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26 tools · 9 MCP · 12 tips

AI Developer Knowledge Base

Tools, configs, and architectures for developers building with AI agents. Bookmark this page.

AI Agent Tools

Below you will find carefully selected tools that help with context management, extending agent capabilities, and automating developer workflow.

Use-Case Stack

🤖

HumanLayer

.claude/ directory with agents and workflow for Claude Code

WorkflowFreemiumAny size8,800
MIT2025-01Trending
HumanLayer is a YC-backed startup building tools for AI agents. Their public repository (8.8k stars) contains a .claude/ directory that's a goldmine. Agents in .claude/agents/ - each has one role: • codebase-analyzer - analyzes code for the task, context, dependencies, impact of changes • codebase-locator - finds places in code, returns file:line format (e.g. app/services/payment_service.rb:127) • codebase-pattern-finder - finds patterns, ask "how do we handle errors?" - get examples from entire codebase • thoughts-analyzer and thoughts-locator - search thoughts/ directory as project knowledge base • web-search-researcher - searches the web when local knowledge isn't enough Commands in .claude/commands/ - here's the magic: • /create_plan - creates implementation plan. First uses agents for research, asks questions, gathers context. Plan lands in thoughts/shared/plans/. Linear tickets save to thoughts/shared/tickets/. Pauses after each phase waiting for confirmation. • /implement_plan - implements plan, each step has a commit. After automated verification pauses: "Phase N Complete - Ready for Manual Verification" • /validate_plan - checks if implementation matches the plan, compares git diff • /commit - commits without "Co-authored by AI". Has instruction: "NEVER add co-author information" • /founder_mode - for experimental features. Made a commit without a ticket? Creates Linear ticket, branch, cherry-pick, push, PR - entire flow after the fact • /research_codebase - documents code without suggesting changes. "DO NOT suggest improvements. ONLY describe what exists." • /ralph_impl - unsupervised loop: fetches highest priority SMALL/XS ticket from Linear, creates git worktree for isolation, implements, commits, creates PR and returns for next ticket More commands: /iterate_plan, /oneshot, /create_handoff, /resume_handoff, /create_worktree, /debug, /local_review, /linear, /ralph_plan, /ralph_research. Also _nt (no-thoughts) and _generic variants for projects without thoughts/ directory. How to adopt? Copy .claude/ to your project and restart Claude Code.

Features

  • 6 AI agents - each with one role (analyzer, locator, pattern-finder, thoughts, web-search)
  • 27+ slash commands with _nt and _generic variants
  • /create_plan - research → questions → plan with confirmation pauses
  • /implement_plan - phase by phase, each step = commit, pause for manual verification

Integrations

Claude CodeLinearGit WorktreesGitHub

Use Cases

  • • Standardizing Claude Code workflow in teams
  • • Automating cycle: Linear ticket → plan → code → PR
  • • Overnight development with /ralph_impl (agent works alone)
VisitGitHub
🧠

Agent OS 2.0

Operating system for AI coding agents

Context ManagementFreeAny size3,400
MIT
Agent OS is a free, open-source markdown file system that transforms AI agents from "confused interns" into productive developers. You define directory structure, naming conventions, tech stack, and testing rules - all in one place. The agent starts a session, reads the configuration, and immediately knows how to work with your project. The key value is built-in workflow commands: • shape-spec - agent ASKS about requirements instead of guessing • write-spec - creates formal specification with use cases • create-tasks - generates task list • implement-tasks - single agent executes tasks • orchestrate-tasks - multiple agents for complex features This is spec-driven development - the agent doesn't start writing code until it understands what and why. In teams, this eliminates the problem of each developer having their own prompts and chaos.

