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/time-series-analysis

@fcb0454

Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models for planning.

Use this Skill: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/time-series-analysis

This session only. Nothing lands on disk.

assetsts_report_template.md

≈570 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Time Series Analysis Report

Metric: [metric name] Period: [start date] to [end date] Frequency: daily / weekly / monthly Analyst: [name] Date: [YYYY-MM-DD]


Summary

Trend: [upward / downward / flat] — approximately [+/-]% per [period] Overall change: [first period value] → [last period value] ([+/-]% total) Notable anomalies: [n] detected ([list dates]) Seasonal pattern: [identified / not present / unknown]


Statistical summary

Statistic Value
Periods [n]
Mean [value]
Median [value]
Min [value] ([date])
Max [value] ([date])
Std deviation [value]
Coefficient of variation [%]

Trend analysis

Direction: [upward / downward / flat] Slope: approximately [+/-]% per [period] Consistency: [consistent / volatile / inflected at [date]]

Interpretation: [2–3 sentences on what the trend implies for the business]


Seasonality

Weekly pattern: [e.g., "Consistently lower on weekends — avg 30% below weekday level"] Monthly pattern: [e.g., "End-of-month spike in last 3 days — billing cycle effect"] YoY comparison: [e.g., "Q4 consistently 25% above Q3 — holiday seasonality confirmed"]


Anomalies

Date Value Direction Z-score Likely explanation
[date] [value] Spike / Dip [z] [explanation or "unknown"]

Period-over-period growth rates

Period Value WoW MoM YoY
[period] [value] [%] [%] [%]
[period] [value] [%] [%] [%]

Forecast (if applicable)

Method: [Seasonal naive / Rolling average / [model name]] Horizon: [n] [periods]

Period Forecast Lower bound Upper bound
[period] [value] [value] [value]

Key assumption: [e.g., "Assumes current trend continues with no major product changes"]


Recommendations

  1. [Action — e.g., "Investigate the dip on [date] — aligns with a deployment; confirm if causal"]
  2. [Action — e.g., "Set an alert for WoW change > 10% to catch future anomalies early"]
  3. [Action — e.g., "Repeat analysis in [n] periods to confirm trend direction"]

Template: ts_report_template.md

Source: SKILL.md on GitHub

No alerts16d4 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    The skill is designed for time series analysis and appears safe for general use, using only standard libraries for statistical calculations. A minor security consideration is identified regarding indirect prompt injection, as the skill processes external CSV data and incorporates non-numeric fields into generated reports without sanitization.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at fcb0454. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 6 days ago.

Activeupdated 5 months ago

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