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Evan 1d32be994c chore(skills): add version/author/tags frontmatter to all 60 skills (#86 )

The `librefang` dashboard's federated catalog UI surfaces every
optional SKILL.md frontmatter field — version, author, and tags — but
the existing skills only carry `name` + `description`, so the catalog
cards render visually empty:

  ┌────────────────┐
  │ ansible        │  ← no version, no author, no tags shown
  │ FangHub        │
  │ Ansible auto…  │
  └────────────────┘

Populate the three optional fields across every skill so the catalog
fills out as designed:

  ┌─────────────────────┐
  │ ansible             │
  │ skill · librefang   │
  │ · v0.1.0            │
  │ Ansible auto…       │
  │ [devops][automation]│
  │ [infra]             │
  └─────────────────────┘

Choices
- author = `librefang`. Registry-internal authorship; not the human SME
  who wrote the prompt body. Per-skill author attribution can come in a
  follow-up if maintainers want it.
- version = `0.1.0` baseline. Future content updates bump per-skill.
- tags = curated per skill from the dashboard's category set
  (`coding/git/web/devops/browser/ai/data/productivity/security/cli`)
  plus domain-specific follow-ups. First tag is the primary category.

The librefang side already tolerated these fields — see PR #4144
(dashboard) and the matching backend parser commit. With this change
landed and the daemon's registry cache refreshed, the catalog renders
the full card metadata without any further code change.

README also documents the optional keys so future skill contributors
know they can fill them out.

2026-04-30 20:03:51 +09:00

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name, description, version, author, tags

name

description

version

author

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

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