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Curated

AI Builder Resources (201)

Hand-picked guides, repos, and courses for shipping with AI.

// showing 46 of 201 resources

taste-skillt

An agent skill that stops your AI generating generic, obviously-AI interfaces

Github

taste-skill

An agent skill that stops your AI generating generic, obviously-AI interfaces

AI coding agents converge hard on the same visual defaults: the same gradients, the same card layouts, the same spacing. taste-skill is a skill you load into your agent to push it away from those defaults and toward design decisions that look deliberate. Worth trying if everything you ship looks like it came out of the same template, because to a large extent it did.

pi-from-scratchp

A working coding agent built from zero in about 600 lines of TypeScript

Github

pi-from-scratch

A working coding agent built from zero in about 600 lines of TypeScript

A minimal agent harness written from scratch, small enough to read in one sitting. No framework, no abstraction layers, just the loop: prompt, tool call, result, repeat. The fastest way to stop treating your coding agent as magic. Once you have seen the whole loop in 600 lines, the behaviour of the big harnesses stops being mysterious and starts being debuggable.

nodetermn

Node-based, tmux-backed terminal manager for running coding agents in parallel

Github

nodeterm

Node-based, tmux-backed terminal manager for running coding agents in parallel

Running three or four coding agents at once quickly becomes an exercise in losing track of which terminal is doing what. nodeterm gives each agent session a node in a visual graph, backed by tmux, so parallel work stays legible. Useful the moment you stop running one agent at a time and start treating them as a small team you supervise.

TracelyT

Turns a failed agent trace into a regression test that blocks the PR

Github

Tracely

Turns a failed agent trace into a regression test that blocks the PR

Most agent eval suites test the failures you imagined. Tracely tests the ones that actually happened. It is trace-native CI/CD for AI agents: when an agent fails in production, that trace is captured and converted into a regression test that blocks the pull request next time. That inversion matters because the failure modes worth guarding against are rarely the ones you predicted at design time. If your agent already embarrassed you in front of a user, this is the tool that stops it happening twice.

Github

book-to-skill

Turn any technical book PDF into a Claude Code skill

book-to-skill converts a technical book PDF into a ready to load Claude Code skill, so the reference you never finished reading becomes something your agent can consult while it works. Point it at a PDF and get back a structured skill directory with the material chunked for retrieval. A neat way to turn a shelf of unread technical books into working context.

Github

sandboxd

Open-source, self-hosted AI app builder that works in isolated sandboxes

sandboxd is a self-hosted app builder where an agent constructs real applications inside isolated sandboxes rather than on your machine. It is the shape most hosted AI app builders should have had from the start: you keep the code, you keep the environment, and the agent gets a contained place to fail in. Run it on your own box and you are not renting your build pipeline from anyone.

Github

Clawk

Give coding agents a disposable Linux VM instead of your laptop

Clawk hands your coding agent a throwaway Linux VM to work in, which is the correct answer to the question every builder asks before approving a shell command. The agent gets a real machine with real tooling, your host filesystem and credentials stay out of reach, and when the task is done the VM goes away. Useful the moment you start running agents unattended.

Github

Mantis

Security review skills your AI coding agent can actually run

Mantis is a modular, stack-agnostic toolkit of security review skills for AI coding agents, open sourced by Google. Instead of hoping your agent remembers to think about injection, secrets handling or unsafe deserialization, you load the relevant skill and let it audit the code it just wrote. The skills are composable and framework independent, so they drop into Claude Code, Cursor or any harness that can load a skill directory.

Github

Strix

Open-source AI pentesting agent that finds and helps fix app vulnerabilities

Strix is an open-source AI agent that pentests your own application: it probes for vulnerabilities the way an attacker would, then reports what it found with enough context to actually fix it. For solo builders shipping AI-assisted code fast, it closes a gap that traditional linters and dependency scanners never covered. Why it earns a slot Runs real offensive-security workflows rather than pattern-matching source code, so it surfaces logic and auth flaws static tools miss. Reports come with reproduction steps and remediation guidance, not just a severity badge. Open source and self-run, which means your code and findings never leave your environment. Fits naturally after a vibe-coded sprint, where speed usually outruns security review. Use it responsibly Point it at systems you own or are explicitly authorized to test. Used that way it is a genuinely useful pre-launch gate — run it before you open signups, not after someone else does. Repo: github.com/usestrix/strix .

