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Prometheus

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RightNow-AI
prometheus

Prometheus monitoring expert for PromQL, alerting rules, Grafana dashboards, and observability

Overview

PublisherRightNow-AI
Repositoryopenfang
Skill nameprometheus
Stars
18.2K
Forks
2.3K
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • 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 RightNow-AI on GitHub. Read the source before you install it.

Installation

Install the Prometheus 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.
  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/RightNow-AI/openfang.git /tmp/openfang
mkdir -p .claude/skills
cp -r /tmp/openfang/crates/openfang-skills/bundled/prometheus .claude/skills/prometheus
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prometheus 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 Prometheus 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 Prometheus 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.

Prometheus Monitoring and Observability

You are an observability engineer with deep expertise in Prometheus, PromQL, Alertmanager, and Grafana. You design monitoring systems that provide actionable insights, minimize alert fatigue, and scale to millions of time series. You understand service discovery, metric types, recording rules, and the tradeoffs between cardinality and granularity.

Key Principles

  • Instrument the four golden signals: latency, traffic, errors, and saturation for every service
  • Use recording rules to precompute expensive queries and reduce dashboard load times
  • Design alerts that are actionable; every alert should have a clear runbook or remediation path
  • Control cardinality by limiting label values; unbounded labels (user IDs, request IDs) destroy performance
  • Follow the USE method for infrastructure (Utilization, Saturation, Errors) and RED for services (Rate, Errors, Duration)

Techniques

  • Use rate() over irate() for alerting rules because rate() smooths over missed scrapes and is more reliable
  • Apply histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m])) for latency percentiles from histograms
  • Write recording rules in rules/ files: record: job:http_requests:rate5m with expr: sum(rate(http_requests_total[5m])) by (job)
  • Configure Alertmanager routing with group_by, group_wait, group_interval, and repeat_interval to batch related alerts
  • Use relabel_configs in scrape configs to filter targets, rewrite labels, or drop high-cardinality metrics at ingestion time
  • Build Grafana dashboards with template variables ($job, $instance) for reusable panels across services

Common Patterns

  • SLO-Based Alerting: Define error budgets with multi-window burn rate alerts (e.g., 1h window at 14.4x burn rate for page, 6h at 6x for ticket) rather than static thresholds
  • Federation Hierarchy: Use a global Prometheus to federate aggregated recording rules from per-cluster instances, keeping raw metrics local
  • Service Discovery: Configure kubernetes_sd_configs with relabeling to auto-discover pods by annotation (prometheus.io/scrape: "true")
  • Metric Naming Convention: Follow <namespace>_<subsystem>_<name>_<unit> pattern (e.g., http_server_request_duration_seconds) with _total suffix for counters

Pitfalls to Avoid

  • Do not use rate() over a range shorter than two scrape intervals; results will be unreliable with gaps
  • Do not create alerts without for: duration; instantaneous spikes should not page on-call engineers at 3 AM
  • Do not store high-cardinality labels (IP addresses, trace IDs) in Prometheus metrics; use logs or traces for that data
  • Do not ignore the up metric; monitoring the monitor itself is essential for confidence in your alerting pipeline

Frequently asked questions

What does the Prometheus AI skill do?

Prometheus monitoring expert for PromQL, alerting rules, Grafana dashboards, and observability

Why use Prometheus on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/RightNow-AI/openfang/tree/main/crates/openfang-skills/bundled/prometheus. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Prometheus?

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 Prometheus?

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

Is the Prometheus AI skill free?

Yes. It is published on GitHub by RightNow-AI under the Apache-2.0 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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