Your AI development stack, curated

The best AI coding tools, MCP workflows, and Claude Code skills — organized for developers. From editor setup to production integrations.

Build your AI stack

Tools, MCP servers, and skills that work together — from editor to production.

AI Coding Tools
8+ tools indexed
Editor extensions, code completion, pair programming tools. Cursor, Windsurf, Copilot, and more.
MCP Servers
6+ MCP servers indexed
Connect your AI to GitHub, databases, browsers, search, and production infrastructure.
Claude Code Skills
6+ skills indexed
Reusable workflow modules for debugging, refactoring, code review, and planning.

MCP Servers

More →

piLoci MCP

piLoci MCP is a self-hosted memory server for AI agents that exposes project-scoped memory storage and retrieval through the Model Context Protocol. Built to run on Raspberry Pi 5, it provides semantic recall, project listing, and user identity tools. Teams connect Claude Desktop, Codex, and other MCP clients to share persistent context without sending memory data to cloud services.

Cloudflare MCP

Bridges AI agents to Cloudflare Workers, KV storage, R2 object storage, and D1 databases for edge deployment inspection and management. Agents can check Workers status, inspect KV namespaces, query D1 databases, and monitor R2 buckets directly from the coding environment.

Grafana MCP

Exposes Grafana dashboards, alerts, and time-series data to AI agents for reading application health and correlating incidents with code changes. Agents can query metrics, inspect alert rules, and fetch panel data for debugging. Supports Grafana Cloud and self-hosted instances.

Agent Protocol MCP

Implements the Agent Protocol standard enabling MCP clients to coordinate with external agent frameworks using shared task, step, and artifact schemas. Useful when composing multiple agents where one agent hands off work to specialized agents. Supports agent registration and state tracking.

Azure MCP

Connects AI agents to Azure resources including App Service, Cosmos DB, Key Vault, and Logic Apps for configuration inspection and diagnostic retrieval. Developers can query Azure settings and logs without Azure Portal. Uses Azure CLI credentials for authentication.

Slackbot MCP

Extends Slack integration with message drafting, channel management, and workflow trigger capabilities for proactive team notifications. Unlike read-only Slack MCP, this enables agents to send summaries, create channels, and initiate workflows on behalf of users. Uses Slackbot token.

Claude Code Skills

More →

Structured AI meeting notes

Converts raw meeting transcripts into structured, actionable notes with decision logs, assigned action items, and key context preserved for future AI retrieval. This skill bridges the gap between what was discussed in a meeting and what AI agents need to know when acting on outcomes days or weeks later.

Incident response

Structured process for handling production incidents from detection to resolution and post-mortem. Covers severity assessment using P0-P3 grading, team coordination with a designated incident commander, communication templates for stakeholders and users, and structured post-mortem requirements to drive organizational learning from every significant outage.

Production debugging

Diagnoses live production incidents using log triage, metric spike correlation, deploy window filtering, and safe reproduction steps without causing further disruption. Production debugging applies systematic debugging principles in a live environment where the cost of wrong actions is high and the ability to reproduce the issue is limited.

Safe dependency upgrades

A structured checklist for upgrading npm, pip, Cargo, or similar dependency managers without breaking production. This covers changelog analysis, semver risk assessment, lockfile handling, and smoke testing so that routine dependency updates do not become sources of production incidents.

RAG pipeline construction

Builds production-ready retrieval-augmented generation pipelines with deliberate chunking strategies, embedding model selection, vector store configuration, hybrid search blending, and reranking so agents answer from your documents with reduced hallucination and cited sources. This skill focuses on the engineering decisions that separate a working prototype from a production-quality RAG system.

Multi-agent handoff design

Designs clean handoff protocols between specialized agents so work passes between planner, coder, reviewer, and executor agents without losing context, creating circular dependencies, or introducing race conditions. Handoff design treats agent-to-agent communication as an API contract with versioning, error handling, and explicit acknowledgment requirements.

AI News

All news →