IoT · Global library

Time-Series Database Design & Optimization

Design high-performance time-series storage with proper retention, compression, and query optimization

CodexClaude CodeKimi Codeorchestrator-mcp

Best use case

Use Time-Series Database Design & Optimization when you need to design high-performance time-series storage with proper retention, compression, and query optimization, especially when the work is driven by time series and influxdb.

Trigger signals

time seriesinfluxdbtimescaledbretentioncompression

Validation hooks

retention-configuredquery-performance

Install surface

Copy the exact command path you need.

Inspect

pip install "orchestrator-mcp[dashboard]"
orchestrator-mcp skills show time-series-database-design-optimization

Use

orchestrator-mcp skills export time-series-database-design-optimization --to ./skillforge-packs
# copy the exported pack into your preferred agent environment

Export

cp -R skills/time-series-database-design-optimization ./your-agent-skills/time-series-database-design-optimization
# or open skills/time-series-database-design-optimization/SKILL.md in a markdown-first client

File patterns

*influx*.{py,conf}*timescale*.{sql,py}*prometheus*.{yml,py}

Model preferences

claude-sonnet-4gpt-4oclaude-haiku

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Apache-2.0 licensedUse it, fork it, redistribute it. No strings attached.Safety-first by designSecret redaction, release scanning, and no shell execution. Constraints are public.GitHub nativeEvery pack is a real folder. Fork, edit, PR. No hidden backend or proprietary pipeline.

Time-Series Database Design & Optimization

Copy the exact command path you need.

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