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setup-timescaledb-hypertables

Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.

72

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is highly actionable with comprehensive executable SQL and a clear step sequence, but it is a monolithic single file that inlines material better suited to separate reference files and carries some general-knowledge padding.

Suggestions

Split the inlined API Reference (deprecated→new parameter and function mappings) and Schema Best Practices Do's/Don'ts into separate reference files (e.g. references/api-migration.md, references/schema-best-practices.md) and link to them from SKILL.md to improve progressive disclosure.

Consolidate the repeated row-density guidance (currently restated in the segment_by, order_by, and sparse_index sections) into a single location to reduce token redundancy.

Turn Step 11 into an explicit validate-and-retry feedback loop (e.g. if verification shows unexpected uncompressed chunks or missing policies, fix the policy and re-run the verification queries) to strengthen the workflow's validation checkpoint.

DimensionReasoningScore

Conciseness

Mostly efficient and dense with actionable SQL, but includes some padding Claude already knows — the generic Schema Best Practices Do's/Don'ts (e.g. 'Use TIMESTAMPTZ NOT timestamp', 'Use NOT EXISTS NOT NOT IN') — and repeats the row-density guidance across three sections.

4 / 5

Actionability

Fully executable, copy-paste-ready SQL throughout — CREATE TABLE with real tsdb options, add_retention_policy, continuous aggregates, refresh policies, compression, and verification queries covering the common cases.

5 / 5

Workflow Clarity

Steps 1–11 are clearly sequenced with 'Good test' checkpoints, user-confirmation guardrails for retention, and a Step 11 verification block, but Step 11 is a check rather than an explicit validate→fix→retry feedback loop.

4 / 5

Progressive Disclosure

No bundle files exist and the ~470-line SKILL.md inlines content that could live in separate references (API/deprecated mappings, Schema Best Practices, detailed column-selection guides); internal section headers provide structure but there is no one-level-deep reference navigation.

3 / 5

Total

16

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is comprehensive, specific, and well-structured with explicit trigger guidance and strong keyword coverage. It clearly communicates both capability and activation conditions with minimal conflict risk.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'creating database schemas or tables', 'Set up hypertables, compression, retention policies, or continuous aggregates', 'Configure partition columns, segment_by, order_by, or chunk intervals' — with comprehensive coverage.

5 / 5

Completeness

Explicitly answers both 'what' (hypertable creation, column selection, compression, retention, continuous aggregates, indexes) and 'when' via a 'Trigger when user asks to' list with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive keyword coverage including synonyms (Timescale, TimescaleDB, TigerData, Tiger Cloud) and natural user terms (hypertable, time-series, IoT, metrics, sensor data) plus a dedicated Keywords line.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (Timescale/TimescaleDB/TigerData hypertables for time-series data) with product-specific triggers, giving minimal overlap risk with other skills.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
timescale/pg-aiguide
Reviewed

Table of Contents

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