');

Step 2: Migration Planning

Pre-Migration Checklist
  • Partition column selected
  • Chunk interval calculated (or using default)
  • PK includes partition column OR user approved modification
  • No Hypertable→Hypertable foreign keys
  • Unique constraints include partition column
  • Created compression configuration (segment_by, order_by, sparse indexes, compression policy)
  • Maintenance window scheduled / backup created.
Show full SKILL.md (304 more words)Show less
Migration Options
Option 1: In-Place (Tables < 1GB)
sql
-- Enable extension
CREATE EXTENSION IF NOT EXISTS timescaledb;

-- Convert to hypertable (locks table)
SELECT create_hypertable(
    'your_table_name',
    'timestamp_column',
    chunk_time_interval => INTERVAL '7 days',
    if_not_exists => TRUE
);

-- Configure compression
ALTER TABLE your_table_name SET (
    timescaledb.enable_columnstore,
    timescaledb.segmentby = 'entity_id',
    timescaledb.orderby = 'timestamp DESC',
    timescaledb.sparse_index = 'minmax(value_1),...'
);

-- Adjust `after` parameter based on update patterns
CALL add_columnstore_policy('your_table_name', after => INTERVAL '7 days');
Option 2: Blue-Green (Tables > 1GB)
sql
-- 1. Create new hypertable
CREATE TABLE your_table_name_new (LIKE your_table_name INCLUDING ALL);

-- 2. Convert to hypertable
SELECT create_hypertable('your_table_name_new', 'timestamp_column');

-- 3. Configure compression
ALTER TABLE your_table_name_new SET (
    timescaledb.enable_columnstore,
    timescaledb.segmentby = 'entity_id',
    timescaledb.orderby = 'timestamp DESC'
);

-- 4. Migrate data in batches
INSERT INTO your_table_name_new
SELECT * FROM your_table_name
WHERE timestamp_column >= '2024-01-01' AND timestamp_column < '2024-02-01';
-- Repeat for each time range

-- 4. Enter maintenance window and do the following:

-- 5. Pause modification of the old table.

-- 6. Copy over the most recent data from the old table to the new table.

-- 7. Swap tables
BEGIN;
ALTER TABLE your_table_name RENAME TO your_table_name_old;
ALTER TABLE your_table_name_new RENAME TO your_table_name;
COMMIT;

-- 8. Exit maintenance window.

-- 9. (sometime much later) Drop old table after validation
-- DROP TABLE your_table_name_old;
Common Issues
Foreign Keys
sql
-- Check foreign keys
SELECT conname, confrelid::regclass as referenced_table
FROM pg_constraint
WHERE (conrelid = 'your_table_name'::regclass
    OR confrelid = 'your_table_name'::regclass)
  AND contype = 'f';

Supported: Plain→Hypertable, Hypertable→Plain NOT supported: Hypertable→Hypertable

⚠️ CRITICAL: Hypertable→Hypertable FKs must be dropped (enforce in application). ASK USER PERMISSION. If no, STOP MIGRATION.

Large Table Migration Time
sql
-- Rough estimate: ~75k rows/second
SELECT
    pg_size_pretty(pg_total_relation_size(tablename)) as size,
    n_live_tup as rows,
    ROUND(n_live_tup / 75000.0 / 60, 1) as estimated_minutes
FROM pg_stat_user_tables
WHERE tablename = 'your_table_name';

Solutions for large tables (>1GB/10M rows): Use blue-green migration, migrate during off-peak, test on subset first

Step 3: Performance Validation

Chunk & Compression Analysis
sql
-- View chunks and compression
SELECT
    chunk_name,
    pg_size_pretty(total_bytes) as size,
    pg_size_pretty(compressed_total_bytes) as compressed_size,
    ROUND((total_bytes - compressed_total_bytes::numeric) / total_bytes * 100, 1) as compression_pct,
    range_start,
    range_end
FROM timescaledb_information.chunks
WHERE hypertable_name = 'your_table_name'
ORDER BY range_start DESC;

Look for:

