Visual-explainer is an agent skill that transforms complex technical output into polished, self-contained HTML pages. It serves as a plugin for AI coding agents (Claude Code, Pi, Cursor, Antigravity, Codex, OpenCode, MCP hosts) to generate diagrams, diff reviews, plan reviews, slide decks, narrated videos, and data tables instead of ASCII art. The architecture is modular: a core skill definition (`SKILL.md`) provides workflow and design rules, while separate subsystems handle plan rendering (`plan/render.mjs`), quick-mode JSON-to-HTML conversion (`quick/render.mjs`), a local stdio MCP server (`mcp/server.mjs`), a best-effort PPTX exporter (`pptx/export.mjs`), and a Playwright-based video renderer (`video/render.mjs`). The Pi agent integration includes a native TypeScript extension (`extension.ts`) with `prepare`, `render`, and `render_quick` actions. The codebase demonstrates high craftsmanship with thorough input validation, security-conscious file handling (symlink rejection, atomic writes, output jail), clear error messages, and extensive documentation. The plan renderer is particularly notable as a zero-dependency HTML generator that validates plan structure, reads cited code from disk, and produces interactive pages with decision trees, comments, and keyboard navigation. The quick renderer validates JSON specs against a JSON Schema and produces complete HTML. The MCP server follows the Model Context Protocol strictly and exposes tools, prompts, and resources without calling an LLM or starting an HTTP listener. The video renderer uses Playwright to drive a virtual clock, ensuring frame-perfect timing. The repository is actively maintained with recent commits, a detailed changelog, and version consistency checks across all metadata files.
This repository defines a reusable AI agent skill for role-playing as Zhang Xuefeng, a Chinese education influencer. The core artifact is SKILL.md, a meticulously crafted markdown file with YAML frontmatter that encodes five mental models, eight decision heuristics, a complete expression DNA, and an agentic workflow (research → analyze → respond). It is not a running service or library; it is a configuration file consumed by compatible agent runtimes (Claude Code, Codex, Cursor, etc.) to transform the agent's behavior. The repository also includes six research files (references/research/) that document the persona's writings, conversations, expression style, external views, decisions, and timeline, serving as the evidence base for the skill. The skill demonstrates advanced prompt engineering, cognitive modeling, and agentic protocol design, making it a high-quality example of the Agent Skills pattern. It is actively maintained and has no technical drift as it consists solely of static markdown files.
This repository is a production-grade Agent Skill and CLI tool for generating geometry-safe technical diagrams from structured JSON or natural language descriptions. It produces SVG, PNG, animated GIF, and offline interactive HTML outputs across 12 visual styles (11 generator-driven, 1 AI-authored) and supports 14 UML diagram types with domain-specific semantic contracts for C4, cloud, event, and ops profiles. The system features a deterministic orthogonal routing engine, composition quality gates, a validated SVG-to-GIF motion pipeline with per-style animation contracts, and a strict validation framework. It is distributed as an npm package and a Claude Code skill, with a nested mirror for `npx skills add` installation. The codebase is well-tested with extensive regression and pixel-level gates, though it suffers from some duplication between the root and skill mirror directories and hardcoded contract constants.
Darwin-skill is a meta-skill (SKILL.md) and supporting tooling for autonomously optimizing agent skills (SKILL.md files). It implements a ratchet-based hill-climbing loop that evaluates skills across 9 dimensions (structure, effectiveness, meta-skill), spawns independent LLM judges for paired comparison, and only retains improvements validated by human-in-the-loop checkpoints. The repo includes a Node.js screenshot script for generating result cards, HTML templates, documentation, and references integrating Microsoft Research's SkillLens and SkillOpt papers. Despite being primarily a markdown document, it exhibits high craftsmanship: rigorous empirical grounding, explicit anti-pattern blacklist, versioned design philosophy, and detailed operational procedures. The actual executable code is minimal (one utility script), but the skill definition itself is a sophisticated artifact that demonstrates deep understanding of LLM evaluation pitfalls and optimization strategies.
drawio-skill is a comprehensive toolkit for generating, manipulating, validating, and publishing draw.io architecture diagrams from natural language, codebases, infrastructure-as-code, API specifications, and other structured sources. It provides a unified CLI (`diagramctl.py`) and an optional MCP server (`diagramctl_mcp.py`) that expose workflows for building, syncing, viewing, testing, reviewing, querying, what-if analysis, story generation, and publishing diagrams. The system uses a formal Diagram Intermediate Representation (IR) to separate semantic meaning from geometry, enabling incremental reconciliation, multi-view projection, architecture contract enforcement, and accessible HTML story output. It includes 15+ importers for languages (Python, JS, Go, Rust), IaC (Terraform, Kubernetes, Docker Compose), schemas (SQL, OpenAPI, AsyncAPI, Protobuf, GraphQL), and live infrastructure (Docker, Terraform state). The project is packaged as an AI agent skill compatible with Claude Code, Cursor, Copilot, and other Agent Skills hosts, and ships with GitHub Actions for CI integration. While primarily an application, it also bundles curated icon indices and style presets that could be considered data assets.
This repository provides an agent skill for attaching before/after screenshots and screen recordings to GitHub pull request descriptions. The core product is `skill/SKILL.md`, which instructs AI agents (like Claude Code) on a workflow: capture media using `agent-browser`, format them with the bundled zero-dependency script `skill/scripts/format.mjs`, and publish via `gh --attach`. The formatter generates deterministic markdown tables with image or video comparisons, and performs idempotent replacement of a marked block (`<!-- before-and-after:start/end -->`) in PR bodies, preserving all unrelated prose. The repository also includes a comprehensive verification suite (unit tests, local browser smoke, live GitHub smoke, and an agent smoke test) and a Next.js landing page at `site/`. The architecture is minimal: one dependency-free script, one skill document, and verification infrastructure. It does not wrap browser automation, capture, or media hosting—those are delegated to `agent-browser` and GitHub CLI.