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Query Metrics

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openclaw
query-metrics

Runs metrics queries against Axiom MetricsDB via scripts. Discovers available metrics, tags, and tag values. Use when asked to query metrics, explore metric datasets, check metric values, or investigate OTel metrics data.

Overview

Publisheropenclaw
Repositoryclawhub
Skill namequery-metrics
Stars
9.5K
Forks
1.5K
Bundled files
7
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by openclaw on GitHub. Read the source before you install it.

Installation

Install the Query Metrics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/openclaw/clawhub/tree/main/.agents/skills/query-metrics
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/openclaw/clawhub.git /tmp/clawhub
mkdir -p .claude/skills
cp -r /tmp/clawhub/.agents/skills/query-metrics .claude/skills/query-metrics
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Query Metrics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Query Metrics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Query Metrics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Querying Axiom Metrics

All script paths are relative to this skill's folder; invoke as scripts/<name>. The target dataset must be of kind otel:metrics:v1.

Setup, prerequisites, and ~/.axiom.toml configuration: see README.md. Edge-deployment routing is automatic — the scripts read each dataset's edgeDeployment and route to the right regional endpoint without configuration.

Workflow

  1. scripts/datasets <deploy> --kind otel:metrics:v1 — list metrics datasets.
  2. scripts/metrics-spec — required before composing any query. MPL evolves; the spec is the source of truth. Also use it to answer general MPL/metrics questions.
  3. scripts/metrics-info <deploy> <dataset> metrics — list metrics with {type, temporality, unit} metadata. Read this before writing the query (see Choosing a Query Shape).
  4. scripts/metrics-info <deploy> <dataset> tags [<tag> values] — explore filter dimensions.
  5. scripts/metrics-query <deploy> '<MPL>' <start> <end> — execute. Iterate.

If the user names a specific entity (service, host, …), scripts/metrics-info <deploy> <dataset> find-metrics "<value>" finds the metrics carrying it. find-metrics searches tag values, not metric names — don't use it for general discovery.

Choosing a Query Shape

The metrics-info listing returns each metric's {type, temporality, unit}. Read these before composing — never assume a metric is a simple scalar.

FieldValuesDrives
typeGauge, CounterMonotonic, CounterNonMonotonic, HistogramRequired pre-aggregation operators.
temporalityCumulative, Delta, nullWhether counter values are running totals or per-interval deltas. null is normal for Gauges.
unitUCUM string (Cel, kW.h, s, %, [ppm], …) or nullDisplay unit; preserve when reporting results.

Rules per type (consult metrics-spec for exact operator names — they evolve):

  • Gauge — instantaneous value. Align directly with avg/min/max/sum. Don't apply a rate; you'd be averaging meaningless deltas of an instantaneous value.
  • CounterMonotonic + Cumulative — running total (resets aside). The raw values are rarely what you want. Convert to a per-second rate first, then align/aggregate.
  • CounterMonotonic + Delta — already per-interval. Sum/align without a rate step.
  • CounterNonMonotonic — can go up or down (queue depth, balance). Intent is ambiguous: rate, delta, or current value all make sense for different questions. Ask the user before picking one.
  • Histogram — not a scalar. align using avg produces nonsense. Use bucket … using with the histogram functions from metrics-spec; quantiles are float specs to those functions, and temporality selects the variant (Cumulative vs Delta interpolation). Consult metrics-spec for the exact signatures.
  • temporality: null — "not applicable for this instrument type" (the norm for Gauges), not "missing data".

When surfacing numbers, attach the unit (treat null as unitless). If you combine metrics with mismatched units in arithmetic, warn rather than silently producing a meaningless number.

Query Metrics

bash
scripts/metrics-query [-w pixels] [--pixel-per-point n] <deploy> '<MPL>' <start> <end>
ParameterNotes
deployName from ~/.axiom.toml (e.g. prod).
MPLPipeline string. Dataset is parsed from the MPL itself.
start / endRFC3339 (2025-01-01T00:00:00Z) or relative (now-1h, now).
-w / --chart-width <px>Optional. Target chart width in pixels; lets the server resolve $__interval.
--pixel-per-point <n>Optional. Pixels per point (server default 10); with -w sets the bucket count.

Always single-quote the MPL string in the shell. MPL is full of backticks; inside double quotes the shell executes them as command substitution, silently mangling the query (or running whatever the identifier names).

Bound the output before grouping. group by <tag> returns one series per tag value with no cap — on a high-cardinality tag this floods the output. Check cardinality first (describe, or tags <tag> values) and prefer plain group using <agg> while exploring.

Examples:

bash
scripts/metrics-query prod -w 1200 \
  '`my-dataset`:`http.server.duration` | align to $__interval using avg' \
  now-1h now

scripts/metrics-query prod -w 1200 \
  '`my-dataset`:`http.server.duration`
   | where `service.name` == "frontend" and method == "GET"
   | align to $__interval using avg
   | group by status_code using sum' \
  now-1d now

Adaptive resolution ($__interval)

Hardcoding a step (align to 5m) makes charts look wrong at other zoom levels — too sparse zoomed in, too dense zoomed out. Prefer the system parameter $__interval wherever a Duration is expected, and pass the chart width so the server picks the step:

bash
scripts/metrics-query prod -w 1200 \
  '`my-dataset`:`http.server.duration` | align to $__interval using avg' \
  now-7d now

The metrics service computes $__interval from the query's time range and the target chart width, then snaps it up to a nice resolution from the ladder 1s, 5s, 10s, 15s, 30s, 1m, 5m, 10m, 15m, 30m, 1h, 12h, 1d, 1w, 1M, 1Y. It never drops below a metric's stored resolution.

