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Kpi anomaly triage

Skill sisodiabhumca/agent-skills/skills/kpi-anomaly-triage

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Install
npx -y skills add sisodiabhumca/agent-skills --skill kpi-anomaly-triage

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Vendor-neutral skill to analyze KPI time-series data, detect anomalies, and generate a triage summary with likely drivers.

SKILL.md

1.1 KB, 260 tokens by cl100k_base, as published. Nobody here has run it

When to invoke

  • When monitoring weekly/daily KPI dashboards for sudden changes.
  • When preparing an investigation checklist for metric movements.

Inputs needed

  • --input: Path to a CSV file with columns date (YYYY-MM-DD) and value.
  • Optional --window: Rolling window size in days (default 14).
  • Optional --z: Z-score threshold (default 3.0).
  • Optional --out: Path to write a JSON report.

Workflow

  1. Parse dates and values; sort by date.
  2. Compute rolling mean and standard deviation.
  3. Flag points with [ z = \frac{x - \mu}{\sigma} ] above threshold.
  4. Summarize recent anomalies and basic context (last 7 days trend).

Output format

JSON with:

  • anomalies: list of {date, value, z, mean, std}.
  • recent_summary: last value, 7d min/max, direction.

Guardrails

  • If standard deviation is zero, do not divide by zero; skip anomaly scoring.
  • Purely statistical heuristic; not a root cause analysis.

Reference code

  • kpi_anomaly_triage.py

What ships with it: 2 files

3.1 KB alongside SKILL.md, 1 of them executable

Gives 0 of the 12 instructions most debug triage skills give in 260 tokens

Counted across 839 of the 1,149 authors here whose files we hold, read 2026-08-07

  • Investigate root cause before proposing any fixin 102 of 839, across 67 files
  • Read error messages completelyin 89 of 839, across 49 files
  • Create a failing test case before fixingin 84 of 839, across 46 files
  • Reproduce the issue consistentlyin 82 of 839, across 41 files
  • Change one variable at a timein 82 of 839, across 42 files
  • Check recent changesin 74 of 839, across 36 files
  • Write the regression test before fixingin 74 of 839, across 40 files
  • Fix the root cause not the symptomin 60 of 839, across 45 files
  • Implement a single fix at a timein 59 of 839, across 20 files
  • Trace data flow backward to the sourcein 50 of 839, across 20 files
  • Remove all debug instrumentationin 49 of 839, across 13 files
  • Form a single hypothesisin 48 of 839, across 18 files

Said here and by no other author read

  • parse dates and values then sort by date
  • compute rolling mean and standard deviation
  • flag points above the z-score threshold
  • summarize recent anomalies and seven-day trend context
  • skip anomaly scoring if standard deviation is zero

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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