Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add librefang/librefang-registry --skill prometheusgit clone --depth 1 https://github.com/librefang/librefang-registryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/librefang/librefang-registry/prometheus)<a href="https://agentmods.dev/skills/librefang/librefang-registry/prometheus"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/prometheus/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/librefang/librefang-registry/prometheus"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/prometheus.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00682 |
| Opus 5.5 | $0.00009 | $0.00273 |
| Sonnet 5.5 | $0.00004 | $0.00136 |
| Haiku 4.5 | $0.00002 | $0.00068 |
Grade A, and why
prometheus scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
94% identical to prometheus — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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()overirate()for alerting rules becauserate()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:rate5mwithexpr: sum(rate(http_requests_total[5m])) by (job) - Configure Alertmanager routing with
group_by,group_wait,group_interval, andrepeat_intervalto batch related alerts - Use
relabel_configsin 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_configswith 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_totalsuffix for counters
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 42 lines · 22 tokens per session scan A 2fa4b7856716
prometheus is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 682 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to prometheus, differing in 3 lines, and is treated as a copy.
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