Claude Code: Everything You Need to Know

A practical guide to Claude Code — from your first prompt to multi-agent automation, hooks, MCP, and team workflows. Built around clear mental models and real examples, not marketing.

npm install -g @anthropic-ai/claude-code

Who this is for: Developers using (or about to use) Claude Code. Beginners get a guided path; power users get depth on Skills, Hooks, MCP, and Agent Teams.

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🧭 Choose your path

You are…	Start here	Time
🚀 New to Claude Code	Setup → Prompt Engineering → Your First Skill	~15 min
⚡ Already using it, want depth	Skills · Hooks · MCP	~30 min each
🧠 Building teams or automation	Agent Teams · Super Claude · BMAD	varies

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🧠 When to use what

The four extension points in Claude Code, side by side:

Tool	Use when…	Skip if…	Lives in
Skills (slash commands)	You repeat the same prompt or workflow ≥3 times	One-off task	.claude/commands/*.md
Hooks	You want code to run automatically on tool use, session start, etc.	You only want manual triggers	.claude/settings.json
Subagents	A subtask is big enough to need its own isolated context	The task fits in your main session	.claude/agents/*.md
MCP servers	You need Claude to use external tools (browsers, DBs, APIs)	All your data is in local files	Configured per project

💡 These four compose. Most polished workflows combine 2–3.

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📚 What's inside

Fundamentals — What is Claude Code? · Setup · Prompt Engineering

Workflow extensions — Slash Commands · Skills · Hooks

Multi-agent & integration — Subagents · Agent Teams · MCP

Productivity & frameworks — Effort levels · Fast Mode · Super Claude · BMAD Method

Reference — Slash Command Cheatsheet · Effort Levels · FAQ · Updates & Deprecations · Further Reading

What is Claude Code?

Claude Code is Anthropic's official CLI for working with Claude from your terminal. You point it at a project; it reads the code, plans, edits files, runs commands, and commits — all from the prompt line.

Three things it does that a chat UI can't:

• Reads your actual repo — not pasted snippets. Claude sees your file tree, runs grep, follows imports, and grounds answers in real context.
• Edits in place and runs your tests — diff-aware edits, then pytest/vitest/go test on the spot to verify the change.
• Composes with the rest of your stack — slash commands, hooks, sub-agents, MCP servers, and your normal git/shell workflow.

If you've used Copilot or Cursor, think of Claude Code as their "agent in your terminal" peer — same idea, different surface, no editor lock-in.

claude          # start a session in the current repo
> explain what this codebase does
> fix the failing test in src/api.test.ts
> open a PR with the changes

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Claude Opus 4.7: The Latest Flagship

Claude Opus 4.7 is the current flagship in the Claude 4 family (May 2026) — sharper adaptive thinking, better long-context grounding, more reliable tool calling than Opus 4.6, at the same price. 200K context (1M beta via API), 128K max output.

Choosing a model — quick guide:

Model	Reach for it when…
Opus 4.7	Complex reasoning, large refactors, multi-file analysis, production-critical code
Sonnet 4.6	Balanced everyday work — most coding tasks live here
Haiku 4.5	Fast, lightweight tasks — quick questions, doc updates
Opus 4.6 (legacy)	Pin a specific build; also the model behind Fast Mode (/fast)

→ Full specs, capabilities, and pricing in docs/reference/models.md

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Claude Code Setup

⏱️ 5-minute setup. Get from zero to your first AI-assisted commit.

1. Install

npm install -g @anthropic-ai/claude-code

Requires Node.js 18+. For other install methods (Homebrew, curl, native binary), see the official install guide.

2. Authenticate

claude

On first run, Claude Code opens a browser to sign in with your Anthropic account (Pro, Max, or API key all work). After that, you can re-authenticate any time with /auth login from inside Claude.

3. Run your first prompt

From any project directory:

cd ~/your-project
claude

Once Claude Code is running, try one of these:

• explain what this codebase does — Claude reads your repo and summarizes.
• add a README section about installation — generates content based on your project.
• find and fix the failing test in src/api.test.ts — diagnoses and edits in place.

