Kami is a locally installed AI-agent skill and plugin for professional document generation, aimed at Claude Code, Codex, Cursor, and MCP-capable agents. It is not a hosted service: agents invoke it through natural language, and the skill orchestrates a deterministic pipeline that validates structured content, fills self-contained HTML templates, renders PDFs via WeasyPrint, optionally exports PPTX or Marp slides, and runs a battery of quality gates before declaring a document deliverable. The repo also ships a zero-dependency MCP stdio server exposing tools such as kami_templates, kami_render, kami_check, and kami_screenshot.
Impeccable is a design-quality and anti-pattern detection engine for front-end web projects, primarily targeting AI-generated UI. It provides a Rust-based CLI, a WASM browser extension core, and a distributed skill system that plugs into over 20 AI coding assistants (Claude, Cursor, Gemini, etc.). The system runs deterministic detection rules against HTML/CSS to flag common issues like poor contrast, typography problems, layout flaws, and 'AI slop' patterns, and also supports live browser iteration with session management, component review workflows, and comp-vs-build image comparison. All detection logic is implemented as a port of a JavaScript reference, with extensive vector-replay tests to maintain byte-for-byte behavioral parity. Architecturally, the repository is a large Rust workspace with 14+ crates handling distinct concerns: static HTML parsing and CSS cascade (`crates/html`), rule evaluation (`crates/foundation`, `crates/core`), browser automation via CDP (`crates/browser`), live-mode HTTP servers and session management (`crates/live`), and skill packaging/distribution (`crates/skills`, `scripts/`). A Chrome DevTools extension embeds the WASM core for in-page scanning. The project is actively maintained with respect to both the Rust core and the constantly shifting AI-harness integration surface, though that breadth introduces significant drift risk across duplicated skill assets and provider-specific configuration. The codebase demonstrates strong engineering discipline—clean abstractions, pervasive testing, and careful parity management—but the sheer scale and duplication in the skill/plugin tree reduces overall reusability.
This is a mature, actively maintained AI agent skill called 'last30days' that enables agents (Claude Code, Codex, Grok, Gemini CLI, MCP hosts) to research any topic by aggregating content from 20+ social and news platforms (Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, arXiv, Techmeme, Digg, and more) over a rolling 30-day window. The system is primarily a CLI engine (Python) with a companion Go MCP server for Claude Desktop. It employs a sophisticated pipeline: query planning by intent, parallel retrieval from multiple backends (keyless APIs, paid scrapers, CLI tools, LLM-based search), source-specific normalization, engagement-weighted relevance scoring, cross-source clustering, and deterministic fusion (weighted RRF) with per-author caps and diversity floors. Output is rendered as structured JSON, compact text, or self-contained HTML with optional publishing and Atom feeds. The codebase emphasizes security (credential hygiene, no browser cookie reads in unattended mode), determinism (fixture-based evaluation harness with grounding/recency/coherence scores), and graceful degradation (per-source fallback chains, never-raise error envelopes). While classified as an application (it's a skill/plugin for agent hosts), it exhibits strong data engineering traits: source adapters, normalization pipelines, scoring models, and an offline evaluation framework.
Ponytail is a skill ecosystem for AI coding agents that enforces a 'lazy senior developer' mindset through a priority ladder: YAGNI, reuse existing code, prefer stdlib/native features, avoid unrequested abstractions, and write minimal working code. It delivers this via platform-specific adapters (hooks, command files, plugin manifests) for agents like Claude Code, Codex, GitHub Copilot, OpenCode, Gemini CLI, and others. The core skill definition lives in skills/ponytail/SKILL.md, with companion skills for review, audit, debt tracking, gain scoring, and help. The system includes automated validation scripts to maintain consistency across platform-specific rule copies and version manifests, benchmark suites to measure code/token/cost reductions, and test coverage for hook behavior and plugin integrity.
CCPM is a structured project management skill for AI agents (Claude Code, Codex, etc.) that enforces a spec-driven development workflow: PRD → Epic → GitHub Issues → Parallel Agents → Shipped Code. The codebase consists of markdown reference documents (plan.md, structure.md, sync.md, execute.md, track.md, conventions.md) and a set of deterministic bash scripts that read project state from .claude/ directory (PRDs, epics, tasks with frontmatter). The system does not contain any runtime code—it is purely a collection of instructions and helper scripts that an AI agent reads and executes. Its architecture is a flat skill structure following the agentskills.io standard, with no external dependencies beyond git, gh CLI, and a GitHub repository. The primary data flows are local file operations (creating/reading/updating markdown files) and GitHub API calls via gh CLI for issue creation, syncing, and closing. The skill is harness-agnostic and works with any compatible agent framework.
This repository is a curated knowledge base and skill package for academic paper writing, specifically targeting ML/CV/NLP-style research papers. It is not a software application or data pipeline; rather, it provides structured writing guides, templates, and examples organized as a reusable 'skill' for AI coding agents (Codex, Claude Code, Gemini). The core content includes section-specific writing guides (Abstract, Introduction, Method, Experiments, Conclusion), paragraph clarity checks, adversarial self-review checklists, and example banks with reusable sentence skeletons. The architecture is a static file collection with a main entry point (SKILL.md) that defines the workflow and references to sub-guides. There are no executable scripts, API endpoints, or data processing components. The repository is well-maintained with high community engagement (6549 stars) and demonstrates strong craftsmanship in organizing and presenting writing methodology, though it is not a software engineering artifact.
wechatDownload is a desktop GUI utility for bulk-downloading articles from WeChat official accounts (微信公众号). The exported artifacts in example/ prove a production pipeline that converts articles into HTML, MHTML, Markdown, PDF, DOCX, and CSV, while also pulling embedded images, videos (nested MP4s), audio, cover images, and reader comments, and recording social metrics (like_num, read_num, share_num, comment_count) per article into CSV files. The tool supports single-article, full-account batch, and collection-based downloads, plus keyword filtering — all driven from a local GUI (README documents version 4.7+ and Windows/macOS use).