find-hypertable-candidates

find-hypertable-candidates is a skill for Claude Code, Codex from timescale/pg-aiguide. It costs 184 tokens per session (2,461 once invoked), scanned A, original, Apache-2.0.

A database review guide for finding PostgreSQL tables that may suit TimescaleDB hypertables, a table format designed for large time-ordered data. It focuses on tables such as metrics, logs, events, and transactions.

In plain words
What is it for?
Use it to inspect an existing PostgreSQL schema and identify likely hypertable candidates. A separate migration guide is needed to convert the selected tables.
Why use it?
It helps identify tables that may benefit from time-based storage and querying before any database migration is attempted. The analysis considers data volume, insert and update patterns, and whether queries use time.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to inspect an existing PostgreSQL schema and identify likely hypertable candidates. A separate migration guide is needed to convert the selected tables.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/timescale/pg-aiguide/find-hypertable-candidates
About the project

pg-aiguide is a knowledge and tooling project that gives AI coding assistants version-aware PostgreSQL documentation and curated database practices. Developers use it through agent skills, an MCP server, or a Claude Code plugin to help coding tools generate better PostgreSQL code. The catalogue entries are its skills, instructions, MCP integration, and rule.

timescale/pg-aiguide · 1,835 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add timescale/pg-aiguide --skill find-hypertable-candidates
Clone the repo
git clone --depth 1 https://github.com/timescale/pg-aiguide

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for find-hypertable-candidates

README.md
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Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

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Your own site · 80×15
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Per session 184 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,461 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00184 $0.02461
Opus 5 $0.00092 $0.01230
Sonnet 5 $0.00037 $0.00492
Haiku 4.5 $0.00018 $0.00246

Measured 9d ago against content hash 12472d435c00, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

find-hypertable-candidates scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/find-hypertable-candidates/SKILL.md · 323 lines

How it starts

The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PostgreSQL Hypertable Candidate Analysis

Identify tables that would benefit from TimescaleDB hypertable conversion. After identification, use the companion "migrate-postgres-tables-to-hypertables" skill for configuration and migration.

TimescaleDB Benefits

Performance gains: 90%+ compression, fast time-based queries, improved insert performance, efficient aggregations, continuous aggregates for materialization (dashboards, reports, analytics), automatic data management (retention, compression).

Best for insert-heavy patterns:

  • Time-series data (sensors, metrics, monitoring)
  • Event logs (user events, audit trails, application logs)
  • Transaction records (orders, payments, financial)
  • Sequential data (auto-incrementing IDs with timestamps)
  • Append-only datasets (immutable records, historical)

Requirements: Large volumes (1M+ rows), time-based queries, infrequent updates

Step 1: Database Schema Analysis

Option A: From Database Connection

Table statistics and size
-- Get all tables with row counts and insert/update patterns
WITH table_stats AS (
    SELECT
        schemaname, tablename,
        n_tup_ins as total_inserts,
        n_tup_upd as total_updates,
        n_tup_del as total_deletes,
        n_live_tup as live_rows,
        n_dead_tup as dead_rows
    FROM pg_stat_user_tables
),
table_sizes AS (
    SELECT
        schemaname, tablename,
        pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) as total_size,
        pg_total_relation_size(schemaname||'.'||tablename) as total_size_bytes
    FROM pg_tables
    WHERE schemaname NOT IN ('information_schema', 'pg_catalog')
)
SELECT
    ts.schemaname, ts.tablename, ts.live_rows,
    tsize.total_size, tsize.total_size_bytes,
    ts.total_inserts, ts.total_updates, ts.total_deletes,
    ROUND(CASE WHEN ts.live_rows > 0
          THEN (ts.total_inserts::float / ts.live_rows) * 100
          ELSE 0 END, 2) as insert_ratio_pct
FROM table_stats ts
JOIN table_sizes tsize ON ts.schemaname = tsize.schemaname AND ts.tablename = tsize.tablename
ORDER BY tsize.total_size_bytes DESC;

Read the full file on GitHub · 323 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 323 lines · 184 tokens per session scan A 12472d435c00

Subscribe to this mod's changes

find-hypertable-candidates is a skill published in the GitHub repository timescale/pg-aiguide (1,835 stars, last pushed 4d ago), licensed Apache-2.0. It adds 184 tokens to every session and 2,461 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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