513 skills · sorted by relevance
Manage continuously running or cloud-hosted agents with lifecycle controls, logs, metrics, restricted permissions, and emergency stops. It guides incident response, staged deployments, rollbacks, and tracking success rates and cost per successful task.
Helps you write Python code for browser automation with browser-use. Its reference guides cover installation, agent and browser settings, custom tools, model connections, and monitoring so you can configure and extend your automation.
Connect an application's LLM calls through the Caveman Cloud gateway to measure usage and cost without changing request content. The skill updates connection settings and verifies the integration with one small, real provider request.
Add logs, metrics, traces that follow requests across services, and actionable alerts to production features. This skill guides signal design, request identification, response instructions, and checks that the collected data can explain failures.
Inspect how a task ran across agents by collecting recorded execution spans, or timed processing steps. Build a trace tree showing agent activity, duration, errors, and bottlenecks, with a summary of the execution flow.
Monitor a swarm, a group of agents, and receive notifications when its status or agent count changes. A watcher script checks status periodically and streams changes through Monitor; separate tools provide one-time status and health checks.
Summarizes recorded system metrics for a chosen period, including completed tasks, errors, memory use, and token consumption. It compares values with historical baselines and flags unusual changes to help assess agent team health.
Export recorded AI usage costs and budgets as Prometheus metrics or JSON. Feed external dashboards with totals, model tier breakdowns, session costs, and budget utilization, or send the same data to a webhook endpoint.
Set up or review production error monitoring to capture unhandled errors, failed requests, and performance signals. Check event context, release labels, and alert routing so teams receive useful information when problems occur.
Reconstruct how an AI coding agent produced a feature, pull request, or report using local ax records. Connect sessions, commits, skills, and tool activity into an evidence-based sequence with key decisions, checks, and a repeatable recipe.
Deploy and operate AI agents with AWS Bedrock AgentCore. Choose services for hosting agents, connecting external APIs, managing credentials and conversation state, monitoring activity, and assessing agent quality through automated evaluations.
Design mock APIs, simulated services that reproduce expected responses, authentication, errors, and delays. Define scenarios and repeatable test data to support frontend development, integration testing, and realistic demos before the real backend is ready.
Coordinate specialist agents to investigate and improve application performance across databases, backend services, frontend code, and delivery infrastructure. Establish baseline measurements, address observed bottlenecks, validate changes with load tests, and set up monitoring to detect future slowdowns.
Inspect local AI coding session logs with agenttrace to identify cost spikes, delays, failed tools, and retry loops. Produce readable reports, compare attempts, and configure automated checks for unusual or unhealthy runs.
Add monitoring to AI agents with response times, token usage, estimated costs, and error metrics. Trace records follow model and tool calls step by step, helping teams investigate failures and understand operating costs.
Build Grafana dashboards that show application and infrastructure health through request rates, errors, response times, and resource usage. Organize metrics into useful panels, add filters and alerts, and manage dashboard definitions as code.
Set up distributed tracing with Jaeger and Tempo to follow requests across services. Examine delays, service dependencies, and error propagation, with guidance on sampling, linking logs to traces, and troubleshooting missing trace data.
Set up Phoenix tracing for LLM applications in Python or TypeScript. The skill guides automatic and custom recording of individual operations, conversation grouping, metadata, feedback, and production settings such as batching and personal data masking.
Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.
Apply consistent names to SGLang speculative decoding code, which verifies draft tokens to generate text. Distinguish tokens with or without the bonus token, counts, counters, and arrays when adding identifiers, renaming code, or reviewing changes.
Design LLM applications using patterns for answers grounded in documents, agents that use tools, and prompt testing. Implementation examples also cover monitoring response quality, latency, costs, and failures during operation.
Investigate production issues by searching Datadog logs, querying metrics, and following request traces across services. Compare error counts across time periods, inspect surrounding logs, and manage dashboards through a command-line tool.
Connect cognee to additional databases and data sources through community packages. The skill covers installing and registering adapters, importing data through connectors, configuring monitoring, and developing extension packages with examples and tests.
Audit a codebase before production deployment across eight areas, including security, databases, dependencies, and AI usage. Receive findings with file locations, manual checklists, and a release verdict that blocks shipping when critical issues remain.