Ops Monitor logo

Ops Monitor

Organization
Lifecycle-Innovations-Limited
ops-monitor

OPS on-demand: This skill should be used when the user asks to "datadog", "APM alerts", or…

Overview

PublisherLifecycle-Innovations-Limited
Repositoryclaude-ops
Skill nameops-monitor
Stars
543
Forks
64
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Lifecycle-Innovations-Limited on GitHub. Read the source before you install it.

Installation

Install the Ops Monitor 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/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-monitor
  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/Lifecycle-Innovations-Limited/claude-ops.git /tmp/claude-ops
mkdir -p .claude/skills
cp -r /tmp/claude-ops/claude-ops/skills/ops-monitor .claude/skills/ops-monitor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ops Monitor 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 Ops Monitor 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 Ops Monitor 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.

Runtime Context

bash
PREFS="${CLAUDE_PLUGIN_DATA_DIR:-$HOME/.claude/plugins/data/ops-ops-marketplace}/preferences.json"
DD_API_KEY=$(jq -r '.datadog_api_key // empty' "$PREFS" 2>/dev/null)
NR_API_KEY=$(jq -r '.newrelic_api_key // empty' "$PREFS" 2>/dev/null)
OTEL_ENDPOINT=$(jq -r '.otel_endpoint // empty' "$PREFS" 2>/dev/null)

Determine $ARGUMENTS mode:

  • Contains --setup → run Setup flow
  • Contains --watch → run Watch mode
  • Otherwise → run Default health check

OPS ► MONITOR

Load ops-rules before acting. Public repo (no personal data). Outbound: one draft → one approval → one send. If AskUserQuestion / Workflow are missing, follow Rule 10 in ops-rules (Hermes: numbered options / two-turn Telegram card; delegate_task).

Setup flow (--setup)

Ask which backends to configure:

Which monitoring backends would you like to configure?
[Datadog]  [New Relic]  [OpenTelemetry]  [All three]

For each selected backend, collect credentials via AskUserQuestion free-text input (one at a time, ≤4 options per call):

Datadog:

  1. datadog_api_key — API Key from app.datadoghq.com/organization-settings/api-keys
  2. datadog_app_key — Application Key from app.datadoghq.com/organization-settings/application-keys

New Relic:

  1. newrelic_api_key — User API Key from one.newrelic.com/api-keys
  2. newrelic_account_id — Numeric Account ID from New Relic admin portal

OpenTelemetry:

  1. otel_endpoint — Base URL of your OTEL-compatible backend (e.g., https://otlp.grafana.net)

Write each credential to preferences.json using atomic tmpfile swap:

bash
tmp=$(mktemp)
jq --arg k "$KEY" --arg v "$VALUE" '.[$k] = $v' "$PREFS" > "$tmp" && mv "$tmp" "$PREFS"

Run smoke test after saving:

  • Datadog: curl -sf -H "DD-API-KEY: $DD_API_KEY" -H "DD-APPLICATION-KEY: $DD_APP_KEY" "https://api.datadoghq.com/api/v1/validate" → expect {"valid": true}
  • New Relic: curl -sf -H "Api-Key: $NR_API_KEY" "https://api.newrelic.com/graphql" -d '{"query":"{ actor { user { name } } }"}' → expect data.actor.user
  • OTEL: curl -sf "$OTEL_ENDPOINT/healthz" → expect HTTP 200

Report ✅ or ❌ with status for each backend.

Agent Teams support

If CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 is set, use Agent Teams when querying multiple backends simultaneously. This enables:

  • Backend probes run in parallel with shared context (e.g., Datadog agent detects latency spike → OTEL agent can correlate with traces)
  • You can steer: "focus on Datadog alerts first, then cross-reference with New Relic"
  • Real-time progress: agents report per-backend as results arrive

Team setup (only when flag is enabled, multiple backends configured):

TeamCreate("monitor-probes")
Agent(team_name="monitor-probes", name="datadog-probe", subagent_type="ops:monitor-agent", ...)
Agent(team_name="monitor-probes", name="newrelic-probe", subagent_type="ops:monitor-agent", ...)
Agent(team_name="monitor-probes", name="otel-probe", subagent_type="ops:monitor-agent", ...)

If the flag is NOT set or only one backend is configured, use a single monitor-agent subagent.

Default health check (no flags)

Spawn monitor-agent via the Agent tool. Display the result as a formatted dashboard:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 OPS ► MONITOR                       [<timestamp>]
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 DATADOG      ✅ healthy (0 alerts)
 NEW RELIC    🔴 2 critical entities
 OTEL         ✅ healthy
──────────────────────────────────────────────────────
 Total alerts: 2   Severity: CRITICAL

Status icons:

  • ✅ — healthy (0 alerts / configured and reachable)
  • ⚠️ — warning (warn-level alerts present)
  • 🔴 — critical (critical alerts or unreachable)
  • ⬜ — not configured

For each alert or critical entity, display: service name, alert name, and link to the relevant dashboard.

If no backends are configured, show a setup prompt:

No monitoring backends configured. Run /ops:monitor --setup to add Datadog, New Relic, or OTEL.

Watch mode (--watch)

Poll every 60 seconds. On each tick:

bash
while true; do
  RESULT=$(# spawn monitor-agent and capture JSON output)
  # Diff against previous tick
  # Print: timestamp, changed items only
  # 🆕 new alert: <name>
  # ✅ resolved: <name>
  sleep 60
done

Exit on Ctrl-C.

--backend filter

If --backend datadog|newrelic|otel is specified, query and display only that backend.

CLI/API Reference

BackendAuth headerBase URLHealth endpoint
DatadogDD-API-KEY: $key + DD-APPLICATION-KEY: $app_keyhttps://api.datadoghq.com/api/v1/validate
New RelicApi-Key: $keyhttps://api.newrelic.com/graphqlPOST GraphQL query
OTELvaries by backend$OTEL_ENDPOINT/healthz
bash
# Datadog — active alerts
curl -sf \
  -H "DD-API-KEY: ${DD_API_KEY}" \
  -H "DD-APPLICATION-KEY: ${DD_APP_KEY}" \
  "https://api.datadoghq.com/api/v1/monitor?monitor_tags=*&with_downtimes=false" \
  | jq '[.[] | select(.overall_state == "Alert" or .overall_state == "Warn")]'

# New Relic — critical entities (GraphQL)
curl -sf \
  -H "Api-Key: ${NR_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"query":"{ actor { entitySearch(queryBuilder: {alertSeverity: CRITICAL}) { results { entities { name alertSeverity entityType } } } } }"}' \
  "https://api.newrelic.com/graphql"

# OTEL — health check
curl -sf "${OTEL_ENDPOINT}/healthz"

Frequently asked questions

What does the Ops Monitor AI skill do?

OPS on-demand: This skill should be used when the user asks to "datadog", "APM alerts", or…

Why use Ops Monitor on TypingMind?

Because you install it once and use it with any model. Ops Monitor 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 Ops Monitor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Lifecycle-Innovations-Limited/claude-ops/tree/main/claude-ops/skills/ops-monitor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ops Monitor?

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 Ops Monitor?

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

Is the Ops Monitor AI skill free?

Yes. It is published on GitHub by Lifecycle-Innovations-Limited 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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