Features

  • Markdown file system for AI context
  • Built-in workflow commands (shape-spec, write-spec, create-tasks)
  • Spec-driven development
  • Multi-agent orchestration

Integrations

Claude CodeCursorCodexGeminiAny AI coding tool

Use Cases

  • • Standardizing AI work in development teams
  • • Onboarding new developers to projects
  • • Automating spec-driven development
VisitGitHub
⚡

Augment Code

Context Engine for development teams

Code AssistantPaidTeam
Augment Code is an AI platform for development teams with a unique Context Engine - a system that maintains current understanding of the entire tech stack: code, dependencies, architecture, and change history. Key components: • IDE Agents - agents integrated with VS Code and JetBrains, supporting task lists for complex work, automatic memory between sessions, converting prompts to pull requests • Code Review - automatic review that "thinks like a senior engineer" • CLI - full AI functionality available in terminal Integrations with Jira and Wiki mean the agent understands not just code, but also business context and team documentation. In tests on Elasticsearch (3.6M lines of Java), Augment agents outperformed Cursor and Claude Code: correctness +14.8%, completeness +18.2%.

Features

  • Context Engine - understands entire stack
  • Jira and Wiki integration
  • IDE Agents for VS Code and JetBrains
  • Automated Code Review

Integrations

VS CodeJetBrainsJiraConfluenceGitHubGitLab

Use Cases

  • • Teams working on large codebases
  • • Automating code review
  • • Integrating AI with existing workflow (Jira, Wiki)
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🔍

Glean

Enterprise AI for company knowledge

Enterprise SearchEnterpriseEnterprise
Glean is an enterprise AI platform that combines three key elements: AI assistant, automation agents, and intelligent search. Glean indexes over 100 business applications (Slack, Teams, GitHub, Salesforce, ServiceNow, Jira) and builds an "Enterprise Graph" - a context map containing all organizational knowledge. For developers this means: • Agent Builder - graphical constructor for building agents without coding • APIs and SDKs - ability to integrate Glean into any application • Model Hub - access to latest AI models Glean Agents can automate business processes, communication, and repetitive tasks. Unlike coding-only tools, Glean understands business context and documentation. ROI: return on investment in under 6 months, 93% employee adoption in 2 years, 20% reduction in IT queries.

Features

  • Indexing 100+ business applications
  • Enterprise Graph - company knowledge map
  • No-code Agent Builder
  • APIs and SDKs for integration

Integrations

SlackMicrosoft TeamsGitHubJiraSalesforceServiceNow+3

Use Cases

  • • Centralizing company knowledge
  • • Automating business processes
  • • Onboarding new employees
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🚀

Antigravity Awesome Skills

132 ready-to-use skills for AI agents

Skills LibraryFreeAny size
MIT
Antigravity Awesome Skills is a library of 132 ready-to-use skills for AI coding assistants. These are functionalities that extend AI agent capabilities. Universal SKILL.md format means skills work across all platforms without modification: • Claude Code (Anthropic CLI) • Gemini CLI (Google DeepMind) • Cursor • GitHub Copilot • Codex CLI (OpenAI) • Antigravity IDE Skill categories: • Cybersecurity (~50) - pentesting, ethical hacking, vulnerability testing • Development (~25) - TDD, debugging, React patterns, architecture • AI & LLM (~8) - prompt engineering, autonomous agent patterns • Autonomous Systems (~8) - planning, skill creator • Automation (~6) - GitHub automation, shell scripts Skills come from official Anthropic, Vercel Labs, and OpenAI repositories - these are battle-tested solutions, not amateur prompts.

Features

  • 132 ready-to-use skills
  • Universal SKILL.md format
  • Official skills from Anthropic, Vercel, OpenAI
  • Categories: Security, Development, AI, Automation

Integrations

Claude CodeGemini CLICursorGitHub CopilotCodex CLIAntigravity IDE+1

Use Cases

  • • Extending AI agent capabilities
  • • Security testing and pentesting
  • • Automating developer workflow
VisitGitHub
🔄

Ralph Wiggum

Automated iteration loop for Claude Code

WorkflowFreeAny size
Ralph Wiggum is an official Claude Code plugin that automates iterative AI development through persistent loops. The technique feeds prompts repeatedly to Claude until completion criteria are met. Core philosophy: rather than seeking perfection on the first attempt, the methodology leverages deterministic failures as learning data points. Key features: • Iterative development with self-referential prompts • Safety controls via --max-iterations parameter • Completion detection using --completion-promise • Compatible with Git worktrees for parallel development Real-world results: • 6 repositories generated at Y Combinator hackathon • $50K contract delivered for $297 in API costs • CURSED programming language developed over 3 months Ideal for well-defined tasks with clear success metrics, projects requiring refinement cycles, and greenfield development suitable for overnight automation.

