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Time-Series Databases · ancoleman | DecimalAI

databy ancoleman

Time-Series Databases

ancoleman/using-timeseries-databases

Source ancoleman/ai-design-components ↗License MIT

Time-series database implementation for metrics, IoT, financial data, and observability backends.

SkillScore91Maintained

Automatic

Data Engineer pack

Last verified 8/21/2026

What it changes

Passed 21 of 23 test cases with the skill, and 12 of 23 without it.

Without skill52%

With skill91%

Checks it passed that the model alone missed

uses time interval relative to NOW()
recommends QuestDB for IoT sensor data
recommends InfluxDB for DevOps monitoring

and 10 more in the full report

9 fixed12 passed either way2 still failing0 broke

Pass-rate lift: +39 pp
Tokens: +54%
Agent turns: 0%
Response time: -16%

Model gemini-3.6-flashMeasured 8/21/2026v121 of 23 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/ancoleman-using-timeseries-databases/SKILL.md https://app.decimal.ai/s/ancoleman-using-timeseries-databases/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add ancoleman-using-timeseries-databases, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/ancoleman-using-timeseries-databases/ and pins it in .decimal/skills.lock

decimalai skills add ancoleman-using-timeseries-databases

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/ancoleman-using-timeseries-databases/SKILL.md https://app.decimal.ai/s/ancoleman-using-timeseries-databases/SKILL.md

Raw file: /s/ancoleman-using-timeseries-databases/SKILL.md ↗ · machine-readable summary at /s/ancoleman-using-timeseries-databases.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 9

Prompt

Our team is setting up an operational metrics backend to feed dynamic dashboards and incident response systems. Please supply a performance optimization guide that outlines write batching parameters to improve ingestion throughput, indexed SQL query templates for KPI cards, trend graphs, and alert thresholds, as well as recommended auto-refresh intervals for operational views.

What was checked, without and with the skill
Checked	Without skill	With skill
specifies KPI card query pattern ordering by time descending with limit 1	Missed	Passed
recommends operational dashboard polling auto-refresh interval of 10 to 30 seconds	Missed	Passed
recommends batch size ranges for ingestion throughput optimization across engines	Passed	Passed

case-02 · gemini-3.6-flash · 8/21/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md225 lines8 filesincludes scripts

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

Track record

Benchmark

21 / 23

test cases passed, vs 12 / 23 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

ancoleman / ai-design-components

skills/using-timeseries-databases/SKILL.md

View on GitHub ↗

License MITImported 8/3/2026Version v1Bundled 7 files · scripts

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Time-Series Databases

Database Selection

Core Patterns

1. Hypertables (TimescaleDB)

Track record

Benchmark

Adoption

SkillSafety

Source

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