Tile38 is an in-memory geolocation data store, spatial index, and realtime geofencing server written in Go. It provides a Redis-compatible interface (RESP, HTTP, WebSocket, Telnet) for storing and querying geospatial objects (points, GeoJSON, bounding boxes, geohashes, tiles) with commands like SET, GET, NEARBY, WITHIN, INTERSECTS, and SCAN. The server supports leader/follower replication, AOF persistence, Lua scripting, and realtime geofencing via webhooks, pub/sub channels, and live connections. Its architecture is a single-binary server with a modular internal structure: a collection layer combining B-tree and R-tree indexing, a binary-encoded object and field storage for memory efficiency, a pluggable endpoint system for webhook delivery (HTTP, Kafka, MQTT, NATS, AMQP, SQS, etc.), and a custom protocol multiplexer. The codebase demonstrates high craftsmanship with reusable components like the collection data structure, glob matching, binary field lists, and endpoint manager, though some areas show signs of organic growth (e.g., large server.go, deprecated flags).
OPC Skills is a curated marketplace and toolchain of AI agent skills designed for solopreneurs, indie hackers, and one-person companies. The repository contains 9 skills (reddit, twitter, producthunt, domain-hunter, logo-creator, banner-creator, nanobanana, seo-geo, requesthunt, archive) that each provide structured prompts and Python scripts to automate tasks like market research, SEO/GEO optimization, domain hunting, logo/banner creation, and social media analysis. Skills are distributed via the Agent Skills standard (`npx skills add`) and also available as a Claude Code plugin. The project includes a full website at opc.dev powered by a Cloudflare Worker (website/worker.js) with per-skill pages, a blog, SEO/GEO optimization, and install statistics tracking. CI/CD pipelines deploy the website, test installs across platforms, and fetch install stats daily. The codebase follows a consistent architecture: each skill has a SKILL.md with YAML frontmatter, optional Python scripts following a credential → API wrapper → per-endpoint script pattern with TOON (Token-Oriented Object Notation) output, and dependencies declared between skills. While primarily an application, it has data-research characteristics due to its heavy use of external API scraping for market signals (Reddit, Twitter, ProductHunt) and SEO analytics. The repository is actively maintained with frequent releases, a changelog, and an agent-assisted development workflow (Ralph). Notable strengths include the reusable skill template pattern, cross-session memory via the archive skill, and the standalone SEO audit script. Key risks include reliance on multiple third-party API keys, tight coupling between some skills, and fragility in the install stats scraper.
This repository is a comprehensive Go library for geometric data structures and operations. It provides implementations of OGC-standard geometry types (Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, GeometryCollection) with support for 2D, 3D, and measured dimensions. The library includes algorithms for computational geometry (area, centroid, convex hull, line intersection, etc.) and encoders/decoders for formats like WKT, WKB, GeoJSON, KML, and IGC. Architecture is modular: core `geom` package defines types and interfaces, `xy`/`xyz` packages handle planar/spatial algorithms, `encoding/*` packages manage format serialization, and utilities like `transform` and `sorting` offer reusable helpers. Command-line tools for IGC parsing and PostGIS integration demonstrate practical usage. The code emphasizes clean separation of concerns, interface-driven design, and thorough testing including property-based and fuzzing tests.
GEO Optimizer is a Go library for Generative Engine Optimization that improves content visibility in AI search engines. It features a layered architecture with LLM abstraction, rule-based content scoring, and pluggable optimization strategies. The system optimizes content through a pipeline that applies strategies like structure enhancement, schema generation, and FAQ creation, using LLMs to generate optimized content while tracking improvements through scoring mechanisms. Key design patterns include the strategy interface for interchangeable optimizations and modular separation of concerns in the scoring system.
This repository is a Python application/library for generating 2D heatmaps from coordinate data. The core module src/heatmap77/heatmap.py implements a complete pipeline: file readers parse plain text, CSV, GPX, and ESRI Shapefile inputs; coordinate systems and projections (equirectangular, mercator) map lat/lon to pixel space; linear and gaussian kernels spread point mass; sparse matrix classes accumulate contributions with optional decay; and an ImageMaker renders the result to PNG/PPM, optionally compositing over OSM tiles or custom backgrounds. The package exposes both a CLI entry point (heatmap77) and a programmatic API used by the examples.
This repository is a Claude Code skill bundle for Generative Engine Optimization (GEO) and SEO auditing, distributed as an installable plugin. The core deliverable is a composite GEO Score (0-100) computed from six weighted categories: AI citability, brand authority, content E-E-A-T, technical health, structured data, and platform optimization. Users invoke slash commands such as /geo audit, /geo quick, /geo citability, /geo llmstxt, and /geo report, which orchestrate a set of skill definitions, agent prompts, and Python helper scripts to crawl external websites, analyze AI crawler accessibility, extract JSON-LD, generate llms.txt files, score content passages, and produce client-ready reports and proposals. It also includes a small Flask + HTMX CRM webapp for managing prospects and a white-label configuration system for agencies. The architecture is primarily a composition of markdown-based skills and agents with Python scripts as deterministic helpers. The main entry points are geo/SKILL.md (orchestrator), the scripts under scripts/, and the webapp at scripts/webapp/app.py. The system behaves as a productized agency service: it runs audits against arbitrary client domains, produces quantitative scores, generates branded PDF reports, pricing proposals, and monthly delta comparisons. Some research-oriented artifacts exist - cited market studies, scoring methodology documentation, and heuristic rubrics - but the dominant character is an application toolkit for practitioners rather than a data pipeline. Strengths include a clear JSON-to-stdout contract between scripts and skills, deterministic scoring modules such as citability_scorer.py and llmstxt_generator.py, reusable JSON-LD schema templates, and a sensible separation between deterministic checks and LLM-judged scoring. Weaknesses include documentation drift (inconsistent skill counts and command references), duplicated scoring rubrics in agent vs skill versions, a legacy ReportLab PDF pipeline documented alongside the newer pandoc/Chrome approach, and only narrow test coverage focused on fetch_page.py.
This is a minimal Node.js library for converting geographic bounding boxes into centerpoint/zoom pairs for static map images, and vice versa. It wraps the @mapbox/sphericalmercator library to perform Mercator projection calculations, caching SphericalMercator instances by tile size and antimeridian flag for performance. The library exposes two functions: 'viewport' computes the center and zoom that best fits a bounding box within given pixel dimensions, and 'bounds' computes the bounding box from a centerpoint, zoom, and dimensions. It supports adjustable tile sizes (default 256) and optional float zooms. The code is well-structured and covered by tests, but the library is simple and does not contain independently reusable subcomponents. It is currently in a stale maintenance state with no recent updates.