  • Consistent chunk sizes (within 2x)
  • Compression >90% for time-series
  • Recent chunks uncompressed
  • Chunk indexes < 25% RAM
Query Performance Tests
sql
-- 1. Time-range query (should show chunk exclusion)
EXPLAIN (ANALYZE, BUFFERS)
SELECT COUNT(*), AVG(value)
FROM your_table_name
WHERE timestamp >= NOW() - INTERVAL '1 day';

-- 2. Entity + time query (benefits from segment_by)
EXPLAIN (ANALYZE, BUFFERS)
SELECT * FROM your_table_name
WHERE entity_id = 'X' AND timestamp >= NOW() - INTERVAL '1 week';

-- 3. Aggregation (benefits from columnstore)
EXPLAIN (ANALYZE, BUFFERS)
SELECT DATE_TRUNC('hour', timestamp), entity_id, COUNT(*), AVG(value)
FROM your_table_name
WHERE timestamp >= NOW() - INTERVAL '1 month'
GROUP BY 1, 2;

✅ Good signs:

  • "Chunks excluded during startup: X" in EXPLAIN plan
  • "Custom Scan (ColumnarScan)" for compressed data
  • Lower "Buffers: shared read" in EXPLAIN ANALYZE plan than pre-migration
  • Faster execution times

❌ Bad signs:

  • "Seq Scan" on large chunks
  • No chunk exclusion messages
  • Slower than before migration
Storage Metrics
sql
-- Monitor compression effectiveness
SELECT
    hypertable_name,
    pg_size_pretty(total_bytes) as total_size,
    pg_size_pretty(compressed_total_bytes) as compressed_size,
    ROUND(compressed_total_bytes::numeric / total_bytes * 100, 1) as compressed_pct_of_total,
    ROUND((uncompressed_total_bytes - compressed_total_bytes::numeric) /
          uncompressed_total_bytes * 100, 1) as compression_ratio_pct
FROM timescaledb_information.hypertables
WHERE hypertable_name = 'your_table_name';

Monitor:

  • compression_ratio_pct >90% (typical time-series)
  • compressed_pct_of_total growing as data ages
  • Size growth slowing significantly vs pre-hypertable
  • Decreasing compression_ratio_pct = poor segment_by
Troubleshooting
Poor Chunk Exclusion
sql
-- Verify chunks are being excluded
EXPLAIN (ANALYZE, BUFFERS)
SELECT * FROM your_table_name
WHERE timestamp >= '2024-01-01' AND timestamp < '2024-01-02';
-- Look for "Chunks excluded during startup: X"
Poor Compression
sql
-- Get newest compressed chunk name
SELECT chunk_name FROM timescaledb_information.chunks
WHERE hypertable_name = 'your_table_name'
  AND compressed_total_bytes IS NOT NULL
ORDER BY range_start DESC LIMIT 1;

-- Analyze segment distribution
SELECT segment_by_column, COUNT(*) as rows_per_segment
FROM _timescaledb_internal._hyper_X_Y_chunk  -- Use actual chunk name
GROUP BY 1 ORDER BY 2 DESC;

Look for: <20 rows per segment: Poor segment_by choice (should be >100) => Low compression potential.

Poor insert performance

Check that you don't have too many indexes. Unused indexes hurt insert performance and should be dropped.

sql
SELECT
    schemaname,
    tablename,
    indexname,
    idx_tup_read,
    idx_tup_fetch,
    idx_scan
FROM pg_stat_user_indexes
WHERE tablename LIKE '%your_table_name%'
ORDER BY idx_scan DESC;

Look for: Unused indexes via a low idx_scan value. Drop such indexes (but ask user permission).

