Everything AI Coding MCP Server

<div align="center"> <img src="assets/logo.png" alt="Everything AI Coding logo" width="600" /> <p><strong><!-- README_APPROX_COUNT:START -->44000<!-- README_APPROX_COUNT:END -->+ curated AI coding resources — browse, evaluate, install</strong><br/>MCP Servers · Skills · Rules · Prompts · Plugins</p> <p> <a href="https://github.com/zgsm-ai/everything-ai-coding/stargazers"><img src="https://img.shields.io/github/stars/zgsm-ai/everything-ai-coding?style=flat-square&color=4A90D9" alt="Stars" /></a> <a href="https://github.com/zgsm-ai/everything-ai-coding/blob/main/LICENSE"><img src="https://img.shields.io/github/license/zgsm-ai/everything-ai-coding?style=flat-square" alt="License" /></a> <a href="https://github.com/zgsm-ai/everything-ai-coding/commits/main"><img src="https://img.shields.io/github/last-commit/zgsm-ai/everything-ai-coding?style=flat-square" alt="Last Commit" /></a> <img src="https://img.shields.io/badge/resources-44000-2ECC71?style=flat-square" alt="Resources" /> </p> <p> <a href="./README.md"><strong>English</strong></a> · <a href="./README.zh-CN.md">简体中文</a> </p> <p> <a href="#knowledge-base">Knowledge Base</a> · <a href="https://zgsm-ai.github.io/everything-ai-coding/">Browse Catalog</a> · <a href="#data-sources--quality">Data Sources</a> · <a href="#coding-hub">Coding Hub</a> · <a href="#contributing">Contributing</a> </p> <img src="assets/title-card.jpg" alt="Everything AI Coding title card" width="900" /> </div>

Why Everything AI Coding?

AI coding agents are improving fast, but the ecosystem around them is still fragmented. Finding a reliable MCP server, reusable skill, practical rule set, or prompt collection usually means searching across multiple repositories and formats.

Everything AI Coding is a curated knowledge base that continuously collects, deduplicates, enriches, and scores resources from 9+ upstream sources. Every entry includes quality signals — LLM-scored coding relevance, documentation quality, specificity, plus health metrics like freshness and community popularity — so you can evaluate before you install. Browse right here on GitHub, explore interactively with the web catalog, or search and install with one command using the Coding Hub tool.

<a id="knowledge-base"></a>

📚 Knowledge Base

🔌 MCP Servers — <!-- README_COUNT_MCP:START -->5167<!-- README_COUNT_MCP:END --> entries

Model Context Protocol servers that connect AI agents to external tools, databases, and services.

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Name	⭐ Stars	Score	Description
Headroom	79.2k	98	AI context compression layer for agents, reducing token usage 60-95%…
upstash/context7	64.4k	98	An MCP server that injects real-time, version-specific library docume…
ChromeDevTools/chrome-devtools-mcp	55.0k	98	Official Google MCP server enabling AI coding agents to control Chrom…
DeusData/codebase-memory-mcp	48.4k	98	Code intelligence MCP server that indexes codebases into a persistent…
microsoft/playwright-mcp	39.4k	98	Official Microsoft MCP server enabling AI to control web browsers via…

<!-- README_TOP5_MCP:END -->

Browse all MCP servers → · Browse interactively →

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🎯 Skills — <!-- README_COUNT_SKILL:START -->26173<!-- README_COUNT_SKILL:END --> entries

Reusable agent capabilities and workflows for AI coding assistants.

<!-- README_TOP5_SKILL:START -->

Name	Source	Score	Description
bug-triage	github-trending	96	A detailed skill for triaging bugs in the ReverbCode repository, incl…
a-stock-data	github-trending	96	A comprehensive Python toolkit for A-share financial data covering 7…
acpx	github-trending	96	A headless CLI for orchestrating AI coding agents via ACP, managing s…
crabbox	github-trending	96	A CLI wrapper for orchestrating remote Linux validation across AWS, B…
make-latex-model	github-trending	96	A specialized AI skill for optimizing and aligning LaTeX templates in…

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Browse all skills → · Browse interactively →

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📋 Rules — <!-- README_COUNT_RULE:START -->245<!-- README_COUNT_RULE:END --> entries

Coding conventions and AI behavior guidelines for consistent development.

<!-- README_TOP5_RULE:START -->

Name	Source	Score	Category
python-checklist-py2-总规范	plugin-bundled-rule	95	backend
shell_checklist	plugin-bundled-rule	95	tooling
Go单元测试规范	plugin-bundled-rule	95	backend
单元测试规范	plugin-bundled-rule	95	tooling
python-checklist-py2-总规范	plugin-bundled-rule	95	backend

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Browse all rules → · Browse interactively →

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💡 Prompts — <!-- README_COUNT_PROMPT:START -->915<!-- README_COUNT_PROMPT:END --> entries

Developer-focused prompt templates for common coding tasks.

