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Magnus HedemarkandGitHub c7c4d3b74f Port 11 methodology skills from hermes-profiles (#69 )

Engineering: backend-engineering, frontend-engineering, data-engineering,
ml-engineering, platform-engineering, qa-methodology

Executive: go-to-market, legal-strategy, operational-design, org-design,
product-strategy

ml-engineering: added missing training-infrastructure.md reference
qa-methodology: added test-data-management, performance-testing,
security-testing references

All frontmatter converted to agent-skills convention.
Source: https://github.com/magnus919/hermes-profiles

2026-07-21 00:58:26 -04:00

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Observability — Reference

Metrics: Prometheus

Core model: Pull-based timeseries — scrape targets, service discovery, metric exposition format (/metrics endpoint)
Metric types: Counter (cumulative, only increases), Gauge (up/down), Histogram (bucketed durations/sizes), Summary (quantile-based)
Recording rules: Compute new timeseries from existing ones — rate(...)[5m] for per-second averages, histogram_quantile() for latency percentiles, aggregation via sum() by () / topk(). Stored as new metric in Prometheus, faster than ad-hoc query
Alerting rules: Vector → alert — for: duration eliminates flapping, severity labels (critical/warning/info), routing via Alertmanager to PagerDuty, Slack, email, etc.
Service discovery: Kubernetes (pod annotations, kubelet), file_sd (JSON/YAML files), Consul, EC2, DNS
Best practices: Use rate() not irate(), prefer histograms over summaries (aggregatable), label hygiene (cardinality limits, structured label naming)

Dashboards: Grafana

Core model: Data source abstraction — panel types (time series, bar, stat, table, gauge, logs), variables (interval, datasource filter), dashboard-as-code via JSON provisioning
Provisional dashboards: JSON files in provisioning/dashboards/ — auto-imported on Grafana startup. YAML datasource config in provisioning/datasources/. Version-controlled in Git alongside application config
Key patterns: Template variables for environment switching, repeat panels per label value, annotations from Prometheus alerts, mixed data sources per panel, transformations (merge, group by, rename)
Best practices: Single dashboard per service, row per concern (traffic, errors, latency, saturation), no more than 15 panels per row, dashboard links for navigation, $__interval for adaptive time range

Logs: Loki

Core model: Label-based log aggregation — indexes labels (not full text), stores compressed chunks in object storage. Promtail/Alloy/Fluent Bit for log shipping
LogQL: {label=~"value"} |= "error" \| json for label matchers + content filters + pipeline stages. rate() for log error rates, count_over_time() for volume monitoring
Best practices: CRI-O/Docker log format handling, structured logging (JSON), label cardinality limits, retention per storage tier (hot/warm/cold), multi-tenancy via label enforcement

Tracing: OpenTelemetry

Core model: Spans (operation units) → Traces (span DAG) → context propagation via W3C TraceContext headers
Signals: Traces (request flow), Metrics (OTLP), Logs (via OTLP or file export) — unified in OpenTelemetry Collector
Components: SDK (instrumentation libraries for Go, Python, JS, Java, etc.), Collector (receiver → processor → exporter pipeline), sampling (head-based, tail-based for storage cost management)
Instrumentation: Auto-instrumentation (agent injection for Java/Python/.NET/Node), manual instrumentation (create spans, add attributes/events), existing library instrumentation (HTTP, gRPC, DB clients, messaging)

3.0 KiB Raw Blame History

Observability — Reference

Metrics: Prometheus

Dashboards: Grafana

Logs: Loki

Tracing: OpenTelemetry

3.0 KiB

Raw Blame History