Ongoing Monitoring
sql
-- Monitor chunk compression status
CREATE OR REPLACE VIEW hypertable_compression_status AS
SELECT
    h.hypertable_name,
    COUNT(c.chunk_name) as total_chunks,
    COUNT(c.chunk_name) FILTER (WHERE c.compressed_total_bytes IS NOT NULL) as compressed_chunks,
    ROUND(
        COUNT(c.chunk_name) FILTER (WHERE c.compressed_total_bytes IS NOT NULL)::numeric /
        COUNT(c.chunk_name) * 100, 1
    ) as compression_coverage_pct,
    pg_size_pretty(SUM(c.total_bytes)) as total_size,
    pg_size_pretty(SUM(c.compressed_total_bytes)) as compressed_size
FROM timescaledb_information.hypertables h
LEFT JOIN timescaledb_information.chunks c ON h.hypertable_name = c.hypertable_name
GROUP BY h.hypertable_name;

-- Query this view regularly to monitor compression progress
SELECT * FROM hypertable_compression_status
WHERE hypertable_name = 'your_table_name';

Look for:

  • compression_coverage_pct should increase over time as data ages and gets compressed.
  • total_chunks should not grow too quickly (more than 10000 becomes a problem).
  • You should not see unexpected spikes in total_size or compressed_size.

Success Criteria

✅ Migration successful when:

  • All queries return correct results
  • Query performance equal or better
  • Compression >90% for older data
  • Chunk exclusion working for time queries
  • Insert performance acceptable

❌ Investigate if:

  • Query performance >20% worse
  • Compression <80%
  • No chunk exclusion
  • Insert performance degraded
  • Increased error rates

Focus on high-volume, insert-heavy workloads with time-based access patterns for best ROI.

© timescale, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/migrate-postgres-tables-to-hypertables of timescale/pg-aiguide.

Open the folder on GitHubat commit 187be00

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in timescale/pg-aiguide, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Migrate Postgres Tables To Hypertables

What does Migrate Postgres Tables To Hypertables do?

A skill your agent uses to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. Migrate Postgres Tables To Hypertables is an agent skill from timescale/pg-aiguide. Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.

When should I use Migrate Postgres Tables To Hypertables?

Migrate Postgres Tables To Hypertables fits situations like: migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation; user asks to: - Migrate; blue-green migration; in-place conversion.

How do I install Migrate Postgres Tables To Hypertables in Claude Code?

Run `npx skills add timescale/pg-aiguide --skill migrate-postgres-tables-to-hypertables -a claude-code`. Or copy the skill folder (skills/migrate-postgres-tables-to-hypertables in timescale/pg-aiguide) into .claude/skills/migrate-postgres-tables-to-hypertables in your project. Claude Code loads it when a task matches its description.

How do I install Migrate Postgres Tables To Hypertables in Codex?

Run `npx skills add timescale/pg-aiguide --skill migrate-postgres-tables-to-hypertables -a codex`. Or copy the skill folder (skills/migrate-postgres-tables-to-hypertables in timescale/pg-aiguide) into .agents/skills/migrate-postgres-tables-to-hypertables in your project. Codex loads it when a task matches its description.

Can I use Migrate Postgres Tables To Hypertables 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 timescale/pg-aiguide --skill migrate-postgres-tables-to-hypertables -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/migrate-postgres-tables-to-hypertables, .gemini/skills/migrate-postgres-tables-to-hypertables, .github/skills/migrate-postgres-tables-to-hypertables and .opencode/skills/migrate-postgres-tables-to-hypertables in your project.

What does Migrate Postgres Tables To Hypertables need to run?

SKILL.md names no scripts, command-line tools or credentials: Migrate Postgres Tables To Hypertables is instructions for the agent only. Compatibility (from SKILL.md): Requires PostgreSQL 15+ with TimescaleDB.

Does Migrate Postgres Tables To Hypertables 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 Migrate Postgres Tables To Hypertables safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): contains instruction-override wording (e.g. “without asking the user”). Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Migrate Postgres Tables To Hypertables use?

Migrate Postgres Tables To Hypertables is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Migrate Postgres Tables To Hypertables use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Migrate Postgres Tables To Hypertables?

Skills that share tags, products or a category with Migrate Postgres Tables To Hypertables: Release (hypequery/hypequery, 103 stars), Take Doc Screenshots (pgplex/pgconsole, 155 stars), Aspireify (CommunityToolkit/Aspire, 629 stars) and Stash Deployment (cipherstash/stack, 157 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Migrate Postgres Tables To Hypertables?

timescale (a GitHub organization) maintains it in timescale/pg-aiguide, which has 1,862 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on October 7, 2026.

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