metrics-chart

Turns a metrics query response into a multi-series line chart.

Input is exactly the body the Axiom metrics query service returns for application/vnd.metrics.v3+json — defined in the metrics query service (service/query, JsonV3QueryResponse in src/server.rs). It is a regularly-sampled, multi-series time series; this skill draws it.

TL;DR

# From a file, stdout picks the best renderer for your terminal:
python3 scripts/metrics_chart.py response.json

# From a pipe (e.g. straight off the query API):
curl ... -H 'Accept: application/vnd.metrics.v3+json' | python3 scripts/metrics_chart.py

# Force a zero-dependency ASCII chart (always works, anywhere):
python3 scripts/metrics_chart.py --format ascii response.json

Aggregate first. Charts are only readable with a handful of series. The cleanest result comes from a query that already groups/aggregates to a few lines (e.g. group by status rather than per-pod). This skill's series reduction (below) is a safety net for high-cardinality results, not a substitute for aggregating in the query.

Input format (vnd.metrics.v3+json)

{
  "metadata": {
    "group_keys": ["code", "method"],
    "warnings": [],
    "unit": "ms",
    "custom_unit": "millisec"
  },
  "series": [
    {
      "metric": "http_requests",
      "tags": { "code": 200, "method": "GET", "path": "/" },
      "start": 1750753164,
      "resolution": 60,
      "data": [420.0, 42.0, 30.0, 9.0, 15.0, 18.0]
    }
  ]
}

The parser also accepts v2/v2a (same shape with an extra per-series summary, ignored) and the v0/v1 bare-array form, so you can feed older responses too.

Behaviour

• Labels come from group_keys values joined with | → 200 | GET (not code=200,method=GET). Without group_keys, falls back to k=v tags.
• Local time axis by default; --tz UTC or --tz Europe/Berlin to override.
• Gaps: null points break the line.
• Series reduction (unless --all), in order:
  1. Collapse overlapping series whose lines essentially coincide (within --eps, default 2% of the y-range); the most prominent is kept.
  2. Top-N by peak value (--top, default 8).
  3. The legend prints how many were dropped, e.g. showing 8 of 30 series (5 overlapping, 17 low/hidden), plus the aggregate-your-query tip.

Output formats

--format auto (default) picks the best renderer for the context:

• an inline image when gnuplot is installed and the terminal supports images (Kitty/Ghostty, iTerm2);
• a PNG file plus a "display this file" instruction when output is piped/captured (e.g. an AI agent harness) and gnuplot is installed — the agent then displays the file;
• ASCII in a plain terminal, or whenever gnuplot is unavailable.

Force a specific backend with --format:

If an image format is requested but gnuplot is missing, it prints a note and falls back to ASCII — it never hard-fails.

Using from an AI agent / harness

Terminal harnesses (e.g. pi) collapse tool output and don't repaint inline ANSI, so ASCII charts come out monochrome and folded. Prefer images: render a file and let the agent display it. With --format auto (the default) this is automatic whenever output is piped and gnuplot is installed — the skill writes a PNG and prints:

metrics-chart: wrote image to /tmp/metrics-chart-XXXX.png
→ Display this file to the user with your image tool (e.g. read it); do not paste its contents.

The agent should then display that file (its image/read tool renders it inline, in colour, un-folded). To control the path, pass --output chart.png. Do not paste the PNG bytes or the ASCII into chat.

Options

Dependencies

• Default path: none. Pure Python 3.9+ standard library. The ASCII renderer always works.
• Optional: gnuplot (one system binary) for PNG/SVG/sixel. Install only if you want high-fidelity images.

Why not graphviz?

graphviz (dot/neato) lays out node-edge graphs; it has no numeric/time axis and cannot plot data[i] over time. This payload is a numeric time series with no graph structure, so graphviz does not apply. gnuplot is the correct image backend. (matplotlib is avoided on purpose: it is a heavy multi-package pip install.)

Tests

cd scripts && python3 -m unittest test_metrics_chart -v

The parser tests are pinned to the golden bytes from the metrics query service's service/query/src/server/tests.rs.