Github

Langfuse

Open-source tracing, evals, and prompt management for LLM apps

Langfuse is the observability layer most LLM apps are missing. It traces every call your app makes — prompts, completions, tool calls, latency, token cost — and gives you evals, datasets, and prompt management on top, so you can answer "why did it do that?" without re-running the whole thing by hand. What you get Tracing — nested spans across chains, agents, and tool calls, with cost and latency attached to each step. Evals — run model-based or custom scorers over datasets so prompt changes get measured instead of vibed. Prompt management — version prompts outside your codebase and roll them forward without a deploy. Integrations — OpenTelemetry, LangChain, LlamaIndex, LiteLLM, and the OpenAI SDK, so instrumenting is usually a few lines. Good for Anyone running an LLM feature in production, or anyone whose API bill is growing faster than their understanding of it. Self-host the whole thing or use the managed cloud; the core is MIT-adjacent open source and the project came out of YC W23. Repo: github.com/langfuse/langfuse .

Github

n8n-mcp

Describe an automation in English, get a working n8n workflow

n8n-mcp is an MCP server that lets Claude Code, Claude Desktop, Cursor, or Windsurf build n8n workflows for you. Instead of dragging nodes around a canvas and guessing at field names, you describe the automation you want and the agent wires up the actual nodes, credentials references, and connections. Why it matters The MCP exposes n8n’s full node catalog to the model, so it picks real nodes with real parameters instead of hallucinating a plausible-looking config. Works across the major agent clients — no lock-in to one editor. Pairs naturally with self-hosted n8n , so the automation and your data both stay on infrastructure you control. Great for the long tail of internal glue work: notifications, syncs, scheduled scrapes, webhook fan-out. Good for Builders who already run n8n and want to stop hand-assembling workflows, and for anyone who has an automation idea but not the patience to learn a node catalog first. Setup is a standard MCP server entry in your client config. Repo: github.com/czlonkowski/n8n-mcp .

Github

Firecrawl

Search, scrape, and crawl any site into clean LLM-ready markdown

Firecrawl is the context API for AI apps. Point it at a URL — or a whole domain — and it returns clean, LLM-ready markdown instead of the tag soup you would get from a raw fetch. It handles JavaScript rendering, pagination, rate limits, and retries so your RAG pipeline stops being 200 lines of Puppeteer glue and prayer. Why builders use it Scrape a single page, crawl an entire site, or run a search that returns full page content — one API for all three. Output is markdown or structured JSON, which means fewer tokens burned on HTML noise and better retrieval quality. Handles the annoying parts: dynamic JS pages, PDFs, proxies, and anti-bot friction. Open source and self-hostable, with a managed cloud tier when you would rather not run crawlers yourself. Good for Feeding agents real web context, building RAG over documentation, competitor monitoring, and any place you need the live web as a dependable input rather than a scraping side-quest. With 160k+ stars it has become the default answer to "how do I get the web into my model?" Get started at github.com/firecrawl/firecrawl .

Github

promptfoo

Test and red-team your prompts, agents, and RAG pipelines.

promptfoo lets you test your prompts, agents, and RAG systems with simple declarative configs — plus red-teaming and vulnerability scanning to catch failures before your users do. Compare GPT, Claude, Gemini, and DeepSeek side by side. It plugs into the command line and CI/CD, so evals run automatically on every change instead of living in someone’s head. Used by teams at OpenAI and Anthropic, it is one of the most practical ways to make agent quality measurable.

Github

caveman

A Claude Code skill that cuts ~65% of tokens by talking like a caveman.

caveman is a gloriously simple Claude Code skill with a serious payoff: it strips your prompts down to terse, caveman-style phrasing — "why use many token when few token do trick" — and cuts roughly 65% of token usage as a result. It is half meme, half real optimization. The underlying insight is sound: models do not need your politeness or filler to understand intent, and trimming it saves money on every call. Worth a look if your Claude Code bills are creeping up.

Github

browser-use

Let your AI agents browse and act on any website.

browser-use makes websites accessible to AI agents — it hands your agent a real browser so it can navigate, click, fill forms, and complete tasks online instead of being limited to APIs that may not exist. Built on Playwright with a clean Python interface, it has become one of the most popular ways to give agents hands on the open web. Ideal for automating research, data entry, and any workflow that lives inside a browser.

Github

dify

Build agentic workflows and RAG pipelines in one open-source workspace.

dify is an open-source platform for building agentic workflows and RAG pipelines with rich model and tool support, all in one collaborative workspace. It combines visual building with the escape hatch of custom code. You can deploy it on cloud, in a VPC, or fully self-hosted, so teams move from prototype to production without rebuilding the stack. If you are assembling agents with real tool use and retrieval, it is one of the most complete open options.

