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 G1Joshi/Agent-Skills --skill timescaledbgit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote 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/g1joshi/agent-skills/timescaledb)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/timescaledb"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/timescaledb/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/g1joshi/agent-skills/timescaledb"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/timescaledb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.01152 |
| Opus 5.5 | $0.00017 | $0.00461 |
| Sonnet 5.5 | $0.00009 | $0.00230 |
| Haiku 4.5 | $0.00004 | $0.00115 |
Grade A, and why
timescaledb 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 23d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TimescaleDB
TimescaleDB is a time-series database built as an extension on top of PostgreSQL. It gives you the scale of NoSQL time-series with the reliability and tooling of Postgres.
When to Use
- High-Velocity Time-Series on PostgreSQL: Ingesting and querying millions of metrics, IoT sensor data, and financial ticks within PostgreSQL.
- Continuous Aggregations & Materialized Views: Automatically computing real-time 1-minute, hourly, or daily rollups as data arrives.
- Native Columnar Compression: Reducing time-series storage footprints by 90%+ using chunk-level columnar compression.
- Data Retention & Automated Tiering: Automatically dropping or migrating historical time-series data to cold object storage.
Quick Start
-- Convert standard table to hypertable
SELECT create_hypertable('conditions', 'time');
-- Query using standard SQL time-bucket functions
SELECT time_bucket('15 minutes', time) AS bucket,
avg(temperature)
FROM conditions
GROUP BY bucket
ORDER BY bucket DESC;
Core Concepts
Hypertables & Automated Time Chunking
A hypertable exposes a unified SQL table interface while partitioning data into time-based chunks automatically in the background:
CREATE EXTENSION IF NOT EXISTS timescaledb;
CREATE TABLE stock_ticks (
time TIMESTAMPTZ NOT NULL,
symbol VARCHAR(10) NOT NULL,
price DOUBLE PRECISION NOT NULL,
volume INT NOT NULL
);
-- Convert to Hypertable with 1-day chunk intervals
SELECT create_hypertable('stock_ticks', 'time', chunk_time_interval => INTERVAL '1 day');
Continuous Aggregates (Real-Time Rollups)
Maintains incrementally updated aggregate views that combine historical materializations with raw live incoming data:
CREATE MATERIALIZED VIEW stock_candlestick_1h
WITH (timescaledb.continuous) AS
SELECT
time_bucket('1 hour', time) AS bucket,
symbol,
first(price, time) AS open_price,
max(price) AS high_price,
min(price) AS low_price,
last(price, time) AS close_price,
sum(volume) AS total_volume
FROM stock_ticks
GROUP BY bucket, symbol;
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.
- 23d ago Changed ad6ce6b71ced
- 27d ago Changed · +80 lines · +22 tokens per session 98794c9e6b9f
- 1mo ago First seen · 59 lines · 21 tokens per session scan A 95c39f91b941
timescaledb is a skill published in the GitHub repository G1Joshi/Agent-Skills (15 stars, last pushed 24d ago), licensed MIT. It adds 43 tokens to every session and 1,152 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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