<!-- README_TOP5_PROMPT:START -->

Name	Source	Score	Category
Comprehensive repository analysis	prompts.chat	96	security
Frontend Developer Skill	prompts.chat	96	frontend
Backend Architect	prompts.chat	96	database
Frontend Developer	prompts.chat	96	frontend
Mobile App Builder	prompts.chat	96	frontend

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Browse all prompts → · Browse interactively →

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🧩 Plugins — <!-- README_COUNT_PLUGIN:START -->3372<!-- README_COUNT_PLUGIN:END --> entries

Bundled marketplace plugins (skills + commands + agents + MCP servers).

Primarily for Claude Code; opencode partially compatible (npm); cursor / windsurf / costrict have no equivalent mechanism.

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Name	Source	Score	Description
superpowers	Anthropic Official	100	Teaches Claude Code systematic development workflows including TDD, d…
hyperframes	Anthropic Official	100	HyperFrames lets developers write HTML/CSS/JS to create deterministic…
chrome-devtools-mcp	Anthropic Official	100	Chrome DevTools MCP server for AI coding agents to debug, automate, a…
claude-code-setup	Anthropic Official	100	A Claude Code plugin that analyzes codebases to recommend tailored au…
mcp-tunnels	Anthropic Official	100	A Claude plugin for connecting to private MCP servers via Anthropic t…

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Browse all plugins → · Browse interactively →

Private-deployment mirror: the plugin catalog is the upstream data source for costrict-plugin-marketplace, which bundles every verified plugin as bare git repos for customers running csc in air-gapped networks. See that project's README for the bundle format and the client import.sh flow.

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<a id="data-sources--quality"></a>

Data Sources & Quality

Everything AI Coding aggregates data from multiple upstream sources, then enriches, scores, and republishes the cleaned catalog.

Type	Sources
MCP	awesome-mcp-servers · Awesome-MCP-ZH · mcp.so · registry.modelcontextprotocol.io (official registry, active + isLatest, ~7,500 entries)
Skills	Tier 1: anthropics/skills · Ai-Agent-Skills · antigravity-awesome-skills · ai-agents-public · skills.sh (via mastra-ai/skills-api, install_count ≥ 1000)<br/>Tier 2: awesome-repo-configs · awesome-openclaw-skills · openclaw/skills<br/>Tier 3: catalog/skills/curated.json
Rules	awesome-cursorrules · rules-2.1-optimized · SchneiderSam/awesome-windsurfrules + balqaasem/awesome-windsurfrules (cross-repo dedup)
Prompts	prompts.chat · wonderful-prompts

Quality scoring

Every entry is scored on a 0–100 composite scale: final_score = LLM × 0.85 + health × 0.15.

LLM dimensions (1–5, up to 6 per type): coding relevance, doc completeness, description accuracy, writing quality, specificity, install clarity (MCP & Skills only)

Health signals: freshness (🟢 Active / 🟡 Stale / 🔴 Abandoned), popularity (GitHub stars), source trust (upstream reputation)

Decisions: accept (≥ 65) · review (50–64) · reject (< 50)

Each sub-directory README shows the Top 100 entries ranked by this composite score.

Pipeline

1. Sync — scripts/sync_*.py pull from upstream sources weekly
2. Merge — scripts/merge_index.py deduplicates across sources, merges metadata
3. Evaluate — Single LLM call: 6-dimension scoring + enrichment (tags, summary, tech_stack) + health signals
4. Download — scripts/download_catalog.py fetches per-entry files into catalog-download/ and reconciles catalog/index.json with what actually landed (docs)
5. Bundle — scripts/build_catalog_bundle.py packs index.json + catalog-download/ into dist/catalog-bundle.tar.gz for downstream consumers (docs)
6. Publish — GitHub Actions refresh the catalog, generate README tables, and update the web catalog

<details> <summary>Repository structure</summary>

everything-ai-coding/
├── install.sh / install.ps1      # One-command installers
├── catalog/                      # Generated catalog data
│   ├── index.json                # Full merged index (4000+ entries)
│   ├── search-index.json         # Lightweight search index
│   ├── mcp/                      # MCP servers — index + README
│   ├── skills/                   # Skills — index + README
│   ├── rules/                    # Rules — index + README
│   └── prompts/                  # Prompts — index + README
├── platforms/                    # Per-platform skill + command definitions
├── scripts/                      # Sync, merge, scoring, and generation scripts
└── .github/workflows/            # CI automation

</details>

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<a id="coding-hub"></a>

🛠 Coding Hub — Search & Install

Everything AI Coding also ships as a CLI tool that lets you search, browse, and install resources directly from your AI coding agent.