Github

agent-skills

Production-grade engineering skills for your AI coding agents.

agent-skills is Addy Osmani’s collection of production-grade engineering skills for AI coding agents — reusable, opinionated instructions that teach your agent how a senior engineer actually approaches a task, not just how to autocomplete. It works across Claude Code, Codex, Cursor and Antigravity, so you can drop battle-tested skills into whatever harness you already use. A strong starting point if you want your agent to write code that fits real engineering standards.

Github

chrome-devtools-mcp

Give your coding agent Chrome DevTools for live debugging.

chrome-devtools-mcp is an official MCP server that hands coding agents the full Chrome DevTools protocol — inspect the DOM, read console errors, trace network requests, and drive the page like a real browser session. It turns "the button doesn’t work" into something your agent can actually debug, live, instead of guessing from source. If you build web apps with an agent in the loop, this is one of the highest-leverage MCP servers to add.

Github

serena

A semantic MCP toolkit that turns your coding agent into an IDE.

serena is a powerful MCP toolkit that gives coding agents semantic retrieval and editing — essentially an IDE for your agent. Instead of blindly grepping and pasting whole files, the agent navigates code by symbols and structure. It plugs into Claude Code, Codex, and JetBrains via a language server, making edits more precise and token-efficient on large codebases. A strong pick for anyone whose agent struggles once a repo gets big.

Github

headroom

Compress tool outputs and RAG chunks to cut agent token usage.

headroom compresses tool outputs, logs, files, and RAG chunks before they ever reach the LLM. The claim: 20% fewer tokens for coding agents and 60–95% fewer for JSON — with the same answers. It ships as a library, a proxy, and an MCP server, so you can drop it into an existing agent stack without rewriting your pipeline. For anyone burning context window on noisy tool results, it is a cheap efficiency win.

Github

claude-mem

Persistent memory that carries context across every agent session.

claude-mem gives your coding agent a long-term memory. It captures everything your agent does during a session, compresses it with AI, and injects the relevant context back into future sessions — so you stop re-explaining your project every time you start fresh. It works across Claude Code, Codex, Gemini, Copilot, OpenCode and more, storing memories in a local SQLite + embeddings pipeline. If you live inside an agent all day, this closes the biggest gap in the workflow: continuity.

Github

Spec Kit

GitHub's toolkit for Spec-Driven Development with AI coding agents

Spec Kit is GitHub's official toolkit for Spec-Driven Development — a workflow where you write a clear specification first and let AI coding agents implement against it. It turns vague vibe-coding into a repeatable, reviewable process. The idea Rather than prompting your agent one feature at a time, you author a structured spec (a PRD-like artifact), and the agent plans, breaks down, and builds from it. That keeps large agent-built projects coherent instead of drifting. Spec-first workflow that scales beyond one-off prompts Works with Copilot and other coding agents Bridges product intent and generated code Get started on GitHub .

Github

Superpowers

An agentic skills framework and dev methodology for AI coding agents

Superpowers is an open-source agentic skills framework and software development methodology built for AI coding agents. It packages battle-tested workflows — brainstorming, subagent-driven development, and structured SDLC practices — into reusable skills your agent can invoke on demand. Why builders care Instead of re-prompting the same instructions every session, Superpowers lets you codify how your agent should think and work. It leans into subagent-driven development, where a lead agent delegates focused tasks to specialized subagents, keeping context clean and output high-quality. Reusable skills that encode proven engineering workflows Subagent-driven development to parallelize and isolate context A methodology, not just a prompt pack — designed to compound over time Explore the framework on GitHub .

Github

TokenScope

Open-source token profiler for AI coding agents — see where your context budget goes

TokenScope bills itself as the first open-source token profiler for AI coding agents. It analyzes session logs, detects repeated content and bloated tool results, and shows you exactly where your context window budget is being spent — with concrete fixes and estimates. For anyone running Claude Code, Codex, or similar agents at scale, context bloat is a silent cost driver. TokenScope turns that invisible spend into a profile you can act on, trimming waste and keeping long agent sessions coherent and cheaper.

Github

brain0

The black box for AI-written code: provenance, drift detection, and MCP memory for coding agents

brain0 is a Rust-based "black box" for AI-written code. It builds a passive decision graph that links every commit back to the agent prompts behind it, giving you provenance, drift detection, and a DLP audit of exactly what your coding agents read and produced. It also ships MCP memory for coding agents plus signed, evidence-driven risk scoring. If you are shipping code written by Claude, Codex, or other agents, brain0 is a local-first way to answer "which prompt produced this change, and can I trust it?" — a governance layer builders increasingly need.

