4,922 boosters for "c" — open source, verified from GitHub, ready to install
A curated collection of .windsurfrules files that guide AI coding assistants in generating performant, well-architected code by establishing best practices for optimization, database tuning, caching, and concurrency. Developers using Windsurf/Cascade benefit from standardized coding patterns that reduce manual code review overhead.
"name": "evolving-lite", "description": "Self-evolving Claude Code system that learns from corrections, manages context, and improves every session", "name": "PrimeLine AI",
"name": "claude-model-router-hook", "description": "Automatic model switching for Claude Code based on prompt complexity. No API calls.", "homepage": "https://github.com/tzachbon/claude-model-router-hook",
"name": "claude-line-channel", "owner": { "name": "NYCU-Chung" }, "metadata": { "description": "LINE Messaging API channel plugin for Claude Code" },
"name": "claude-mem-lite", "description": "Persistent long-term memory for Claude Code via MCP — captures coding decisions, bugfixes, and context across sessions. Hybrid FTS5 + TF-IDF search with episode batching. Single SQLite DB, no external services. A lighter, lower-cost alternative to claude-me
Analyze skills using historical session data + static quality checks, output a diagnostic report with P0/P1/P2 prioritized fixes. Scores each skill on a 5-point composite scale across 8 dimensions. CSO (Claude/Agent Search Optimization) = writing skill descriptions so agents select the right skill a
A Windsurf rules collection that guides AI coding assistants in implementing data-driven UI testing patterns, helping developers separate test logic from test data for improved maintainability and scalability.
"description": "Production-ready Claude Code configuration with role-based workflows (PM→Lead→Designer→Dev→QA), safety hooks, 44 commands, 19 skills, 8 agents, 43 rules, 23 hook scripts, auto-learning pipeline, hook profiles, and multi-language coding standards", "name": "xiaobei930", "url": "https:
A .windsurfrules collection that guides AI coding assistants in implementing rigorous backtesting techniques for trading strategies, helping developers avoid common pitfalls like lookahead bias and overfitting. Useful for quants and trading engineers building reliable strategy validation systems.
Syncs OpenClaw framework, ClawHub, and Skills documentation into a local searchable mirror for the QMD memory backend, enabling AI agents to access fresh, precise references during coding tasks.
"name": "amplihack", "description": "AI-powered development framework with specialized agents and automated workflows for Claude Code", "name": "Microsoft Amplihack Team",
<!-- Bilingual skill: this SKILL.md is the English primary; the Chinese mirror is SKILL.zh.md. Each references/.md has a Chinese mirror references/.zh.md (same content, two languages). The .md files are authoritative for the agent; the .zh.md files are for human readers. -->
"name": "design-system-ops", "description": "Staff-level design system auditing, governance, documentation, validation, and communication — 39 skills, 4 agents, and 11 knowledge notes for the full design system lifecycle", "name": "Murphy Trueman"
"description": "iMessage integration for Claude Code - read messages, send replies, and run autonomous agents via iMessage", "name": "David Siegel", "email": "djsiegel@gmail.com"
An MCP server that enables AI systems to store and retrieve information using semantic memory with vector embeddings and pgvector. Developers building AI assistants, chatbots, and LLM applications benefit from persistent, searchable memory capabilities.
Generate a public-news-based information briefing for any subject (region / industry / policy issue / institution) and produce a single self-contained HTML file ready to paste into the 微信公众号 / WeChat Official Account editor. User says something like: Any descriptive request such as "make a briefing
Skill metadata (version, author, license, optional_mcps, fallback) lives in manifest.json and plugin.json. Claude Code only reads and from this frontmatter; extra keys are silently ignored, so they belong with
Automates upgrading the sunsama-api dependency and wrapping new API endpoints as MCP tools, helping developers keep their Sunsama integrations current and fully functional.
The user provides a sentence or paragraph that needs verification. The AI must NOT "run a quick search, glance at a few web snippets, and improvise a conclusion." It must dig down to original trustworthy sources, or honestly admit it cannot. Five scenarios are covered: 1. Basic truthfulness check: w
"name": "scrapbox-cosense", "description": "Scrapbox/Cosense integration - read, search, create, and edit wiki pages", "repository": "https://github.com/worldnine/scrapbox-cosense-mcp",
Heuristic scoring (no AI key configured).
FHIRy is a Python package that converts FHIR healthcare data into pandas DataFrames, enabling data scientists and healthcare developers to perform analytics, machine learning, and AI on standardized health records. It's ideal for researchers and engineers working with health data who need quick, programmatic access to structured clinical datasets.
<!-- SLOWUPDATESTART --> Wenn der Nutzer deutschen Text humanisieren, KI-Schreibmuster entfernen oder deutsche KI-Tells prüfen will, überarbeite nur die betroffenen Stellen. Bewahre Substanz, Register und belegbare Aussagen. Mache den Text nicht glatter als nötig. Nutze diesen Skill nicht für normal
Turn a folder of raw files into a Markdown vault that an LLM can grep, and then answer questions over that vault responsibly. source file, carrying retrieval frontmatter (abstract / tags / synonyms) + a
