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 ancoleman/ai-design-components --skill using-timeseries-databasesgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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/ancoleman/ai-design-components/using-timeseries-databases)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/using-timeseries-databases"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/using-timeseries-databases/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/ancoleman/ai-design-components/using-timeseries-databases"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/using-timeseries-databases.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 | $0.00075 | $0.01754 |
| Opus 5.5 | $0.00030 | $0.00702 |
| Sonnet 5.5 | $0.00015 | $0.00351 |
| Haiku 4.5 | $0.00007 | $0.00175 |
Grade A, and why
using-timeseries-databases 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 1mo 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Time-Series Databases
Implement efficient storage and querying for time-stamped data (metrics, IoT sensors, financial ticks, logs).
Database Selection
Choose based on primary use case:
TimescaleDB - PostgreSQL extension
- Use when: Already on PostgreSQL, need SQL + JOINs, hybrid workloads
- Query: Standard SQL
- Scale: 100K-1M inserts/sec
InfluxDB - Purpose-built TSDB
- Use when: DevOps metrics, Prometheus integration, Telegraf ecosystem
- Query: InfluxQL or Flux
- Scale: 500K-1M points/sec
ClickHouse - Columnar analytics
- Use when: Fastest aggregations needed, analytics dashboards, log analysis
- Query: SQL
- Scale: 1M-10M inserts/sec, 100M-1B rows/sec queries
QuestDB - High-throughput IoT
- Use when: Highest write performance needed, financial tick data
- Query: SQL + Line Protocol
- Scale: 4M+ inserts/sec
Core Patterns
1. Hypertables (TimescaleDB)
Automatic time-based partitioning:
CREATE TABLE sensor_data (
time TIMESTAMPTZ NOT NULL,
sensor_id INTEGER NOT NULL,
temperature DOUBLE PRECISION,
humidity DOUBLE PRECISION
);
SELECT create_hypertable('sensor_data', 'time');
Benefits:
- Efficient data expiration (drop old chunks)
- Parallel query execution
- Compression on older chunks (10-20x savings)
2. Continuous Aggregates
Pre-computed rollups for fast dashboard queries:
-- TimescaleDB: hourly rollup
CREATE MATERIALIZED VIEW sensor_data_hourly
WITH (timescaledb.continuous) AS
SELECT time_bucket('1 hour', time) AS hour,
sensor_id,
AVG(temperature) AS avg_temp,
MAX(temperature) AS max_temp,
MIN(temperature) AS min_temp
FROM sensor_data
GROUP BY hour, sensor_id;
-- Auto-refresh policy
SELECT add_continuous_aggregate_policy('sensor_data_hourly',
start_offset => INTERVAL '3 hours',
end_offset => INTERVAL '1 hour',
schedule_interval => INTERVAL '1 hour');
Query strategy:
- Short range (last hour): Raw data
- Medium range (last day): 1-minute rollups
- Long range (last month): 1-hour rollups
- Very long (last year): Daily rollups
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/iot-data-pipeline/main.go 6.3 KB
- examples/iot-data-pipeline/README.md 9.2 KB
- examples/metrics-dashboard-backend/api.py 7.8 KB runs code
- examples/metrics-dashboard-backend/README.md 8.8 KB
- examples/metrics-dashboard-backend/schema.sql 3.8 KB
- outputs.yaml 14 KB
- README.md 10 KB
- references/clickhouse.md 18 KB
- references/downsampling-strategies.md 17 KB
- references/influxdb.md 14 KB
- references/questdb.md 12 KB
- references/timescaledb.md 12 KB
- scripts/generate_retention_policy.py 8.7 KB runs code
- scripts/setup_hypertable.py 11 KB runs code
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.
- 1mo ago First seen · 231 lines · 75 tokens per session scan A b859dd8a33b0
using-timeseries-databases is a skill published in the GitHub repository ancoleman/ai-design-components (525 stars, last pushed 10mo ago), licensed MIT. It adds 75 tokens to every session and 1,754 once invoked, about $0.0003 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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