Features

  • Automated iteration loops with safety controls
  • Completion detection via text matching
  • Git worktrees compatibility
  • Claude Code ecosystem integration

Integrations

Claude CodeGit WorktreesTest FrameworksLinters

Use Cases

  • • Automating repetitive developer tasks
  • • Overnight greenfield project development
  • • Iterative code refinement with tests
VisitGitHub
MCP
📚

Context7

Up-to-date documentation for LLMs and AI editors

Context ManagementFreemiumAny size
Apache-2.0Trending
Context7 is an MCP (Model Context Protocol) server from Upstash that delivers current, version-specific documentation directly into your prompt. The problem it solves: LLMs rely on outdated training data, leading to "hallucinated APIs that don't even exist" and obsolete code examples. How it works: 1. Write your coding prompt naturally 2. Append "use context7" to trigger documentation retrieval 3. Receive code solutions using current APIs Example: "Create a Next.js middleware validating JWT cookies. use context7" Key capabilities: • Library search and discovery • Context extraction from current documentation • Version-specific code examples • Private repository integration (premium tier) Free tier available, premium unlocks higher API limits and private repository support.

Features

  • Current documentation directly in your prompt
  • MCP server compatible with multiple clients
  • Library search and discovery
  • Version-specific code examples

Integrations

Claude CodeCursorOpenCodeAny MCP-compatible client

Use Cases

  • • Eliminating API hallucinations in AI-generated code
  • • Working with latest framework versions
  • • Rapid prototyping with current documentation
VisitGitHub
MCP
📦

Repomix

Packs entire codebase into single AI-friendly file

Context ManagementFreeAny size
MITTrending
Repomix is an open-source CLI tool that converts entire code repositories into single files optimized for AI models. Packages code into XML, Markdown, JSON, or plain text formats - perfect for working with Claude, ChatGPT, and Gemini. Key features: • Git-aware - automatically respects .gitignore • Secret detection - built-in Secretlint for security • Token counting - per-file and repository-wide metrics • Code compression - Tree-sitter extracts signatures, ~70% token reduction • Remote repositories - process GitHub repos directly via URL Use cases: • Code reviews and refactoring analysis • Bug investigation with full context • Implementation planning • Security audits of third-party libraries Nominated for JSNation Open Source Awards 2025 in "Powered by AI" category.

Features

  • Convert repos to XML, Markdown, JSON, plain text
  • Automatic .gitignore respect
  • Secret detection via Secretlint
  • Code compression with Tree-sitter (~70% fewer tokens)

Integrations

ClaudeChatGPTGeminiGitHub ActionsDockerMCP Server

Use Cases

  • • Full codebase context for AI code review
  • • Debugging with entire repository as context
  • • Planning implementation of new features
VisitGitHub
📖

Google Code Wiki

AI-generated documentation that evolves with your code

Code AssistantFreemiumAny size
Google Code Wiki is a platform powered by Gemini AI that automatically generates and maintains comprehensive, always-up-to-date documentation for code repositories. The problem it solves: Developers spend 30-40% of their time understanding code they didn't write - compounded by poor, outdated, or missing documentation. How it works: • Architecture diagrams, class relationships, and sequence flows generated automatically • Documentation regenerated after each COMMIT - eliminates "documentation drift" • Built-in Gemini chat agent answers questions using current wiki as context Benefits: • New contributors can commit on Day 1 • Senior developers understand new libraries in minutes, not days • SREs and auditors get instant access to annotated code Currently in public preview - free for public repositories. Private repo pricing TBD. Gemini CLI extension in development for teams with legacy codebases.

