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find-hypertable-candidates

Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.

68

Quality

84%

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SKILL.md
Quality
Evals
Security

Quality

Content

68%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 body is highly actionable with executable SQL and a clear scoring rubric, but it is somewhat verbose due to repeated good/poor pattern sections and lacks explicit validation/verification checkpoints for its batch analysis workflow, capping workflow clarity at 3.

Suggestions

Consolidate the repeated good/poor pattern lists (Option B code patterns, Schema Indicators, and Common Patterns) into a single reference section to reduce token overlap and verbosity.

Add an explicit validation/checkpoint step in the workflow, e.g. 'Verify candidate scoring against actual query patterns before finalizing the ranked list,' to raise workflow clarity for this batch assessment task.

Move the detailed example CREATE TABLE statements for each pattern into a separate reference file (e.g., references/patterns.md) and link to it from the body to improve progressive disclosure.

DimensionReasoningScore

Conciseness

The body is mostly efficient with executable SQL and code, but it repeats the same good/poor pattern explanations across multiple sections (Option B, Schema Indicators, Common Patterns) and restates candidacy criteria, adding padding that could be tightened.

3 / 5

Actionability

It provides copy-paste-ready SQL queries for table stats, indexes, pg_stat_statements, and constraints, plus concrete table-creation examples and an explicit 8+ point scoring rubric — fully executable guidance covering common cases.

5 / 5

Workflow Clarity

Steps are sequenced (schema analysis -> candidacy scoring -> output), but this database-analysis skill involves batch/assessment operations with no explicit validation or verification checkpoint confirming results before output; the rubric caps batch/evaluation workflows lacking feedback loops at 3.

3 / 5

Progressive Disclosure

Content is well-organized into clear sections (Benefits, Step 1/2, Patterns, Output) with a signaled one-level reference to the companion migrate skill; minor gaps only — no external reference files exist, but the body is appropriately structured.

4 / 5

Total

15

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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 strong across all dimensions: it states concrete actions, includes a natural trigger list and keyword set, and clearly delineates a distinctive niche. It does not pad with fluff or over-claim.

DimensionReasoningScore

Specificity

Multiple concrete actions are named: 'analyze an existing PostgreSQL database,' 'identify which tables should be converted,' 'analyze table statistics, index patterns, and query patterns,' and 'score or rank tables,' giving comprehensive coverage of capabilities.

5 / 5

Completeness

It clearly answers 'what' (analyze database and identify hypertable conversion candidates, provide SQL/scoring) and 'when' (an explicit '**Trigger when user asks to:**' list with four concrete trigger phrases), satisfying the highest anchor.

5 / 5

Trigger Term Quality

Comprehensive natural keyword coverage including synonyms and variations: 'hypertable candidate,' 'Timescale/TimescaleDB,' 'time-series detection,' 'event logs,' 'audit tables,' 'insert-heavy tables,' and 'migration assessment,' plus an explicit 'Trigger when' list of user phrasings.

5 / 5

Distinctiveness Conflict Risk

It targets a clear niche (PostgreSQL-to-TimescaleDB hypertable candidacy) with distinct triggers that are unlikely to fire for unrelated skills, minimizing conflict risk.

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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