Github

OpenScience

The open-source AI workbench for scientific research

OpenScience is an open-source, agent-driven workbench built for scientific research. It packages a CLI, an LLM-backed "co-scientist" agent, and a set of research tools into a single TypeScript/Bun project so researchers and builders can run reproducible, AI-assisted investigations from the terminal. For AI builders, it is a strong reference architecture for domain-specific agents: it shows how to wire an LLM agent to real research workflows, structure tool use, and keep an experiment loop transparent and inspectable rather than a black box.

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Godcoder

Local-first, open-source coding agent — bring your own LLM key

Godcoder is a local-first, open-source coding agent for your desktop built with Rust and Tauri. Bring your own LLM key; your code stays on your machine and only ever leaves to the model provider you choose. The agent builds and manages its own harness.

Github

ShipGenAI

50 production-ready GenAI SaaS apps you can brand and ship

ShipGenAI is an MIT-licensed collection of 50 production-ready generative-AI SaaS apps. Each comes with Stripe billing, Google OAuth, and one-click Vercel deploy — brand them, ship them, and keep 100% of the revenue.

Github

Hello-Agents

Build AI agent systems from scratch — a practical, production-grade tutorial

Hello-Agents is a comprehensive, hands-on tutorial that takes you from agent fundamentals to production-grade multi-agent applications. It covers LLM orchestration, RAG, LangChain patterns, and the architecture behind real-world agent systems.

Github

GITVERSE

Reverse-engineer any codebase into an AI-ready build prompt

GITVERSE analyzes any GitHub repository and reverse-engineers it into a structured build prompt: an architecture breakdown, an ASCII blueprint, and an AI-ready reconstruction prompt you can feed to Cursor, Claude, or any LLM to rebuild or learn from the project.

Github

blue-spec

A Security-Driven Hardening toolkit that helps AI agents harden the defenses that matter.

Blue Spec is a toolkit for Security-Driven Hardening, a defensive workflow that helps AI agents detect what a system actually does and harden the defenses that matter most. It brings spec-driven, blue-team thinking to AI-assisted development so security is designed in, not bolted on.

Github

ai-memory-vault

Turn an Obsidian vault into your AI's persistent, markdown-based working memory.

AI Memory Vault gives your AI a real, persistent memory using nothing but an Obsidian vault and markdown files. No vector database required. The open-source system plus templates turn your notes into durable working memory your coding agent can read and update across sessions.

Github

foreman

A Boris-style TUI that supervises headless Claude Code agents through a gated pipeline.

Foreman is an agentic orchestrator TUI that supervises headless Claude Code agents through a gated software-delivery pipeline, pointed at any repository. It brings structure and human oversight to autonomous coding runs, letting you watch, gate, and steer multiple agents from one terminal interface.

Github

recall

Durable, offline memory for Claude Code so you stop re-explaining your project.

Recall gives Claude Code durable memory that lives entirely on your machine. Instead of burning tokens re-explaining your project every session, Recall stores and summarizes context offline using TextRank, keeping your AI coding agent grounded across sessions with zero cloud dependency.

Github

Kage

Shadow any website into a single self-contained binary for offline viewing

Kage “shadows” any website into a single self-contained binary you can run offline. Instead of juggling scraped folders, caches, or archive formats, you get one portable executable that serves a faithful copy of the site — ideal for documentation, demos, or preserving a reference before it disappears. It is a neat reminder that not every problem needs a cloud service. For builders, Kage is a clean example of packaging a complete experience into a distributable binary, and a handy utility for keeping critical docs available even when you are offline.

Github

Third-Eye

Production-grade OSINT platform for situational awareness across intel domains

Third-Eye is a production-grade open-source intelligence (OSINT) platform that aggregates situational awareness across multiple intelligence domains into one interface. Rather than stitching together a dozen scripts and feeds, it gives security researchers and analysts a unified, deployable system. For builders working on monitoring, threat intelligence, or data-aggregation products, it is a useful architectural reference for how to structure multi-source intelligence pipelines responsibly. As always with OSINT tooling, use it only for authorized research and defensive purposes.

Github

PerfectPixel Studio

Generate character sprite sheets with 8 directions and 100+ actions from a prompt

PerfectPixel Studio is an AI-powered animation sprite studio that turns a single text prompt into full character sprite sheets — 8 directions and over 100 actions — ready to drop into a game engine. It is built with Wails, Go, and React, so it ships as a fast native desktop app rather than a browser tab. For indie game developers and vibe coders, this collapses one of the most time-consuming parts of 2D game art into a prompt-and-iterate loop. It is a strong example of how generative AI is moving from one-off image generation toward structured, production-ready asset pipelines.