Quick Start

Install for your platform with one command:

macOS / Linux

# Costrict CLI (run from your project root)
curl -fsSL https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.sh | bash -s -- --platform costrict

# VSCode Costrict extension
curl -fsSL https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.sh | bash -s -- --platform vscode-costrict

# Claude Code
curl -fsSL https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.sh | bash -s -- --platform claude-code

# Opencode (run from your project root)
curl -fsSL https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.sh | bash -s -- --platform opencode

Windows (PowerShell)

# Costrict CLI
irm https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.ps1 | iex

# Specify the platform manually if auto-detection fails
& ([scriptblock]::Create((irm https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.ps1))) -Platform costrict

After installation, try a search command:

# Claude Code
/eac:search typescript

# Opencode / Costrict CLI / VSCode Costrict (Roo Code)
/eac-search typescript

<video src="https://github.com/user-attachments/assets/e58f0b08-73c0-4fba-ac95-138c8087a917" controls width="100%"></video>

You can also hand the installation off to another agent with this prompt:

You are an installation assistant. Open the following URL, read the "For Agents" section,
and follow it exactly to install Everything AI Coding for the platform you are currently running on.

Do not clone the repository. Only read this raw file:
https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/README.md

After installation, report which platform path was used and whether verification succeeded.

Platforms

Everything AI Coding supports four AI coding platforms. The resource catalog is shared, while command names and installation paths vary slightly by platform.

	Claude Code	Costrict	Opencode	VSCode Costrict (Roo Code)
Search	/eac:search <kw> [type:mcp]	/eac-search <kw> [type:mcp]	/eac-search <kw> [type:mcp]	/eac-search <kw> [type:mcp]
Browse	/eac:browse [category]	/eac-browse [category]	/eac-browse [category]	/eac-browse [category]
Recommend	/eac:recommend	/eac-recommend	/eac-recommend	/eac-recommend
Install	/eac:install <id>	/eac-install <id>	/eac-install <id>	/eac-install <id>
Uninstall	/eac:uninstall <id>	/eac-uninstall <id>	/eac-uninstall <id>	/eac-uninstall <id>
Update	/eac:update	/eac-update	/eac-update	/eac-update
Evo	/eac:evo <id>	/eac-evo <id>	/eac-evo <id>	/eac-evo <id>

<details> <summary>Platform path differences</summary>

	Claude Code	Costrict	VSCode Costrict	Opencode
Global skill path	~/.claude/skills/eac/	~/.costrict/skills/eac/	~/.costrict/skills/eac/	~/.opencode/skills/eac/
Command path	same global dir	.costrict/eac/commands/ (project)	~/.roo/commands/ (global)	.opencode/command/ (project)
Separator	:	-	-	-

• Costrict CLI installs command files per project, so run the installer in each repository root.
• VSCode Costrict / Roo Code installs command files globally under ~/.roo/commands/.
• Claude Code can load the full skill and subcommands from the global skills directory.
• Opencode installs commands per project under .opencode/command/.

</details>

<a id="for-agents"></a>

For Agents

If you are an AI agent installing Everything AI Coding automatically, follow this section exactly.

Step 1: Run the installer

The install scripts auto-detect the host platform when possible.

macOS / Linux

curl -fsSL https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.sh | bash

Windows (PowerShell)

irm https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.ps1 | iex

Auto-detection uses process-level environment variables that each platform injects at startup:

Environment variable	Platform
COSTRICT_CALLER=vscode	VSCode Costrict extension
COSTRICT_RUNNING=1	Costrict CLI
CLAUDECODE=1	Claude Code
OPENCODE=1	Opencode

If auto-detection fails, specify the platform manually.

macOS / Linux

curl -fsSL https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.sh | bash -s -- --platform <platform>

Windows (PowerShell)

& ([scriptblock]::Create((irm https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.ps1))) -Platform <platform>

Examples:

# Claude Code (macOS/Linux)
curl -fsSL https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.sh | bash -s -- --platform claude-code

# Opencode (run from the project root)
curl -fsSL https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.sh | bash -s -- --platform opencode

# Claude Code (Windows)
& ([scriptblock]::Create((irm https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/install.ps1))) -Platform claude-code

<details> <summary>Fallback: manual installation without <code>curl | bash</code></summary>

Run the platform-specific commands below if you need to download the files directly.