Features

  • Automatic documentation generation by Gemini AI
  • Architecture and class relationship diagrams
  • Documentation updates after every commit
  • Built-in chat agent for code questions

Integrations

GitHubGemini AIGemini CLI (coming soon)

Use Cases

  • • Fast onboarding of new team members
  • • Understanding legacy codebase without tribal knowledge
  • • Maintaining up-to-date documentation effortlessly
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🎙️

SuperWhisper

AI-powered dictation for developers

Voice InputFreemiumSolo developer
SuperWhisper is a macOS dictation tool running OpenAI's Whisper model locally (whisper.cpp on Apple Silicon). • Mixed-language recognition — native language + English tech terms in one sentence • Custom dictionary for technical terms • Works system-wide in any application • Modes: Voice to Text, Super Mode (LLM-processed), custom • Pricing: $8.49/mo or $249 lifetime

Features

  • Local Whisper model on Apple Silicon
  • Mixed-language recognition (native + English tech terms)
  • Custom dictionary for technical terms
  • System-wide — works in any app

Integrations

macOSApple SiliconAny application

Use Cases

  • • Dictating long AI agent prompts (1800+ chars)
  • • Mixed-language coding conversations
  • • Hands-free coding workflow
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🤖

Clawdbot

Private AI assistant on your own server

AI AssistantFreeSolo developer
MIT
Clawdbot is a private AI assistant you deploy on any VPS (AWS, DigitalOcean). • 50+ integrations (Gmail, Calendar, Telegram, Slack, Discord, WhatsApp) • Persistent memory that accumulates context over time • Runs 24/7 even when your laptop is off • Data stays on your server • Config via USER.md and MEMORY.md

Features

  • 50+ integrations (Gmail, Calendar, Telegram, Slack, Discord, WhatsApp)
  • Persistent memory across conversations
  • Full data control on your server
  • Runs 24/7 even when laptop is off

Integrations

GmailGoogle CalendarTelegramSlackDiscordWhatsApp+1

Use Cases

  • • Private AI assistant available via messaging apps
  • • Automated email/calendar management
  • • Data-sovereign AI that never leaves your server
VisitGitHub
🐝

Claude-Flow

60 specialized AI agents in one system

WorkflowFreeTeam
MIT
Claude-Flow is a swarm system with 60 specialized AI agents. • Agents: developer, reviewer, tester, security, architect, tech writer • Swarm model — parallel task execution • Learns user preferences over time • After a month it knows you prefer Rails, write tests first, and want docs in a specific language • Multi-stage code verification

Features

  • 60 specialized agents (dev, review, test, security, architect, docs)
  • Swarm model — parallel execution
  • Learns your preferences over time
  • Multi-stage code verification

Integrations

Claude CodeGitAny test framework

Use Cases

  • • Full development cycle automation
  • • Parallel code review + testing + security audit
  • • Team-scale AI for solo developers
VisitGitHub
💉

Skills

npm for AI agents — instant abilities

Skills LibraryFreeAny size11,500
Skills is a repository of 11,500+ ready-to-use abilities for AI agents. Created by Jakub Mrugalski. • One command install: `npx skills add owner/repo` • Top: vercel-react-best-practices (38.9k installs), web-design-guidelines (29.5k), remotion-best-practices (20.4k) • Covers security, design, databases, optimization • Works with any AI coding agent

Features

  • 11,500+ ready-to-use skills
  • One command install: npx skills add owner/repo
  • Top skill: vercel-react-best-practices (38.9k installs)
  • Covers security, design, databases, optimization

Integrations

Claude CodeCursorCodexAny AI coding tool

Use Cases

  • • Instantly teach your AI agent best practices
  • • Standardize skills across team agents
  • • Add domain expertise without writing prompts
VisitGitHub
🖥️

Codex App

Native macOS app for AI-assisted coding by OpenAI

Code AssistantPaidAny size
Codex App is a native macOS application from OpenAI (released Feb 2026). • Git worktrees — different agents work on same repo without conflicts • Automations — background tasks on schedule • Skills extension system • Better at debugging than Claude Code (community reports), but slower • Native macOS experience

Features

  • Git worktrees — agents work on same repo without conflicts
  • Background automations on schedule
  • Skills extension system
  • Strong debugging capabilities

Integrations

macOSGitOpenAI API

Use Cases

  • • Multi-agent parallel development
  • • Scheduled background tasks
  • • Complex debugging sessions
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🔬

