Axiom Alerting logo

Axiom Alerting

OrganizationPopular
openclaw
axiom-alerting

Create and manage Axiom monitors and notifiers via the v2 public API. Use when building alerting, routing notifications, validating monitor behavior, and maintaining alert configurations end-to-end.

Overview

Publisheropenclaw
Repositoryclawhub
Skill nameaxiom-alerting
Stars
9.5K
Forks
1.5K
Bundled files
13
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.

  • 13 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 Axiom Alerting 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/axiom-alerting
  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/axiom-alerting .claude/skills/axiom-alerting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Axiom Alerting 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 Axiom Alerting 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 Axiom Alerting 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.

Axiom Alerting

You manage alerting in Axiom end-to-end: notifiers for routing and monitors for detection.

API Overview

Base URL: https://api.axiom.co/v2/ with Bearer token auth from .axiom.toml (project root or ~/.axiom.toml).

Monitors (/v2/monitors)

OperationMethodPath
ListGET/v2/monitors
GetGET/v2/monitors/{id}
HistoryGET/v2/monitors/{id}/history
CreatePOST/v2/monitors
UpdatePUT/v2/monitors/{id}
DeleteDELETE/v2/monitors/{id}

Notifiers (/v2/notifiers)

OperationMethodPath
ListGET/v2/notifiers
GetGET/v2/notifiers/{id}
CreatePOST/v2/notifiers
UpdatePUT/v2/notifiers/{id}
DeleteDELETE/v2/notifiers/{id}

Prerequisites

  1. Run scripts/setup
  2. Ensure .axiom.toml has a deployment:
toml
[deployments.prod]
url = "https://api.axiom.co"
token = "xaat-your-token"
org_id = "your-org-id"

Scripts

Core:

  • scripts/axiom-api <deploy> <method> <path> [body]

Monitor scripts:

  • scripts/monitor-list <deployment> [--json]
  • scripts/monitor-get <deployment> <id>
  • scripts/monitor-history <deployment> <id> <startTime> <endTime>
  • scripts/monitor-create <deployment> <json-file>
  • scripts/monitor-update <deployment> <id> <json-file>
  • scripts/monitor-delete <deployment> <id>

Notifier scripts:

  • scripts/notifier-list <deployment> [--json]
  • scripts/notifier-get <deployment> <id>
  • scripts/notifier-create <deployment> <json-file>
  • scripts/notifier-update <deployment> <id> <json-file>
  • scripts/notifier-delete <deployment> <id>

Recommended Workflow

  1. Create notifier first.
  2. Create monitor and set notifierIds.
  3. Validate monitor behavior with monitor-history.
  4. Iterate monitor thresholds and schedule.

Workflow: End-To-End Alerting

  1. Run scripts/setup.
  2. List existing notifiers with scripts/notifier-list <deployment> and reuse one if appropriate.
  3. If no suitable notifier exists, create one with scripts/notifier-create.
  4. Create or update the monitor with notifierIds attached.
  5. Validate with scripts/monitor-history <deployment> <id> <startTime> <endTime>.
  6. If behavior is noisy or silent, tune threshold, rangeMinutes, intervalMinutes, and N-of-M trigger fields.
  7. Re-check history after each change.

Best Practices

  • Configure one channel per notifier.
  • Use emails (not recipients) for email notifier payloads.
  • Prefer triggerAfterNPositiveResults/triggerFromNRuns for noisy signals.
  • Use explicit bin() in monitor queries; avoid bin_auto() for alert logic.
  • For metrics-backed monitors, prefer mplQuery for definitions; API responses may include both aplQuery and mplQuery.

Monitor Types And Operators

Monitor types:

  • Threshold
  • MatchEvent
  • AnomalyDetection

Operators:

  • Above
  • Below
  • AboveOrEqual
  • BelowOrEqual
  • AboveOrBelow

Monitor Field Reference

Core fields:

  • name: Human-readable monitor name.
  • type: Threshold, MatchEvent, or AnomalyDetection.
  • aplQuery / mplQuery: Query evaluated by the monitor.
  • notifierIds: Array of notifier IDs to notify.
  • disabled: Whether monitor is disabled.
  • disabledUntil: Optional timestamp for temporary disable/snooze.
  • description: Optional monitor description.

Threshold and evaluation fields:

  • operator: Threshold comparison operator.
  • threshold: Numeric threshold value.
  • rangeMinutes: Query evaluation window in minutes.
  • intervalMinutes: Evaluation cadence in minutes.
  • alertOnNoData: Whether no-data should trigger alerting.
  • triggerAfterNPositiveResults: Positive evaluations required before firing.
  • triggerFromNRuns: Total evaluation runs considered for N-of-M logic.

Advanced behavior fields:

  • resolvable: Whether alerts can resolve automatically.
  • notifyByGroup: Notify per group key/value result.
  • notifyEveryRun: Notify on every positive evaluation.
  • skipResolved: Skip sending resolved notifications.
  • secondDelay: Delay (seconds) to tolerate late-arriving data.

