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Found 219 Skills
Automatically discover observability and monitoring skills when working with Prometheus, Grafana, distributed tracing, structured logging, metrics, alerting, dashboards, or monitoring. Activates for observability development tasks.
Explores codebase with structural and text search using ast-grep (syntax-aware AST matching), ripgrep (fast text/regex search), and fd (file discovery). Use when (1) navigating unfamiliar code or understanding architecture, (2) tracing call flows, symbol definitions, or usages, (3) answering "how does this work" or "where is this defined/called" questions, (4) finding files by name, extension, or path pattern, (5) pre-refactoring analysis to locate all references before changing code.
Generate Go repository port interfaces and implementations following GO modular architechture conventions (Gorm, PingoDB, OTEL tracing, Fx DI, ports architecture). Use when creating data access layers for entities in internal/modules/<module>/ including CRUD operations (Create, FindAll, FindByID, Update, Delete), custom queries, pagination, or transactions.
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".
Vercel observability for Web Analytics, Speed Insights, logs, tracing, alerts, and observability tooling. Use when monitoring performance or debugging production behavior on Vercel.
Rust debugging skill for systems programming. Use when debugging Rust binaries with GDB or LLDB, enabling Rust pretty-printers, interpreting panics and backtraces, debugging async/await with tokio-console, stepping through no_std code, or using dbg! and tracing macros effectively. Activates on queries about rust-gdb, rust-lldb, RUST_BACKTRACE, Rust panics, debugging async Rust, tokio-console, or pretty-printers.
strace and ltrace skill for system call and library call tracing. Use when a binary behaves incorrectly without crashing, diagnosing file-not-found errors, permission failures, network issues, or unexpected library calls by tracing syscalls and library function calls. Activates on queries about strace, ltrace, syscall tracing, library interception, ENOENT, EPERM, strace -e, or diagnosing binary behaviour without a debugger.
Integrates Kelet into AI applications end-to-end: instruments agentic flows with OTEL tracing, maps session boundaries, adds user feedback signals (VoteFeedback, edit tracking, coded behavioral hooks), generates synthetic signal evaluator deeplinks, and verifies the integration. Kelet is an AI agent that performs Root Cause Analysis on AI app failures — it ingests traces and signals, clusters failure patterns, and suggests fixes. Use when the developer mentions Kelet or asks to integrate, set up, instrument, or add tracing/signals/feedback to their AI app. Triggers on: "integrate Kelet", "set up Kelet", "add Kelet", "instrument my agent", "connect Kelet", "use Kelet".
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework with built-in backward tracing for deep-stack failures, ensuring root-cause understanding before implementation
Grafana Tempo distributed tracing backend. Covers TraceQL query language (span selectors, attribute scopes, pipeline operators, structural operators, metrics functions), trace ingestion via OTLP/Jaeger/Zipkin, Tempo architecture (distributor/ingester/compactor/querier/metrics-generator), full configuration reference with YAML, metrics-from-traces (span metrics, service graphs, TraceQL metrics), deployment modes (monolithic/microservices/Helm/Kubernetes), multi-tenancy, performance tuning, caching, and HTTP API. Use when working with distributed traces, writing TraceQL queries, deploying Tempo, configuring trace pipelines, or setting up Grafana-Tempo integrations (traces-to-logs, traces-to-metrics, traces-to-profiles).
Builds composable, pipeable function chains on the iii engine. Use when building functional pipelines, effect systems, or typed composition layers where each step is a pure function with distributed tracing and retry.
Set up orq.ai observability for LLM applications. Use when setting up tracing, adding the AI Router proxy, integrating OpenTelemetry, auditing existing instrumentation, or enriching traces with metadata.