ralph-init
Initialize a PRD (Product Requirements Document) for structured ralph-loop execution
Initialize a PRD (Product Requirements Document) for structured ralph-loop execution
Configuring diagram appearance and layout. Set direction (TB/BT/LR/RL), theme (pastel/neutral/blues/greens/orange), curve style (ortho/curved/spline/polyline). Apply custom Graphviz attributes via graphAttr, nodeAttr, edgeAttr. Enable autolabel for automatic node type prefixes.
Prometheus monitoring — scrape configuration, service discovery, recording rules, alert rules, and production deployment for infrastructure and application metrics.
Configure and troubleshoot sensor data ingest into weather-station-dashboard via MQTT or HTTP POST. Use when asked to set up MQTT broker connection, debug why readings are not appearing, configure a new sensor sending data over HTTP, check MQTT topic configuration, test sensor connectivity, or understand the ingest pipeline. Triggers include "MQTT not receiving", "sensor not sending", "configure MQTT", "HTTP POST reading", "ingest setup", "topic configuration", "reading not showing up", or any task involving getting sensor data into the system.
Interactively onboard a project to OpenSpec by running a structured interview and generating a complete QRSPI-configured openspec/config.yaml. Use this skill whenever a user mentions "openspec config", "config.yaml for openspec", "set up openspec", "onboard to openspec", "generate openspec config", "QRSPI config", or asks how to configure OpenSpec for their project — even if they just say "help me set up openspec" or "I want to use openspec". Always prefer this skill over ad-hoc config generation.
Production incident response procedures for Python/React applications. Use when responding to production outages, investigating error spikes, diagnosing performance degradation, or conducting post-mortems. Covers severity classification (SEV1-SEV4), incident commander role, communication templates, diagnostic commands for FastAPI/ PostgreSQL/Redis, rollback procedures, and blameless post-mortem process. Does NOT cover monitoring setup (use monitoring-setup) or deployment procedures (use deployment-pipeline).
Application monitoring and observability setup for Python/React projects. Use when configuring logging, metrics collection, health checks, alerting rules, or dashboard creation. Covers structured logging with structlog, Prometheus metrics for FastAPI, health check endpoints, alert threshold design, Grafana dashboard patterns, error tracking with Sentry, and uptime monitoring. Does NOT cover incident response procedures (use incident-response) or deployment (use deployment-pipeline).
Prometheus Metrics Query & Alert Interpreter — query metrics, interpret timeseries, triage alerts
Creates tracing utility infrastructure for a target project/language. Generates decorator templates, log writers, and configuration. Use when setting up tracing for a new project or adding tracing support to existing project. Triggers on "create tracing utility", "set up tracing for", "add tracing infrastructure".
Structured logging, distributed tracing, and metrics collection patterns for building observable systems. Use when implementing logging infrastructure, setting up distributed tracing with OpenTelemetry, designing metrics collection (RED/USE methods), configuring alerting and dashboards, or reviewing observability practices. Covers structured JSON logging, context propagation, trace sampling, Prometheus/Grafana stack, alert design, and PII/secret scrubbing.
BullMQ queue configuration patterns including connection pooling, job options, rate limiting, and TypeScript types. Use when setting up queues or configuring job behavior.
Generate ralph.yml and PROMPT.md configuration files for Ralph Orchestrator workflows from plans, tickets, or ideas. Use when setting up Ralph Orchestrator, creating hat-based workflows, or adapting Linear/Jira/markdown plans into ralph.yml + PROMPT.md files.
Sentry issue triage and investigation with automatic cross-linking. Handles issue investigation, AI analysis via Seer, and integration with Linear for tracking fixes. Use when triaging Sentry errors, investigating sentry.io URLs, or creating Linear issues from Sentry.
OpenTelemetry semantic conventions — attribute naming, placement across telemetry levels, stability/versioning, legacy→current migration, and registry namespace reference.
Application-side OpenTelemetry SDK setup — traces, metrics, structured logging across Node.js, Go, Python, Java, .NET, Ruby. Prescriptive guidance for resource attributes, span design, metric instrument selection, sensitive data handling, and validation.
OpenTelemetry orchestrator — auto-activates on observability, telemetry, tracing, metrics, logging, OTel SDK, Collector, semantic conventions, or OTTL requests. Routes to the correct sub-skill.
OpenTelemetry Collector configuration — receivers, processors, exporters, pipelines, sampling, deployment. Covers OTLP, Prometheus, filelog, hostmetrics; processor ordering; pipeline design; head/tail sampling; and RED metric derivation via signaltometrics.
OpenTelemetry semantic conventions — attribute naming, placement across telemetry levels, stability/versioning, legacy→current migration, and registry namespace reference.
Configures end-to-end tracing for an LLM application using OpenTelemetry with LangSmith, Langfuse, or Helicone — span naming, metadata tagging, latency thresholds, and cost tracking.
Application-side OpenTelemetry SDK setup — traces, metrics, structured logging across Node.js, Go, Python, Java, .NET, Ruby. Prescriptive guidance for resource attributes, span design, metric instrument selection, sensitive data handling, and validation.