Google Antigravity

Next.js performance diagnostics with AI

Code AssistantFreeAny size
Google Antigravity combines IDE + browser + performance analysis in one interface. • Full diagnosis in 5 minutes with ready-to-apply fixes • Example: found Edge Runtime incompatible libraries in middleware (jsonwebtoken → jose), fixed 404 cascading • Result: startup 10s → 3.2s, page load "forever" → 200-500ms

Features

  • IDE + browser + perf analysis in one interface
  • Full diagnosis in 5 minutes
  • Ready-to-apply code fixes
  • Edge Runtime compatibility detection

Integrations

Next.jsChrome DevToolsVS Code

Use Cases

  • • Next.js performance optimization
  • • Debugging slow page loads and server startup
  • • Finding Edge Runtime incompatibilities
Visit
📈

SEO Machine

AI agents for content marketing automation

SEO & ContentFreeAny size
MIT
SEO Machine is an open-source tool for full content workflow via terminal commands. • /research — keyword research + competitive analysis from GA4, GSC, DataForSEO • /write + /optimize — content creation aligned with brand_voice.md • /analyze-existing — published content audit • Brand context defined in brand_voice.md

Features

  • /research — keyword research + competitive analysis
  • /write + /optimize — brand-aligned content creation
  • /analyze-existing — published content audit
  • Context via brand_voice.md

Integrations

Google Analytics 4Google Search ConsoleDataForSEOClaude Code

Use Cases

  • • Automated keyword research and content planning
  • • SEO-optimized content generation
  • • Existing content performance audit
VisitGitHub
MCP
🧠

claude-mem

Memory for AI agents — SQLite + Vector DB

MemoryFreeAny size
MIT
claude-mem replaces static markdown memory with a hybrid database. • SQLite for structured data (sessions, observations) + Chroma Vector DB for semantic search • Auto-logs 5 key moments per session • Categories: decision, bugfix, feature, refactor, discovery • Web UI at localhost:37777 • `` tag for sensitive data exclusion

Features

  • Hybrid: SQLite (structured) + Chroma Vector DB (semantic)
  • Auto-logs 5 key moments per session
  • Semantic search — finds by meaning, not exact words
  • Categories: decision, bugfix, feature, refactor, discovery

Integrations

Claude CodeSQLiteChroma Vector DB

Use Cases

  • • Persistent agent memory across sessions
  • • Semantic project history search
  • • Auto-documenting development sessions
VisitGitHub
MCP
📂

GitMCP

Any GitHub repo as documentation source

DocumentationFreeAny size
MIT
GitMCP turns any public GitHub repository into an MCP documentation source. • Useful for less popular libraries not indexed in Context7 • Agent reads repo content as structured documentation • MCP server integration • Automatic content extraction and indexing

Features

  • Any public GitHub repo as docs source
  • MCP server integration
  • Works with libraries not in Context7
  • Automatic content extraction and indexing

Integrations

GitHubClaude CodeAny MCP client

Use Cases

  • • Documentation for niche/new libraries
  • • Supplement to Context7 for unlisted libs
  • • Quick reference from any GitHub project
VisitGitHub
MCP
🔒

Ref.tools

Private repository and PDF documentation

DocumentationFreemiumTeam
Ref.tools is a paid ($9/mo) documentation tool for private repos and PDFs. • Private repository support • PDF document indexing • MCP server integration • Supplements Context7 and GitMCP for internal/proprietary codebases

Features

  • Private repository support
  • PDF document indexing
  • MCP server integration
  • Works with proprietary codebases

Integrations

Claude CodePrivate Git reposPDF files

Use Cases

  • • Private codebase documentation for AI
  • • Internal PDF docs accessible to agents
  • • Enterprise documentation integration
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🎯

Shotgun CLI

Automated codebase analysis and AGENTS.md generation

Context ManagementPaidAny size
Shotgun CLI is a tool that indexes entire codebase locally using tree-sitter. • Builds graph of classes, functions, dependencies, and patterns • Generates AGENTS.md — complete project spec for Cursor or Claude Code • Modes: Planning, Drafting, Multi-agent, Workspace sharing • Warsaw-based team

Features

  • Tree-sitter based local codebase indexing
  • Generates AGENTS.md project specification
  • Dependency graph: classes, functions, patterns
  • Modes: Planning, Drafting, Multi-agent, Workspace sharing

Integrations

Claude CodeCursortree-sitterGit

Use Cases

  • • Auto-generate project context for AI agents
  • • Onboarding AI to legacy codebase
  • • Multi-agent workspace coordination
VisitGitHub
MCP
🔎

QMD

100% free, local AI-powered search engine for your knowledge base

DocumentationFreeAny size
MIT
QMD is a local AI search engine for your notes, meeting transcripts, docs — anything you need to remember. Created by Tobias Lütke (Shopify CEO). • 3 search modes: BM25 keyword, vector semantic, and hybrid combining both with LLM re-ranking • Hybrid pipeline: query expansion → parallel BM25 + vector retrieval → Reciprocal Rank Fusion → re-ranking via local Qwen3 model • Models run locally via node-llama-cpp (GGUF) — zero API keys, zero cost • Built-in MCP server — your agent can search your knowledge base as part of any workflow • 100% local — no data ever leaves your machine

Features

  • BM25 keyword search — fast full-text retrieval
  • Vector semantic search — finds content even when you can't remember the words
  • Hybrid mode — BM25 + vector + Reciprocal Rank Fusion + Qwen3 re-ranking
  • Local models via node-llama-cpp (GGUF) — zero API keys

Integrations

Claude CodeClaude DesktopAny MCP clientSQLitenode-llama-cpp

Use Cases

  • • Searching notes, meeting transcripts, and project docs
  • • Private RAG without external APIs
  • • Semantic search across personal knowledge base
VisitGitHub
🎙️

Handy

Local speech-to-text — speak and paste into any text field

Voice InputFreeAny size14,900
MITTrending
Handy is a free, open source desktop app for speech-to-text transcription. Runs 100% offline — your voice never leaves your machine. • Keyboard shortcut → speak → release → text pasted into active field • Whisper (GPU) or Parakeet V3 (CPU with automatic language detection) • Voice Activity Detection (Silero) — filters silence • Built with Tauri (Rust + React/TypeScript) • 14.9k GitHub stars, MIT license

Features

  • Push-to-talk with configurable keyboard shortcut
  • Whisper (GPU) or Parakeet V3 (CPU) model options
  • Voice Activity Detection — filters silence automatically
  • 100% offline — data never leaves your machine

Integrations

Any desktop appmacOSWindowsLinux

Use Cases

  • • Dictating code and comments hands-free
  • • Quickly speaking prompts to AI agents
  • • Real-time meeting notes transcription
VisitGitHub
🔥

Fireflies.ai

AI notetaker — automatic meeting transcription and summaries

Meeting AIFreemiumTeam
Fireflies.ai is an AI meeting assistant. Connect to Google Calendar, the bot auto-joins every call, records, transcribes, and generates summaries with action items. • Auto-joins meetings (Google Meet, Zoom, Teams, Webex) • 95% accuracy transcription in 100+ languages • After each call you get: summary, action items, full transcript • AskFred — ask AI about any meeting content • Integrates with 200+ apps: Slack, Jira, HubSpot, Salesforce, Asana

Features

  • Auto-join meetings from Google Calendar
  • 95% accuracy transcription in 100+ languages
  • AI summaries with action items after every call
  • AskFred — search meeting content with questions

Integrations

Google MeetZoomTeamsWebexSlackJira+3

Use Cases

  • • No more "what did we agree on that call?" — open the link and get the exact quote
  • • PM automatically gets action items after every meeting
  • • Retrospective from 3 weeks ago? Full transcript with exact quotes
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MCP
🎨

Figma MCP Server

Official Figma MCP server — AI sees your designs

Design → CodeFreeTeam
Official MCP server from Figma. Developer opens Claude Code, AI SEES the design — frames, components, spacing, colors — and generates code that matches the mockup. • AI reads frames, components, design tokens, layout directly from Figma • Two modes: select element in Figma or paste a frame link • Generates code matching the mockup — no screenshots, no guessing • Works with Claude Code, Cursor, Windsurf, GitHub Copilot • One-command setup: `claude mcp add figma https://mcp.figma.com/mcp`

Features

  • AI reads frames, components, spacing, colors from Figma
  • Select element in Figma or paste a frame link
  • Generates code matching the mockup
  • Works with Claude Code, Cursor, Windsurf, Copilot

Integrations

FigmaClaude CodeCursorWindsurfGitHub Copilot

Use Cases

  • • Designer creates mockup → developer gets code without screenshots
  • • No more "this was supposed to be 16px not 24px" on code review
  • • Automatic extraction of design tokens (colors, typography, spacing)
Visit
MCP
📋

Atlassian MCP (Jira + Confluence)

Official Atlassian MCP server — AI reads and writes to Jira and Confluence

WorkflowFreeTeam
Official remote MCP server from Atlassian. AI reads and writes directly to Jira and Confluence — developer opens Claude Code, agent pulls task content from Jira and starts coding. • AI reads Jira tickets: title, description, comments, status, priority • Creates and updates tickets, adds comments, bulk create • Reads and creates Confluence pages — documentation in context • Zero copy-paste between tracker and IDE • Works with Claude Desktop and Claude Code

Features

  • Reads Jira tickets: title, description, comments, status
  • Creates and updates tickets, bulk create
  • Reads and creates Confluence pages
  • Zero copy-paste between tracker and IDE

Integrations

JiraConfluenceClaude CodeClaude Desktop

Use Cases

  • • Developer opens Claude Code → agent pulls task content from Jira
  • • PM creates tickets via AI — no clicking through forms
  • • AI generates Confluence documentation from code
Visit
MCP
⚡

Linear

Modern project tracker with official MCP server

WorkflowFreemiumTeam
Linear is a product management and issue tracking system. Fast as a native app, clean UX, built for dev teams. • Issues, Projects, Cycles (sprints) — full PM workflow • Automated workflows: auto-close on PR merge, auto-assign, triage • Official MCP server — AI agent reads and creates issues directly from IDE • Integrations: GitHub, GitLab, Slack, Figma • Roadmap planning with milestones and deadlines • Free tier up to 250 issues, paid from $8/user/month

Features

  • Issues, Projects, Cycles (sprints)
  • Automated workflows: auto-close, auto-assign, triage
  • Official MCP server — AI reads and creates issues from IDE
  • Integrations: GitHub, GitLab, Slack, Figma

Integrations

GitHubGitLabSlackFigmaClaude CodeCursor+2

Use Cases

  • • Developer opens Claude Code → agent pulls task from Linear
  • • Replacing Jira for teams that value speed and clean UX
  • • PM creates issues via AI — no clicking through forms
Visit
🏗️

Reference Architectures

How these tools connect in real workflows

🔄

The Autonomous Dev Loop

From ticket to Pull Request — full cycle automation

📋Linear Ticket
🤖HumanLayer /create_plan
⚡Claude Code
✅/validate_plan
🚀GitHub PR
🧠

Context-First Architecture

Maximum context for AI agents — zero hallucinations

📦Repomix
📚Context7
⚡Claude Code
🎯Shotgun CLI
🧠Agent OS
🔒

Local-First Privacy Stack

Full AI workflow without cloud dependencies

🔎QMD
🎙️SuperWhisper
🤖Clawdbot
🧠claude-mem

Quick Tips

TL;DR — the essentials in one line

📝

AI without documentation = junior without onboarding

🧹

Clean ~/.claude/ regularly — leftover files eat context

📚

Use Context7 for new libraries, never let AI Google docs

context7
🔌

More MCP servers ≠ better AI. Only keep what you actively use

🔒

Dedicated AI account — if compromised, blast radius is limited

🌙

End-of-day agents: last 30 min → agents prep context for tomorrow

💰

50,000 tokens Googling docs → 500 tokens via Context7. Same result

context7
🏗️

Convention-based frameworks (Rails, Django) give AI 2-3x productivity boost

🔄

Ralph Wiggum: --max-iterations is your safety net against infinite loops

ralph-wiggum
🔍

Semantic search finds "authentication issues" from log "fixed user login bug"

claude-mem
🛡️

Never run AI agent where you store SSH keys. Use container/VM isolation

📈

3-stage autonomy: advise → act with approval → full autonomy in strict boundaries

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