octocov is a Go CLI tool and GitHub Action for collecting, analyzing, and reporting code metrics. It measures code coverage (supporting Go cover, LCOV, SimpleCov, Clover, Cobertura, JaCoCo), code-to-test ratio, test execution time, and custom user-defined metrics. The tool integrates deeply with GitHub: it comments on pull requests, posts job summaries, generates SVG badges, uploads artifacts, and stores reports in configurable datastores (local, S3, GCS, BigQuery, GitHub artifacts, Mackerel). It also supports a central mode that aggregates reports from multiple repositories into a single index with per-repo badges. The architecture is a single Go binary with a Cobra CLI. Key entry points are the root command (cmd/root.go) which orchestrates the CI workflow, and subcommands for badge generation, diff comparison, report dump, and central aggregation. Configuration is via YAML (config/). The coverage package contains format-specific parsers that normalize reports into a common model. The datastore package provides a clean interface for storing/retrieving reports, with implementations for each backend. The badge package renders SVG badges using an embedded template and font. The internal page package serves a React-based viewer for coverage diffs. Primary data flow: Parse coverage files from CI (or local), compute metrics, optionally compare with previous report from a datastore, generate badges/comments/summaries, store the new report. Custom metrics are ingested via environment variable files and validated against a JSON schema. Acceptable conditions (using expr-lang) allow failing CI when metrics regress beyond thresholds. External dependencies include GitHub API (google/go-github), AWS SDK, GCP SDK, BigQuery client, Mackerel API, and a JS bundle for the viewer.
Custom Pod Autoscaler is a Kubernetes operator that provides custom autoscaling functionality. It runs as a pod in a Kubernetes cluster, periodically gathering metrics (via custom scripts or Kubernetes metrics server), evaluating those metrics to determine desired replica count, and scaling target resources accordingly. The application features a REST API for manual interaction, supports hooks for custom logic at various points in the process, and is designed to work with any language or framework through shell commands or HTTP requests. The architecture follows clean separation of concerns with distinct packages for configuration loading, execution abstraction, metric gathering, evaluation, and scaling logic.