cosmix/loom

Observability visualization with Grafana and LGTM stack. Dashboard design, panel configuration, alerting, variables/templating, and data sources. USE WHEN: Creating Grafana dashboards, configuring panels and visualizations, writing LogQL/TraceQL queries, setting up Grafana data sources, configuring dashboard variables and templates, building Grafana alerts. DO NOT USE: For writing PromQL queries (use /prometheus), for alerting rule strategy (use /prometheus), for general observability architecture (use senior-infrastructure-engineer). TRIGGERS: grafana, dashboard, panel, visualization, logql, traceql, loki, tempo, mimir, data source, annotation, variable, template, row, stat, graph, table, heatmap, gauge, bar chart, pie chart, time series, logs panel, traces panel, LGTM stack.

Comprehensive test implementation across all domains including unit, integration, e2e, security, infrastructure, data pipelines, and ML models. Covers TDD/BDD workflows, test architecture, flaky test debugging, and coverage analysis.

Kubernetes node autoscaling and cost optimization with Karpenter. Use when implementing node provisioning, spot instance management, cluster right-sizing, node consolidation, or reducing compute costs. Covers NodePool configuration, EC2NodeClass setup, disruption budgets, spot/on-demand mix strategies, multi-architecture support, and capacity-type selection.

Feature flag patterns for controlled rollouts, A/B testing, kill switches, and runtime configuration. Use when implementing feature toggles, feature flags, gradual rollouts, canary releases, percentage rollouts, dark launches, user targeting, A/B tests, experiments, circuit breakers, emergency kill switches, model switching, or infrastructure flags.

Restructures existing code to improve readability, maintainability, and performance without changing external behavior. USE WHEN: Restructuring code without changing behavior, extracting methods/classes, removing duplication, applying design patterns, improving code organization, reducing technical debt. DO NOT USE: For bug fixes (use /debugging), for adding tests (use /testing), for new features (implement directly). TRIGGERS: refactor, restructure, rewrite, clean up, simplify, extract, inline, rename, move, split, merge, decompose, modularize, decouple, technical debt, code smell, DRY, SOLID, improve code, modernize, reorganize.

Authentication and authorization patterns including OAuth2, JWT, RBAC/ABAC, session management, API keys, password hashing, and MFA. USE WHEN: Implementing login flows, access control, identity management, tokens, permissions, session handling, API key authentication, or MFA. DO NOT USE: For security vulnerability scanning (use /security-scan), for security audits (use /security-audit), for threat modeling (use /threat-model). TRIGGERS: login, logout, signin, signup, authentication, authorization, password, credential, token, JWT, OAuth, OAuth2, OIDC, SSO, SAML, session, cookie, RBAC, ABAC, permissions, roles, MFA, 2FA, TOTP, API key, PKCE.

Comprehensive logging and observability patterns for production systems including structured logging, distributed tracing, metrics collection, log aggregation, and alerting. Triggers for this skill - log, logging, logs, trace, tracing, traces, metrics, observability, OpenTelemetry, OTEL, Jaeger, Zipkin, structured logging, log level, debug, info, warn, error, fatal, correlation ID, span, spans, ELK, Elasticsearch, Loki, Datadog, Prometheus, Grafana, distributed tracing, log aggregation, alerting, monitoring, JSON logs, telemetry.

Background job processing patterns including job queues, scheduled jobs, worker pools, and retry strategies. Use when implementing async processing, job queues, workers, task queues, async tasks, delayed jobs, recurring jobs, scheduled tasks, ETL pipelines, data processing, ML training jobs, Celery, Bull, Sidekiq, Resque, cron jobs, retry logic, dead letter queues, DLQ, at-least-once delivery, exactly-once delivery, job monitoring, or worker management.

Data validation patterns including schema validation, input sanitization, output encoding, and type coercion. Use when implementing validate, validation, schema, form validation, API validation, JSON Schema, Zod, Pydantic, Joi, Yup, sanitize, sanitization, XSS prevention, injection prevention, escape, encode, whitelist, constraint checking, invariant validation, data pipeline validation, ML feature validation, or custom validators.

Event-driven architecture patterns including message queues, pub/sub, event sourcing, CQRS, and sagas. Use when implementing async messaging, distributed transactions, event stores, command query separation, domain events, integration events, data streaming, choreography, orchestration, or integrating with RabbitMQ, Kafka, Apache Pulsar, AWS SQS, AWS SNS, NATS, event buses, or message brokers.