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Found 865 Skills
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
Debug, develop, and operate apps hosted on Railway (railway.com) from the CLI — list projects/services, tail and filter build/deploy/HTTP logs, read metrics, inspect and set variables, deploy from the current directory, redeploy / restart / roll back, run local commands with the service's env, SSH into containers, and open a DB shell. Authenticates via the `RAILWAY_TOKEN` environment variable (account token, or project-scoped token). Optional bundled scripts (`scripts/preflight.sh`, `scripts/debug.sh`, `scripts/smoke.sh`) are Onsager-specific wrappers — other repos can ignore them or fork. Triggers include "deploy to railway", "railway deploy this", "railway logs", "tail railway logs", "why is my railway service crashing", "why did the build fail on railway", "railway 500s", "railway latency", "show railway http logs", "redeploy on railway", "restart my railway service", "roll back railway", "set a railway env var", "list railway variables", "railway metrics", "is my railway service healthy", "connect to my railway postgres", "ssh into railway", "run this locally with railway env", "list railway projects/services/deployments", and (Onsager-specific) "check railway", "preflight", "smoke test", "is the deploy healthy".
Guides organizational and business storytelling—narrative structure (setup, tension, resolution), audience-tailored stories for executives, customers, boards, and teams, honest data and metrics framing, product and strategy narratives, incident and postmortem storytelling, and actuarial or insurance risk narratives for non-technical audiences. Covers story spine, key messages, and visual or slide narrative outlines. Use when the user says "tell the story", "storytelling", "narrative for executives", "data story", "board presentation narrative", "explain with a story", "story arc", "key message", "compelling narrative", "pitch story", or "incident story"—not cross-department reframing only (cross-department-translation), company-wide comms cadence and crisis wording packs (communication-lead), long-form creative fiction or screenwriting, brand copy without strategy context, or technical documentation and API reference (tech-writer-researcher).
Migrate configuration from Bluejay voice AI testing platform to Coval. Use when customer says "migrate from bluejay", "bluejay migration", "import bluejay config", or needs to transfer agents, simulations, metrics, and schedules from Bluejay to Coval.
Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.
Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Configure and use ktx to build an executable context layer for AI agents querying data warehouses with semantic layers, wiki knowledge, and approved metrics
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."
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.