everything-ai-coding

42000+ curated AI coding resources — browse, evaluate, install
MCP Servers · Skills · Rules · Prompts · Plugins

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Knowledge Base · Browse Catalog · Data Sources · Coding Hub · Contributing

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.

📚 Knowledge Base

🔌 MCP Servers — 5170 entries

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

Browse all MCP servers → · Browse interactively →

🎯 Skills — 24370 entries

Reusable agent capabilities and workflows for AI coding assistants.

Browse all skills → · Browse interactively →

📋 Rules — 245 entries

Coding conventions and AI behavior guidelines for consistent development.

Browse all rules → · Browse interactively →

💡 Prompts — 893 entries

Developer-focused prompt templates for common coding tasks.

Browse all prompts → · Browse interactively →

🧩 Plugins — 3369 entries

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

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

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.

Data Sources & Quality

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

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

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

🛠 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

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.

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:

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

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

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

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

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

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

Data endpoints

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

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 →

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.