Loading...
Loading...
Found 384 Skills
Comprehensive guide for production-ready Python backend development and software architecture at scale. Use when designing APIs, building backend services, creating microservices, structuring Python projects, implementing database patterns, writing async code, or any Python backend/server-side development task. Covers Clean Architecture, Domain-Driven Design, Event-Driven Architecture, FastAPI/Django patterns, database design, caching strategies, observability, security, testing strategies, and deployment patterns for high-scale production systems.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Use this skill when implementing logging, metrics, distributed tracing, alerting, or defining SLOs. Triggers on structured logging, Prometheus, Grafana, OpenTelemetry, Datadog, distributed tracing, error tracking, dashboards, alert fatigue, SLIs, SLOs, error budgets, and any task requiring system observability or monitoring setup.
Use this skill when working with PostHog - product analytics, web analytics, feature flags, A/B testing, experiments, session replay, error tracking, surveys, LLM observability, or data warehouse. Triggers on any PostHog-related task including capturing events, identifying users, evaluating feature flags, creating experiments, setting up surveys, tracking errors, and querying analytics data via the PostHog API or SDKs (posthog-js, posthog-node, posthog-python).
Add Opik tracing to an existing codebase. Detects language (Python/TypeScript), identifies LLM frameworks, adds appropriate decorators and integrations, marks entrypoints, and wires up environment config. Use for "instrument my code", "add opik tracing", "add observability", or "trace my agent".
Build search applications and query log analytics data with OpenSearch. Use this skill when the user mentions OpenSearch, search app, index setup, search architecture, semantic search, vector search, hybrid search, BM25, dense vector, sparse vector, agentic search, RAG, embeddings, KNN, PDF ingestion, document processing, or any related search topic. Also use for log analytics and observability — when the user wants to set up log ingestion, query logs with PPL, analyze error patterns, set up index lifecycle policies, investigate traces, or check stack health. Activate even if the user says log analysis, Fluent Bit, Fluentd, Logstash, syslog, traceId, OpenTelemetry, or log analytics without mentioning OpenSearch.
Use when writing or reviewing TypeScript/full-stack code. Encodes principles for type safety (branded types, discriminated unions, end-to-end types), real tests over mocks, OpenTelemetry observability, and picking the right abstractions instead of premature ones.
Builds, configures, debugs, and optimizes AWS observability using CloudWatch (Logs Insights, Metrics, Alarms, Dashboards, EMF), X-Ray, CloudTrail, and ADOT. Covers Log Insights query syntax (fields, filter, stats, parse, pattern, join, subqueries), alarm configuration (metric, composite, anomaly detection, missing data treatment), dashboard design, custom metrics (PutMetricData, EMF, metric filters), X-Ray tracing (ADOT, sampling rules, annotations vs metadata), ADOT collector config, and CloudTrail auditing. Use when the user mentions CloudWatch, Log Insights, alarms, INSUFFICIENT_DATA, dashboards, custom metrics, EMF, X-Ray, traces, sampling, CloudTrail, who deleted, ADOT, OpenTelemetry, observability, monitoring, synthetics, canaries, or troubleshooting alarm behavior. Do NOT use for application logging setup, container log drivers, or security threat detection.
Node.js/Bun backend reference skill: TypeScript-first, structured error handling, pino logging, Zod validation, async patterns, HTTP server conventions, database access, auth, queues, caching, testing, security, CLI tooling, and observability. Covers both Node.js and Bun runtimes. Use when the task touches server-side TypeScript/JavaScript code and should follow the project's backend conventions.
Pre-production audit that scans a codebase for security, database, deployment, code quality, AI/LLM, dependency, frontend, and observability issues. Intercepts deploy commands and blocks until critical items pass. Stack-agnostic. Use for "run ship gate", "am I ready to ship", "pre-launch audit", "can I deploy", "push to production", "go live checklist", "preflight check". Not for CI/CD setup or infra provisioning.
Run a local observability and control dashboard for OpenClaw AI agents with real-time collaboration, task management, and safety-first defaults.
Workload-aware architecture design for Apache Doris. MUST USE when designing data architectures, choosing between data models, planning ingestion strategies, sizing clusters, or translating business requirements into Apache Doris system designs. Complements doris-best-practices with decision frameworks and sizing-first workflow. Use when user describes a workload involving: IoT, sensor data, telemetry, real-time analytics, dashboard, log analysis, log search, CDC sync, time-series, device monitoring, point query service, ad-hoc analytics, lakehouse federation, ETL/ELT pipeline, report analytics, clickstream, user behavior, observability, metrics, fleet tracking, or any OLAP workload requiring table design from scratch. Also triggers on prompts like: "design a table for...", "how should I store...", "build an architecture for...", "we have X devices sending data every Y seconds", "recommend a cluster size for...", "what data model should I use for...", "we need to ingest X GB/day", "migrate from MySQL/PostgreSQL to Apache Doris". Also use for legacy analytics/search/serving stack consolidation prompts even when Apache Doris is not named explicitly, including replacing or migrating from Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms.