Windsurf

Windsurf AI

Windsurf AI

Windsurf AI

Windsurf AI

Windsurf AI

Windsurf AI

Windsurf AI

Windsurf AI

Windsurf AI

Windsurf AI

We built a free CLI to keep CLAUDE.md, slash commands, MCP servers, and skills in sync across machines

I'm part of the three-person team behind gaal. G/ and Mickael hand-built the core in Go. I dogfood everything we ship. We built this because we hit two compounding pains. First: if you use Claude Code alongside any other coding agent (Cursor, Codex, Windsurf), same project means different rules filename per agent. Claude Code wants CLAUDE.md. Codex wants AGENTS.md. Cursor wants .cursorrules. Same MCP server, three different configs. Same skill, three install paths. Second: I run Claude Code on three machines (work MacBook, personal PC, personal desktop), so every one of those agent-specific configs has to live in three places. Multiply, and "config" turns into what can feel like a part-time job. So we built gaal: one declarative YAML that lives in your git repo. On each machine, git pull && gaal sync writes everything where each agent expects it. Free, open source (AGPL-3.0), no account, no server required to run it solo. GitHub: https://github.com/getgaal/gaal The Claude Code part is the content: block. Keep one file as your source of truth and gaal writes it where each agent expects: content: - source: ./project-rules targets: - agents: [claude-code] root: workspace paths: { rules.md: CLAUDE.md } - agents: [codex] root: workspace paths: { rules.md: AGENTS.md } - agents: [cursor] root: workspace paths: { rules.md: .cursorrules } One file in your project (rules.md, name it whatever you want). gaal renames it on the way out so each agent reads its own native filename. Same pattern handles your commands/ directory for slash commands, settings.json, hooks, MCP server entries (upserted into ~/.claude.json without clobbering anything you added by hand), and skill packages. gaal supports 21 agents total (Claude Code, Cursor, Codex, Windsurf, Cline, Continue, Goose, and 14 others). Claude Code is the one we use every day and the one that drove the design. How Claude Code shaped it: we use Claude Code daily and have for months. The content-routing feature came directly from CLAUDE.md drift between my machines being the most acute pain. The MCP merge logic came from an evening Mickael spent rebuilding a ~/.claude.json after a stray edit nuked his hand-added servers. The tool is real because the frustration was. We're not first in this space. chezmoi, skills-sync, agent-dotfiles, and rule-porter all overlap. And yes, you could do parts of this with a dotfiles repo plus a sync script, that's where we started, but you end up reinventing per-agent install paths, MCP JSON merges, and skill packaging. gaal is what we extracted after rebuilding that scripting one too many times. Where we landed differently: repos + skills + MCPs + content in one file, with a three-scope model (system / user / workspace, workspace wins) so a shared baseline can't stomp your project-level config. If you run Claude Code on more than one machine, or alongside another agent, how are you keeping your rules files and ~/.claude/ in sync today? Git? Symlinks? Just suffering? GitHub: https://github.com/getgaal/gaal Site: https://getgaal.com submitted by /u/gquizal [link] [comments]

VS Code extension that lets you switch AI agent harnesses/skills/prompts in one click (works with Claude Code, Github Copilot, Cursor, and Windsurf)

https://preview.redd.it/zhsn5dpxzj5h1.png?width=522&format=png&auto=webp&s=f026d25565ec88542849095125f927baf00f2638 I ended up maintaining a bunch of different harness markdown files for different projects based upon whether I was working with data or a side project. Swapping, downloading and copying entire folders is a 3-4 click process but still a bit annoying. So I built Harness Manager. It's a sidebar extension that lets you browse, install, and switch between pre-built harnesses in one click. If you work on multiple projects and have to make several repositories quickly, it is quite helpful. I've added tons of features! Most importantly, SECURITY! I scan each prompt within my own repository (I have provided the skill I use below and I PROMISE I at least skim over every markdown file with my own eyes. Centralized harness source: https://github.com/AdmiralGallade/harness-repository/tree/main/skills/scan-harnesses Please give me as much feedback as you can! I would love to improve this more! And if there are any harnesses you want me to add, just open a PR! You can of course use this with your own repository, just change the URL in the settings or import as a zip! I'll summarize the functions below using AI: What it does: Browse harnesses from a GitHub repository, grouped by category One-click install — copies files into agent-harnesses/ and immediately writes the right config files for whichever AI tool you use: Claude Code → .claude/CLAUDE.md GitHub Copilot → .github/copilot-instructions.md Cursor → .cursorrules + .cursor/rules/harness.mdc Windsurf → .windsurfrules + .windsurf/rules/harness.md Star harnesses to pin favourites, focus mode to hide everything else Full version history — every switch is backed up automatically, restore any previous state Import your own harnesses from a local folder or ZIP Multi-harness mode if you want several active at once Works in VS Code, Cursor, and Windsurf. It's free and open source. The harness repository it ships with is also public so you can add your own or fork it. 🛒 VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=AdmiralGallade.harness-manager 💻 GitHub: https://github.com/AdmiralGallade/vscode-harness-manager submitted by /u/EmotionallyReboot [link] [comments]

/design-sync Storybook source shape - what's new in CC 2.1.161 (+64 tokens) and CC 2.1.162 (+9,871 tokens)

System Prompt: Action safety and truthful reporting — Allows hard-to-reverse or outward-facing action approvals to persist across contexts when durable approval context is enabled, while preserving the stricter one-context approval rule otherwise. Tool Description: Agent (usage notes) — Updates agent usage guidance to key subagent-type instructions off subagent-type availability rather than message-continuation support, and scopes subagent-context restrictions to the actual subagent context check. Tool Description: Background monitor (streaming events) — Strengthens streaming-pipeline guidance so every pipe stage flushes per line, explicitly warns that head buffers until enough matches accumulate, and simplifies output-volume guidance around filtering to actionable success and failure signals. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.161 NEW: Skill: /design-sync package source shape — Adds package-based /design-sync instructions for React design systems without Storybook, covering .d.ts export discovery, deterministic config, build and validation commands, preview verification, upload, and troubleshooting. NEW: Skill: /design-sync Storybook source shape — Adds Storybook-based /design-sync instructions that build or use Storybook output, derive components and args from stories, preserve Storybook config paths, and share the validation, upload, and troubleshooting flow. Skill: /design-sync slash command — Refactors the main command around explicit source-shape detection, records shape and storybookConfigDir in design-sync.config.json, and delegates the detailed workflow to the new Storybook or package shape skill. Skill: /init CLAUDE.md and skill setup (new version) — Expands AI coding tool config discovery to include .devin/rules/ and .windsurf/rules/ alongside existing AGENTS, Cursor, Copilot, Windsurf, and Cline files. Tool Description: Bash (Git commit and PR creation instructions) — Adds a configurable note slot after common GitHub PR operations, allowing extra PR workflow guidance to be injected when available. Tool Description: DesignSync — Marks explicit asset registration and unregistration as legacy for /design-sync, explaining that preview cards are now indexed from @dsCard comments and that normal uploads only need finalize, write, and delete operations. Tool Description: LSP — Clarifies that workspaceSymbol searches symbols by query and instructs agents to always provide a query because many language servers return no results for an empty one. Tool Description: NotebookEdit — Reworks notebook editing guidance around cell IDs from prior Read output, requiring the notebook to be read before editing and changing insert behavior to add cells after a target cell or at the notebook start. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.162 submitted by /u/Dramatic_Squash_3502 [link] [comments]

We open-sourced a Four-Leaf MCP server + Claude Skill for job search and interview prep

We shipped a hosted MCP server today that brings our job-search and interview-prep stack into Claude (Desktop and Code), and any other MCP-aware client (Cursor, ChatGPT, Cline, Continue, Windsurf, Perplexity). Install in Claude Code: claude mcp add four-leaf --transport http https://four-leaf.ai/api/mcp More install details at: four-leaf.ai/oss What you get: search_jobs (180k+ active postings) get_role_intelligence (24 roles, interview pipeline + scoring rubric) generate_practice_questions match_score (resume vs JD) comp_benchmarks (cited salary bands via server-side web search) comp_coach (offer analysis + negotiation strategy) plus role catalog, interview format walk-throughs, curated question bank Two paid surfaces (voice mock interviews, AI resume tailoring) hand off to four-leaf.ai via deep-link. We also open-sourced an MIT Claude Skill wrapper at github.com/fourleafai/clover-public that turns Claude Code, Cursor, OpenAI Codex, or GitHub Copilot into a guided coach on top of the MCP. One command to install: npx four-leaf-coach add Architecture write-up: four-leaf.ai/blog/job-search-assistant-mcp Also launching on Product Hunt today if you're around: producthunt.com/products/four-leaf?launch=four-leaf-mcp submitted by /u/FourLeafAI [link] [comments]

My AI coding agent tried to touch files it should never touch. So I built a local guardrail.

AI coding agents are amazing until they touch the wrong file. I had agents delete files, inspect things they shouldn’t, and get way too confident around sensitive project data. So I built Phylax : a local safety layer that blocks risky file access before an AI agent touches your secrets. No login. No cloud. No telemetry. Just local rules for what agents can and cannot touch. I’m collecting real failure cases from developers using Cursor, Claude Code, Windsurf, Cline, OpenCode, etc. What’s the worst thing an AI coding agent has done in your project? submitted by /u/DumbbMoneyy [link] [comments]

naksha-studio v5 is out. It now remembers your project so you stop explaining your stack every session.

Been building naksha-studio for a while now. It's a plugin that gives you a virtual design agency as slash commands inside Claude Code, Cursor, Windsurf, and Gemini CLI. 62 commands, 26 specialist roles, all the design work you'd normally bounce between tools to do. The problem was every session started from zero. New chat, explain your stack again. Brand color, framework, grid system, WCAG level. Every time. v5 fixes that with project memory. What's new: /naksha-browse captures a live site through Playwright and stores the findings. Layout grid, type scale, color palette, UX patterns, all written to your project memory. Run it on your competitors or sites you admire and every future design command uses those as reference automatically. /naksha-remember persists design constraints. "Grid is 8px. No dark mode. WCAG AA required." Classified and stored. Never repeated again. Both write to .naksha/project.json and 5 existing commands (/design, /brand-kit, /design-system, /design-score, /accessibility-audit) read it automatically from that point on. The Stop hook also processes memory blocks written during a session so context you establish mid-conversation survives to the next one. GitHub: https://github.com/Adityaraj0421/naksha-studio If you were already using it, just git pull. No reinstall needed. Run /naksha-init to upgrade your existing project to the v5 schema. Feedback welcome, especially on whether the memory actually reduces your setup friction. submitted by /u/Known-Delay-9689 [link] [comments]

[Open Source] I built a full Git MCP server in Go that doesn't just wrap bash. It uses tree-sitter, handles real plumbing (write-tree), and runs 100% locally.

I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating giant, messy multi-feature commits. In-Memory Previews: The ⁠PREVIEW⁠ tool uses ⁠write-tree⁠ to snapshot the staging area into a ⁠job_id⁠. The working tree is never touched during the preview stage. ⁠APPLY⁠ then uses ⁠commit-tree⁠ + ⁠update-ref⁠ to seal the deal cleanly. Client & Backend Support 13 Clients Configured Automatically: Runs out of the box with ⁠git-courer mcp setup⁠ for Claude Code, Cursor, Windsurf, OpenCode, Cline, Roo Code, VS Code, Zed, Claude Desktop, Continue, and more. 100% Local-First: Works with any backend exposing an OpenAI-compatible ⁠/v1⁠ API (Ollama, LM Studio, llama.cpp). The project is fully open source. I’d love to hear your thoughts on the architecture, the plumbing pipeline, or any features you'd like to see added! Repo: github.com/Alejandro-M-P/git-courer submitted by /u/blakok14 [link] [comments]

Grateful to be accepted into Claude for Open Source Program

Just got the email from Anthropic. Claude Max 20x free for 6 months for open source maintainers. Really thankful for this. I have been building CodeBurn, a CLI that shows where your AI coding tokens go. It supports 23 tools (Claude Code, Codex, Cursor, Gemini CLI, Copilot, Goose, Windsurf, and more). Reads session data from disk. No API keys, no wrappers, nothing leaves your machine. It breaks down cost by model, project, and task type. Has a waste detector with copy-paste fixes and a head-to-head model comparison using your own data. With this support there is a lot more coming for the open source community. If you use AI coding tools, check it out: npx codeburn@latest GitHub: https://github.com/getagentseal/codeburn submitted by /u/MurkyFlan567 [link] [comments]

pg-mnemosyne-mcp – Give your Cursor & Claude Code assistants persistent PostgreSQL memory and task tracking

Hi everyone! I was tired of my AI coding agents losing context across different chats or stepping on each other's toes when running multi-agent sessions. So I built **pg-mnemosyne-mcp**, a high-performance Model Context Protocol (MCP) server for PostgreSQL. It does three things really well: **Persistent Super Memory**: Let's your AI store key-value memories with tags directly in a local or cloud Postgres DB. **Dynamic Task checklists**: Prompts a specialized task board for AI tracking. **Agent Coordination Hub**: If you run multiple agents (e.g. Claude Desktop and Cursor), they register their active files and tasks in a shared database to prevent merge conflicts. Setup is a single command: `pg-mnemosyne init --dsn "..."` (it auto-configures Claude Desktop, Cursor, Roo Code, Windsurf, Claude Code, and more). It's fully open-source! If this sounds useful to your workflow, I'd love for you to try it out or drop a ⭐ to support the project! 👉 **GitHub**: https://github.com/Janadasroor/pg-mnemosyne-mcp 👉 **PyPI**: https://pypi.org/project/pg-mnemosyne-mcp/ submitted by /u/janadasroor [link] [comments]

I built and open-sourced Skill Index to organize & standardize your AI agent knowledge across Claude, Codex, Cursor, and more. 100% local and free on macOS.

I’ve been using Claude alongside other coding agents, and I kept running into the same problem: useful skills, MCPs, commands, hooks, and workflows start getting scattered across different tools. Sometimes Claude has the best version of something. Sometimes Codex or Cursor does. Sometimes an MCP is configured in one agent but missing or slightly different in another. Over time, it gets harder to treat your agent knowledge as one reusable system. So I built Skill Index: a free, open-source, 100% local macOS app for organizing and standardizing AI agent knowledge. The goal is to make it easier to bring reusable skills/MCPs/agent knowledge into Claude, while also keeping Claude’s own knowledge portable across the rest of your setup. It can help you: - see where your skills and MCPs live - compare what Claude, Codex, Cursor, Windsurf, and other agents can access - standardize around a canonical definition of each skill/MCP - keep your skills and MCPs in sync across every agent It’s local-first: no accounts, no cloud sync, no telemetry. Website: https://skillindex.app GitHub: https://github.com/arjitj2/skillindex submitted by /u/CombinationOk2374 [link] [comments]

I built a tool that lets your AI assistant test your entire app in a real browser

So i've been working on this thing called Vibe Testing for a while now and finally putting it out there. Basically it's an MCP server that plugs into Claude Code, Cursor, Windsurf etc. you tell your AI assistant "test the login flow" and it actually does it, reads your source code to understand real selectors and routes, opens a real Playwright browser, clicks through stuff, takes screenshots, and tells you what broke. No test files to write or maintain. it figures out your framework, your routes, your forms from the codebase itself. it even remembers what worked and what was flaky between runs so it gets better over time. 12 tools total, scanning your codebase, exploring pages, executing test scenarios, generating reports, the whole thing. Setup is one command: npx vibe-testing@latest init it auto-detects your editors and configures everything. it's fully open source, would love feedback or contributions: https://github.com/AishwaryShrivastav/vibe-testing https://www.npmjs.com/package/vibe-testing submitted by /u/AishwaryShrivastava [link] [comments]

Glia – Local-first shared memory layer (SQLite-vec + FTS5 + Offline Knowledge Graph)

Hey everyone, I wanted to share a project I've been working on called Glia. It is a 100% offline, local-first RAG and memory layer designed to connect your AI web chats (Claude, ChatGPT, DeepSeek) with your local developer tools (Claude Code, Cursor, Windsurf) using a unified local database. I wanted something lightweight that did not require pulling heavy Docker containers or subscribing to third-party memory APIs. I settled on a Node.js + SQLite architecture running sqlite-vec (for 768-dim float32 embeddings) alongside SQLite FTS5 for hybrid search, powered completely by local Ollama instances. We just launched a live website that outlines the details and demonstrates the features in action: Website: https://glia-ai.vercel.app/ Codebase: https://github.com/Eshaan-Nair/Glia-AI Technical Stack & Features: Hybrid Search Retrieval: SQLite-vec (using nomic-embed-text locally) + FTS5 keyword prefix matching (porter stemmer). Surgical Sentence-level Trimming: Chunks are sliced into sentences. When a prompt is intercepted, only the exact matching sentences are pulled out of the vector store instead of the whole paragraph. It cuts LLM prompt bloat by ~90-95% in my benchmarks. Knowledge Graph Extraction: An offline task queue uses a local LLM (llama3.1:8b via Ollama) to extract entity triples (subject-relation-object). These are stored in a SQLite facts table (or Neo4j if you run the full Docker compose profile) and fused with the vector retrieval score. HyDE (Hypothetical Document Embeddings): Queries are pre-processed to generate a hypothetical answer, which is embedded together with the original query to bridge semantic gaps. Concurrency: Running SQLite in WAL (Write-Ahead Logging) mode allows the browser extension dashboard and active MCP sessions to read/write concurrently without locking. PII Redaction: Aggressive scrubbing of JWTs, API keys, emails, and IPs in the extension before data is saved. The extension works on Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. The MCP server runs out of the same backend database for your terminal agent or Cursor. You can set it up with a single command: npx glia-ai-setup Glia is completely open-source (MIT). If you like the local-first approach or want to contribute to the SQLite vector pipeline, PRs are very welcome, and a star on GitHub helps the project get discovered! I would appreciate any feedback on the SQLite hybrid search scaling, the scoring fusion algorithm (RAG pipeline details are in RAG_PIPELINE.md), or local graph extraction performance. submitted by /u/Better-Platypus-3420 [link] [comments]

We built a tool that installs frameworks like ComfyUI, Ollama, OpenWebUI etc on any cloud GPU in one command and saves your whole setup between sessions [R]

We kept running into the same problem every time we rented a GPU to run Ollama + OpenWebUI or ComfyUI, we'd spend the first 45 minutes reinstalling everything. Custom nodes, models, configs, all of it. Docker images went stale fast, different providers had different base images, and nothing was truly portable. We got sick of it and built swm. Here's what it does for ComfyUI users specifically: swm gpus -g a100 --max-price 2.00 --sort price shows you the cheapest available GPU across RunPod, Vast ai, Lambda, and 7 other providers in one view swm pod create — spins up an instance on whatever provider you pick swm setup install comfyui — installs ComfyUI on the pod From there the main thing is the workspace sync. Your entire setup custom nodes, models, outputs, configs lives in S3-compatible object storage (I use B2). When you're done you run swm pod down and it pushes everything, kills the instance, and next time you spin up on any provider you just pull and everything is exactly where you left it. No more reinstalling 15 custom nodes and redownloading checkpoints every session. We also built a lifecycle guard because we kept falling asleep mid-session and waking up to dumb bills. It watches GPU utilization and if nothing's happening for 30 minutes (configurable), it saves your workspace and terminates automatically. Has saved us more money than we want to admit lol. A few other things: Background auto-sync daemon pushes changes every 60 seconds so you don't have to remember to save Tar mode for huge workspaces with tons of small files packs everything into one S3 object instead of 600k individual uploads Also supports vLLM, Ollama, Open WebUI, SwarmUI, and Axolotl if you do more than SD Works with Cursor, Claude Code, Codex, Windsurf if you want your AI agent to manage GPU instances for you Free, open source, Apache 2.0. pipx install swm-gpu Site: https://swmgpu.com GitHub: https://github.com/swm-gpu/swm Would love feedback from anyone who rents GPUs. What's the most annoying part of your current workflow? We are also looking for contributors to the open source repo and suggestions on new frameworks/extensions to be included. Please share your thoughts submitted by /u/Tkpf18 [link] [comments]

I built SeeFlow – architecture diagrams that actually run, wired to your live app

Architecture diagrams rot. You spend an afternoon in Confluence, three months later it's wrong, and nobody updates it because there's no forcing function. https://preview.redd.it/l14h40ly3m1h1.png?width=2508&format=png&auto=webp&s=df60b2ba6da04fadf7e1039b9472a106ed163314 SeeFlow tries to fix that by making diagrams executable. It generates a flow canvas from your codebase, then wires each node to your actual running app. There's a Claude Code / Codex/ Cursor / Windsurf plugin that does the heavy lifting: /seeflow show me the shopping cart feature It also ships an MCP server so any MCP-aware editor can register and edit demos without leaving the IDE. Link to the site: https://seeflow.dev 100% Free/ MIT Open Source submitted by /u/mrtule [link] [comments]

I built SeeFlow - architecture diagrams that actually run, wired to your live app

Architecture diagrams rot. You spend an afternoon in Confluence, three months later it's wrong, and nobody updates it because there's no forcing function. https://preview.redd.it/9svmg8ih3m1h1.png?width=2508&format=png&auto=webp&s=0d06df1f82fd417ee9a45e504efd26628eaf33fd SeeFlow tries to fix that by making diagrams executable. It generates a flow canvas from your codebase, then wires each node to your actual running app. There's a Claude Code / Codex/ Cursor / Windsurf plugin that does the heavy lifting: /seeflow show me the shopping cart feature It also ships an MCP server so any MCP-aware editor can register and edit demos without leaving the IDE. Link to the site: https://seeflow.dev 100% Free/ MIT Open Source submitted by /u/mrtule [link] [comments]