Type-specific fields:

  • columnName: Field used by some anomaly/value-anomaly monitors.

Minimal Valid Monitor Examples

Threshold:

json
{
  "name": "High Error Count",
  "type": "Threshold",
  "aplQuery": "['logs'] | where status >= 500 | summarize count()",
  "operator": "Above",
  "threshold": 100,
  "rangeMinutes": 5,
  "intervalMinutes": 5,
  "notifierIds": ["notifier-id"],
  "triggerAfterNPositiveResults": 2,
  "triggerFromNRuns": 3,
  "disabled": false
}

MatchEvent:

json
{
  "name": "Error Event Match",
  "type": "MatchEvent",
  "aplQuery": "['logs'] | where level == 'error'",
  "rangeMinutes": 5,
  "intervalMinutes": 5,
  "notifierIds": ["notifier-id"],
  "disabled": false
}

AnomalyDetection:

json
{
  "name": "CPU Anomaly",
  "type": "AnomalyDetection",
  "aplQuery": "['metrics'] | summarize avg(cpu_usage)",
  "columnName": "cpu_usage",
  "operator": "AboveOrBelow",
  "rangeMinutes": 5,
  "intervalMinutes": 5,
  "notifierIds": ["notifier-id"],
  "disabled": false
}

Minimal Valid Notifier Examples

Email:

json
{
  "name": "Oncall Email",
  "properties": {
    "email": {
      "emails": ["oncall@example.com"]
    }
  }
}

Slack:

json
{
  "name": "Oncall Slack",
  "properties": {
    "slack": {
      "slackUrl": "https://hooks.slack.com/services/T.../B.../XXX"
    }
  }
}

Custom webhook:

json
{
  "name": "Oncall Custom Webhook",
  "properties": {
    "customWebhook": {
      "url": "https://api.example.com/alerts",
      "body": "{\"action\":\"{{.Action}}\",\"monitorID\":\"{{.MonitorID}}\"}"
    }
  }
}

Troubleshooting

401 Unauthorized:

  • Cause: invalid or expired token.
  • Fix:
    • Verify token in ~/.axiom.toml.
    • Re-run scripts/setup and retry:
      • scripts/notifier-list <deployment>

403 Forbidden:

  • Cause: token lacks required permissions.
  • Fix:
    • Create/assign token scopes for monitor/notifier management and dataset query access.
    • Retry:
      • scripts/monitor-list <deployment>

404 Not Found on get/update/delete:

  • Cause: wrong monitor/notifier ID or wrong deployment/org.
  • Fix:
    • Confirm deployment in .axiom.toml.
    • Re-list objects and use exact IDs:
      • scripts/monitor-list <deployment> --json
      • scripts/notifier-list <deployment> --json

400 Bad Request on notifier create/update:

  • Cause: invalid notifier payload shape.
  • Fix:
    • Use one notifier channel inside properties.
    • For email, use emails (not recipients).
    • Validate against a known-good example and retry:
      • scripts/notifier-create <deployment> <json-file>

400 Bad Request on monitor create/update:

  • Cause: invalid monitor schema, operator/type mismatch, or invalid query fields.
  • Fix:
    • Validate required fields: name, type, query field, schedule, and notifierIds.
    • Confirm operator matches monitor type and threshold logic.
    • Retry:
      • scripts/monitor-create <deployment> <json-file>
      • scripts/monitor-update <deployment> <id> <json-file>

Monitor created but never alerts:

  • Cause: threshold too strict, wrong query window, or not enough positive runs.
  • Fix:
    • Inspect history over a known active period:
      • scripts/monitor-history <deployment> <id> <startTime> <endTime>
    • Reduce threshold or widen rangeMinutes.
    • Tune triggerAfterNPositiveResults/triggerFromNRuns.

Too many alerts (noisy monitor):

  • Cause: threshold too low or interval too short.
  • Fix:
    • Increase threshold.
    • Increase triggerAfterNPositiveResults and/or triggerFromNRuns.
    • Increase intervalMinutes or narrow match conditions.

Notifier exists but no delivery:

  • Cause: destination config invalid (URL/key/channel/email list), or destination-side rejection.
  • Fix:
    • Fetch notifier and verify destination fields:
      • scripts/notifier-get <deployment> <id>
    • Recreate/update notifier with corrected properties:
      • scripts/notifier-update <deployment> <id> <json-file>
    • Confirm monitor references correct notifier IDs.

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 Axiom Alerting AI skill do?

Create and manage Axiom monitors and notifiers via the v2 public API. Use when building alerting, routing notifications, validating monitor behavior, and maintaining alert configurations end-to-end.

Why use Axiom Alerting on TypingMind?

Because you install it once and use it with any model. Axiom Alerting 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 Axiom Alerting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/clawhub/tree/main/.agents/skills/axiom-alerting. 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 Axiom Alerting?

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 Axiom Alerting?

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

Is the Axiom Alerting 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