Loading...
Loading...
Found 875 Skills
When the user wants to build or improve a sales bot's ability to track conversion rates, drop-off points, and response patterns. Also use when the user mentions "bot analytics," "conversation metrics," "tracking performance," "measuring bot effectiveness," or "conversion tracking."
Analyze production Agentforce agent behavior using session traces and Data Cloud. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries. DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use agentforce-generate); writes or runs test specs (use agentforce-test); uses sf agent preview for local development iteration; deploys or publishes agents.
AWS CloudWatch monitoring for logs, metrics, alarms, and dashboards. Use when setting up monitoring, creating alarms, querying logs with Insights, configuring metric filters, building dashboards, or troubleshooting application issues.
AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
Guide for implementing Grafana Mimir - a horizontally scalable, highly available, multi-tenant TSDB for long-term storage of Prometheus metrics. Use when configuring Mimir on Kubernetes, setting up Azure/S3/GCS storage backends, troubleshooting authentication issues, or optimizing performance.
RevenueCat metrics, customer data, and documentation search. Use when querying subscription analytics, MRR, churn, customers, or RevenueCat docs.
MLflow experiment tracking via Python API. TRIGGERS - MLflow metrics, log backtest, experiment tracking, search runs.
Monitors active goals against current state. Calculates gaps, scores urgency, detects at-risk goals, generates alerts. Reads goals from strategy/goals/active/, computes progress from metrics, outputs prioritized alerts and updates goal status.
Analyze performance metrics and identify slow transactions in Sentry
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.
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.