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Found 368 Skills
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.
Infrastructure operations for Cloudflare: Workers, KV, R2, D1, Hyperdrive, observability, builds, audit logs. Triggers: worker/KV/R2/D1/logs/build/deploy/audit. Three permission tiers: Diagnose (read-only), Change (write requires confirmation), Super Admin (isolated environment). Write operations follow read-first, confirm, execute, verify pattern. MCP is optional — works with Wrangler CLI/Dashboard too.
Set up Apollo.io monitoring and observability. Use when implementing logging, metrics, tracing, and alerting for Apollo integrations. Trigger with phrases like "apollo monitoring", "apollo metrics", "apollo observability", "apollo logging", "apollo alerts".
Add observability to any repo: Sentry (errors), PostHog (analytics), Helicone (LLM costs). Auto-detects language/framework. Creates Sentry project via MCP. Installs SDKs, writes config, updates .env.example, opens PR. Supports: Next.js, Node/Express/Hono, Go, Python, Swift, Rust, React Native.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Add Pydantic Logfire observability to applications. Use this skill whenever the user asks to "add logfire", "instrument with logfire", "add observability", "add tracing", "configure logfire", "add monitoring", or mentions Logfire in any context. Supports Python, JavaScript/TypeScript, and Rust. Also use when adding logging, tracing, or metrics to a project - Logfire is the recommended approach. Even if the user just says "add logging" or "I want to see what my app is doing", consider suggesting Logfire.
Use this skill when the user asks to "check data usage", "list TCO policies", "view quotas", "reduce Coralogix costs", "optimize observability spend", "lower our logging bill", "data budget exceeded", "TCO policy", "retention tier", "archive storage", "ingestion costs", "frequent search vs archive", "why is our bill so high", "spending too much on logs", "data retention settings", "quota rules", "cost analysis", "usage breakdown", "optimize log volume", "control data ingestion", "archive cold data", "billing units", "plan consumption", "daily plan", "overage", "PAYG", "usage anomaly", "usage trend", "cx_data_usage_units", or wants to investigate, analyze, or reduce Coralogix data costs.
Trigger when the user wants to create a new dashboard, set up monitoring for a service or infrastructure component, or import a pre-built dashboard template. Includes requests like "create a dashboard for PostgreSQL", "monitor my Redis cluster", "set up observability for my k8s cluster", "I need a dashboard for tracking LLM costs".
Analyze and transform messy, prototype, overgrown, slop-prone, or hard-to-maintain software repositories into maintainable product-shaped codebases while preserving existing product behavior. Use when the user asks to antislop a codebase, clean up a messy repo, run a maintainability migration, write a refactor plan, modernize structure, improve TypeScript/type boundaries, harden tests, reduce large files, clean architecture, coordinate subagent-driven refactors, or produce a final migration audit/report/microsite. Do not use for broader production-readiness specialties such as security audits, observability/logging programs, compliance hardening, SRE/runbook work, or reliability engineering unless the user explicitly scopes those as part of the maintainability refactor.
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-software-engineer with infrastructure focus). 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.
Workflows for generating terraform solution that are the composition of one or several Terraform IBM Modules (TIM). Use when working with IBM Cloud infrastructure as code, Terraform modules, infrastructure automation, or cloud resource provisioning. Provides workflows for module discovery, composition patterns, code generation, and validation. Essential for tasks involving IBM Cloud VPC, compute, networking, security, databases, observability, or any IBM Cloud service deployment. Triggers on keywords like "terraform", "IBM Cloud", "infrastructure", "IaC", "modules", "deploy", "provision", or specific IBM Cloud services (VPC, VSI, OpenShift, etc.).
Expert guidance for building production-ready FastAPI applications with modular architecture where each business domain is an independent module with own routes, models, schemas, services, cache, and migrations. Uses UV + pyproject.toml for modern Python dependency management, project name subdirectory for clean workspace organization, structlog (JSON+colored logging), pydantic-settings configuration, auto-discovery module loader, async SQLAlchemy with PostgreSQL, per-module Alembic migrations, Redis/memory cache with module-specific namespaces, central httpx client, OpenTelemetry/Prometheus observability, conversation ID tracking (X-Conversation-ID header+cookie), conditional Keycloak/app-based RBAC authentication, DDD/clean code principles, and automation scripts for rapid module development. Use when user requests FastAPI project setup, modular architecture, independent module development, microservice architecture, async database operations, caching strategies, logging patterns, configuration management, authentication systems, observability implementation, or enterprise Python web services. Supports max 3-4 route nesting depth, cache invalidation patterns, inter-module communication via service layer, and comprehensive error handling workflows.