  • No declaration needed — the server auto-registers $__interval; do not add param $__interval: Duration; (the edge forwards the query verbatim and the metrics service injects the parameter).
  • Bucket count ≈ chart-width / pixel-per-point (pixel-per-point default 10). Omit -w and the server targets ~500 buckets.
  • Works anywhere a Duration is valid, e.g. bucket to $__interval using histogram(0.5, 0.95).
  • Set -w to your render width (e.g. the metrics-chart skill's plot width) so one bucket ≈ one pixel column. The value is forwarded under the request body's queryOptions (chart-width, pixel-per-point).

Parameters

MPL can declare parameters (param $svc: string;). Pass values with repeated -p name=value. The script applies the API's param__ prefix; values are forwarded verbatim as MPL literals (string literals include their quotes).

bash
scripts/metrics-query \
  -p svc='"frontend"' \
  -p window='5m' \
  prod \
  'param $svc: string; param $window: Duration;
   `otel-metrics`:`http.server.duration` | where `service.name` == $svc | align to $window using avg' \
  now-1h now

Required parameters must be supplied; optional ones may be omitted. Resulting request body shape:

json
{
  "apl": "param $svc: string; …",
  "startTime": "now-1h",
  "endTime": "now",
  "params": { "param__svc": "\"frontend\"", "param__window": "5m" }
}

Literal syntax per type lives in metrics-spec.

Discovery (metrics-info)

Time range defaults to the last 24h; override with --start / --end. Both accept RFC3339 (offsets allowed) or relative now / now-<N><unit> with <unit> in s m h d w, resolved to RFC3339 UTC client-side. This is narrower than metrics-query, which forwards times to the server unparsed and so also accepts forms like now-1y; in metrics-info anything outside now / now-<N>[smhdw] must already be RFC3339 or the request 400s.

CommandReturns
metrics-info <d> <ds> metricsAll metrics, keyed by name, with {type, temporality, unit}.
metrics-info <d> <ds> metrics --by-typeSame listing grouped by type (client-side reshape).
metrics-info <d> <ds> metrics --type Gauge --type HistogramFiltered listing (repeatable, OR semantics; composes with --by-type).
metrics-info <d> <ds> metrics <metric> infoSingle metric's {type, temporality, unit}. Non-zero exit if absent.
metrics-info <d> <ds> metrics <metric> describeBundle: metadata + all tags + tag values in one call (replaces 1+1+N round trips). Flags: --no-values (tag names only), --values-limit N (cap per-tag values; default 50, 0 = unlimited).
metrics-info <d> <ds> metrics <metric> tagsTags carried by a specific metric.
metrics-info <d> <ds> metrics <metric> tags <tag> valuesTag values for that metric.
metrics-info <d> <ds> metrics <metric> tags <tag> typeProbe whether the tag is int/float/string/bool. Returns {type, present_types}; mixed if multiple types coexist, absent if not present.
metrics-info <d> <ds> tagsAll tags in the dataset.
metrics-info <d> <ds> tags <tag> valuesAll values for a tag (across metrics).
metrics-info <d> <ds> find-metrics "<value>"Metrics that carry the given tag value (not metric name).

Error Handling

HTTP errors return JSON with code and message; some include a detail object:

json
{"code": 400, "message": "MPL syntax error: …"}

Syntax errors (400) include an annotated source pointer listing the valid operators at the failure position — read it, it usually names the fix.

CodeCause
400Invalid query syntax or bad dataset name
401Missing/invalid auth
403No permission
404Dataset not found
429Rate limited — back off and retry; don't tight-loop
500Internal error

Requests time out client-side after 120s (AXIOM_MAX_TIME to override; AXIOM_CONNECT_TIMEOUT for the 10s connect timeout).

On 500, re-run with curl -v to capture the traceparent / x-axiom-trace-id header and report it — the trace ID is what the backend team needs to debug.

Scripts

ScriptUsage
scripts/setupCheck requirements and config.
scripts/datasets <deploy> [--kind <kind>]List datasets with edge deployment.
scripts/metrics-specFetch the MPL query spec.
scripts/metrics-query [-w px] [--pixel-per-point n] <deploy> <mpl> <start> <end>Execute a query; use $__interval + -w for adaptive resolution.
scripts/metrics-info <deploy> <dataset> ...Discover metrics, tags, values.
scripts/axiom-api <deploy> <method> <path> [body]Low-level API calls.
scripts/resolve-url <deploy> <dataset>Resolve to the edge deployment URL.

Run any script without arguments for full usage.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Query Metrics AI skill do?

Runs metrics queries against Axiom MetricsDB via scripts. Discovers available metrics, tags, and tag values. Use when asked to query metrics, explore metric datasets, check metric values, or investigate OTel metrics data.

Why use Query Metrics on TypingMind?

Because you install it once and use it with any model. Query Metrics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Query Metrics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/clawhub/tree/main/.agents/skills/query-metrics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Query Metrics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Query Metrics?

As many as you like. As long as a model supports skills, you can use Query Metrics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Query Metrics AI skill free?

Yes. It is published on GitHub by openclaw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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