Costrict CLI (global skill + project commands)

mkdir -p ~/.costrict/skills/eac
curl -fsSL "https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/platforms/costrict/skills/eac/SKILL.md" -o ~/.costrict/skills/eac/SKILL.md
mkdir -p .costrict/eac/commands
for cmd in search browse recommend install uninstall update; do
  curl -fsSL "https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/platforms/costrict/commands/eac/eac-${cmd}.md" -o .costrict/eac/commands/eac-${cmd}.md
done

VSCode Costrict extension / Roo Code (global skill + global commands)

mkdir -p ~/.costrict/skills/eac
curl -fsSL "https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/platforms/vscode-costrict/skills/eac/SKILL.md" -o ~/.costrict/skills/eac/SKILL.md
mkdir -p ~/.roo/commands
for cmd in search browse recommend install uninstall update; do
  curl -fsSL "https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/platforms/vscode-costrict/commands/eac/eac-${cmd}.md" -o ~/.roo/commands/eac-${cmd}.md
done

Claude Code (global skill + global commands)

mkdir -p ~/.claude/skills/eac
curl -fsSL "https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/platforms/claude-code/skills/eac/SKILL.md" -o ~/.claude/skills/eac/SKILL.md
for cmd in search browse recommend install uninstall update; do
  curl -fsSL "https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/platforms/claude-code/commands/eac/${cmd}.md" -o ~/.claude/skills/eac/${cmd}.md
done

Opencode (global skill + project commands)

mkdir -p ~/.opencode/skills/eac
curl -fsSL "https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/platforms/opencode/skills/eac/SKILL.md" -o ~/.opencode/skills/eac/SKILL.md
mkdir -p .opencode/command
for cmd in search browse recommend install uninstall update; do
  curl -fsSL "https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/platforms/opencode/command/eac-${cmd}.md" -o .opencode/command/eac-${cmd}.md
done

</details>

Step 2: Verify the installation

Run a search command to confirm that the commands are available:

# Costrict CLI / Opencode
/eac-search typescript

# VSCode Costrict extension
Send this in the chat: "Search typescript with eac"

# Claude Code
/eac:search typescript

Expected result: a Markdown table of matching resources. If the command is missing, the installation did not land in the correct platform path.

Available commands

Command	Description
search <keyword> [type:mcp|skill|rule|prompt]	Search resources, optionally filtered by type
browse [category] [type:...]	Browse by category
recommend [type:...]	Recommend resources based on the current project stack
install <id>	Install a resource
uninstall <id>	Uninstall a resource
update	Pull the latest Everything AI Coding files
evo <id>	Evolve a locally installed skill / prompt / rule via a 7-dimension quality rubric (adapted from darwin-skill)

Data endpoints

Search, browse, and recommend operate on a lightweight search index. Installation fetches a per-entry API document.

Purpose	URL
Search index	https://zgsm-ai.github.io/everything-ai-coding/api/v1/search-index.json
Entry API	https://zgsm-ai.github.io/everything-ai-coding/api/v1/{type}/{id}.json
Type index	https://zgsm-ai.github.io/everything-ai-coding/api/v1/{type}/index.json
Full index fallback	https://raw.githubusercontent.com/zgsm-ai/everything-ai-coding/main/catalog/index.json

Why Costrict?

If you like the Everything AI Coding workflow, try Costrict — our broader AI coding agent platform built for stronger automation and team workflows.

Explore Costrict →

<a id="contributing"></a>

Contributing

Contributions are welcome. Open a PR against the appropriate directory under catalog/ and make sure the resource:

• is relevant to coding or AI-assisted development,
• has an accurate source_url, description, and tags,
• follows catalog/schema.json.

If you need maintainers' context instead of quick-start instructions, see the repository wiki in docs/wiki/.

Acknowledgments

The /eac:evo command's quality rubric is adapted from darwin-skill by 花叔 (MIT License) — an autonomous skill optimization system inspired by Karpathy's autoresearch. darwin-skill first systematized a "structure + effectiveness" dual-evaluation framework for SKILL.md files with a ratchet mechanism for keeping only measurable improvements. Everything AI Coding simplifies it for on-demand client-side use: the dynamic live-testing dimension and the ratchet are deferred; the core dimensions (workflow clarity, instruction specificity, edge-case coverage, checkpoint design, overall architecture, etc.) are preserved and rewritten for our on-demand evo context. See docs/wiki/evo-rubric.md for the full rubric specification.

Thanks to 花叔 for making the ideas and the original skill publicly available.

Disclaimer

Everything AI Coding is an index and installation helper for third-party open-source resources. Every MCP server, skill, rule, and prompt listed in the catalog remains the property of its original author.

This repository does not guarantee the safety, availability, accuracy, or compliance of third-party resources. Review source code and licenses before use, and open an issue if you find security or copyright problems.

Everything AI Coding is released under the MIT License.