Github

Ghostwork

Local AI worker that watches your screen and automates repetitive workflows

Ghostwork is a personal AI worker that runs entirely on your machine. Built on top of Screenpipe, it continuously watches your screen, learns the workflows you repeat, and automates the tedious parts — no cloud, no manual setup, and no data leaving your computer. What makes it interesting for builders is that it improves passively: the more you work, the more it understands your patterns and starts handling repetitive tasks without you changing how you operate. If you are exploring local-first AI agents or ambient automation, it is a clean reference for screen-aware, privacy-preserving tooling.

Github

arXiv Reader MCP

MCP server to search arXiv papers and extract full-text PDFs from your editor

An MCP server that connects Claude Code and other MCP-compatible clients directly to arXiv. Search papers, fetch metadata, and extract full-text PDFs without leaving your editor. Perfect for AI researchers and builders who want to quickly reference papers while coding. Ask your AI assistant to find relevant papers and it fetches them in context.

Github

AlignDev

Generate shared coding standards so Claude Code, Cursor, and Copilot write consistently

AlignDev is a frontend conventions generator for teams using multiple AI coding agents. It solves the problem of inconsistent code when different AI agents work on the same codebase by generating a shared SKILL.md that all agents follow. A 7-step wizard covers stack selection, UI styling (49 pre-built styles), state management, toolchain, directory structure, naming conventions, and design tokens. Supports Next.js, React, Vue, Nuxt, and SvelteKit with real-time npm version syncing. Built with Next.js 16, React 19, TypeScript, and Tailwind v4. Includes WCAG contrast ratio calculations and i18n support with LTR/RTL handling.

Github

AgentGraphed

Local-first analytics dashboard for Claude Code and Codex CLI sessions

AgentGraphed reads JSONL logs from Claude Code and Codex CLI and turns them into an interactive dashboard. See every session, every project, every dollar across your whole machine. Just run npx agentgraphed and open localhost:3737. Features timeline views, session browsing with chat-bubble UI, project tracking by AI usage, one-click resume for past sessions, 30-day usage charts with token counts and cost estimates across 2700+ models, and optional live quota monitoring from Anthropic. All data stays local in SQLite. No cloud account or API setup needed. Built with TypeScript and Next.js. MIT licensed.

Github

Rainman

Context-aware project memory for AI coding tools — zero LLM, zero tokens, runs locally

Rainman is a persistent memory system that helps AI coding assistants like Claude Code remember project context, lessons learned, and solutions across sessions. It uses no LLM tokens and runs entirely on your local machine with just Python 3.10+. Its smart retrieval system combines keyword matching, temporal recency using ACT-R power-law decay, category-based importance, and associative linking. Integrates via MCP server, Claude Code lifecycle hooks, or CLI. New memories automatically link when keyword overlap exceeds 25%. Born from a real debugging scenario where Claude Code spent hours on a workaround when a 516-line solution already existed in the codebase. Rainman ensures that never happens again.

Github

Axocoatl

Self-coordinating multi-agent runtime in Rust — persistent, local-first, zero telemetry

Axocoatl is a Rust-based runtime for building self-coordinating multi-agent systems. Agents activate when their dependencies complete — no central orchestrator needed. Think pheromone-based coordination, but for AI agents. It features persistent state that survives server restarts, per-agent token budgets to control LLM costs, and works with Ollama, OpenAI, Anthropic, Mistral, Gemini, and OpenRouter. Supports both MCP and A2A protocols. Apache 2.0 licensed. No Rust toolchain needed to install — just curl the install script and run.

Github

Nia Context-Augmentation Layer

Boosts coding agent performance 27% with knowledge bases

Open-source context-augmentation layer boosting coding agent performance by 27% using up-to-date knowledge bases. Ready to integrate with existing agent workflows.

Github

Real-time Recommendation Engine Tutorial

Step-by-step guide using LLMs + Graph DBs

Step-by-step tutorial for building real-time recommendation engines combining LLMs with Graph Databases. Covers product recommendations and personalization strategies.

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dspy-micro-agent

Minimal agent runtime with DSPy modules

Lightweight agent runtime featuring DSPy modules with CLI and FastAPI server. Perfect alternative to heavy frameworks - agents without the 500MB overhead.