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Found 105 Skills
Logging best practices for applications and services including structured logging, log levels, and log management strategies
Operate execution flow across GitHub and Linear by triaging issues and pull requests, linking active work, and keeping GitHub public-facing while Linear remains the internal execution layer. Use when the user wants backlog control, PR triage, or GitHub-to-Linear coordination.
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".
This skill should be used when the user asks to "finish a feature", "merge feature branch", "complete feature", "git flow feature finish", or wants to finalize and merge a feature branch into develop.
Use when planning work (to create items and tasks), when starting implementation (to mark tasks in-progress), when completing work (to mark tasks done), or to check backlog status. Manages .backlogmd/ for features, bugfixes, refactors, and chores.
Analyze application logs to detect errors, patterns, anomalies, and generate insights. Use when troubleshooting issues or analyzing system behavior.
Backlog Management. Users can submit ideas or pain points at any time, and the AI is responsible for following up, organizing, merging, and archiving them into the backlog file. When users are preparing to launch a new version, it assists in filtering from the backlog. Driven by pain points, no advance scheduling is done.
Log management - search, pipelines, archives, and cost control.
Generic migration orchestrator that reads CHANGELOG.md to understand and execute version-specific migrations
Plans sprint by selecting items from backlog, defining objective, capacity, and execution order. Use at the beginning of a work cycle to align what will be done.
Design structured logging systems with context propagation. Use to ensure Python applications are observable and logs are machine-readable.
Package specification compliance for Elastic integration packages. Covers manifest structure (format_version, conditions, variables, routing rules), changelog schema and semantic version bumps, and alignment with the upstream elastic/package-spec. Use when building or reviewing manifest.yml, changelog.yml, or debugging elastic-package lint/check errors on package metadata.