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.
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.
npx skills add timescale/pg-aiguide --skill find-hypertable-candidatesgit clone --depth 1 https://github.com/timescale/pg-aiguideWrote 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.
[](https://agentmods.dev/skills/timescale/pg-aiguide/find-hypertable-candidates)<a href="https://agentmods.dev/skills/timescale/pg-aiguide/find-hypertable-candidates"><img src="https://agentmods.dev/badge/skills/timescale/pg-aiguide/find-hypertable-candidates/github.svg" alt="Measured on agentmods" height="20"></a>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.
<a href="https://agentmods.dev/skills/timescale/pg-aiguide/find-hypertable-candidates"><img src="https://agentmods.dev/badge/skills/timescale/pg-aiguide/find-hypertable-candidates.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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;
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.
- 9d ago First seen · 323 lines · 184 tokens per session scan A 12472d435c00
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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