4. (Optional) Generate a CLAUDE.md

/init

Creates a project-level instruction file that Claude reads on every session — your project's "house rules." More on this in Prompt Engineering Deep Dive.

5. (Bonus) See a real Claude Code project setup

This repo's own .claude/ directory is a working example of a fully-configured Claude Code project. Browse it as a reference for what a polished setup looks like:

Path	What it does
.claude/settings.json	Project-level Claude Code settings — permissions, hooks, MCP integrations
.claude/agents/	5 specialized subagents (frontend, tech lead, PM, UX designer, code reviewer)
.claude/commands/	8 custom skills — /pr, /review, /tdd, /test, /five, /ux, /todo, /mermaid. See Skills for the full guide.
.claude/hooks/	Python hook scripts (pre_tool_use.py, post_tool_use.py, notification.py, stop.py, subagent_stop.py) — see Hooks

💡 Next: Once you're comfortable with the basics, jump to Claude Skills to build reusable slash commands in 3 minutes.

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Prompt Engineering Deep Dive

📖 Claude Initialization Run the /init command to automatically generate a CLAUDE.md file. Your CLAUDE.md files become part of Claude's prompts, so they should be refined like any frequently used prompt. A common mistake is adding extensive content without iterating on its effectiveness. Take time to experiment and determine what produces the best instruction following from the model.

1. Explore → Plan → Code → Commit

Versatile workflow for complex problems.

• Explore: Read relevant files/images/URLs; use subagents for verification. Do not code yet.
• Plan: Ask Claude to make a plan. Use "think", "think hard", "think harder", or "ultrathink" to nudge depth in the prompt — see Effort levels for the full reasoning dial. Optionally save the plan for future reference.
• Code: Implement the solution; verify reasonableness as you go.
• Commit: Commit results, create pull requests, update READMEs/changelogs.
• Claude has two default modes: Plan Mode and Accept Edits Mode. You can toggle between them using the Shift + Tab keys.
  • 
  • 

💡 Pro Tip: Research & planning first significantly improves performance for complex tasks.

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2. Test-Driven Workflow (Write Tests → Code → Commit)

Ideal for changes verifiable with unit/integration tests.

• Write Tests: Create tests based on expected inputs/outputs; mark as TDD.
• Run & Fail Tests: Confirm they fail; no implementation yet.
• Commit Tests: Commit once satisfied.
• Write Code: Implement code to pass tests; iterate with verification via subagents.
• Commit Code: Final commit after all tests pass.

🔹 Clear targets (tests, mocks) improve iteration efficiency.

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3. Visual Iteration (Code → Screenshot → Iterate → Commit)

• Provide screenshots or visual mocks.
• Implement code, take screenshots, iterate until outputs match mock.
• Commit once satisfied.

🔹 Iteration significantly improves output quality (2-3 rounds usually enough).

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4. Effort levels — how hard Claude thinks

→ Full guide in docs/reference/effort-levels.md

Mental model: Effort is a behavioural dial, not a token budget — it shifts thinking depth, tool-call appetite, response length, and how persistently Claude pushes through multi-step work. Higher ≠ smarter; context quality often matters more.

5 levels exist (most users assume 4):

Level	Reach for it when…
low	Fast interactive queries you're steering — file renames, simple greps
medium	General coding, small refactors, autonomous sessions where the plan is clear
high	Multi-file refactors, complex debugging — Anthropic's recommended default for Sonnet 4.6 / Opus 4.6
xhigh (Opus 4.7 only)	Long autonomous agentic sessions — Anthropic's recommended default for Opus 4.7
max	Architecture, subtle bugs, security review — genuinely hard problems only

Current defaults (May 2026): Opus 4.7 → xhigh; Opus 4.6 + Sonnet 4.6 → high on every plan. (The "Pro/Max users on a nerfed medium default" lore was true ~March → late April 2026 but is fixed in Claude Code v2.1.117. Check yours with /effort.)

Setting it, in order of persistence:

# This turn only — adds an in-context cue (does not change API effort)
> ultrathink — design the migration strategy

# This session — slider with no args, level name with arg
/effort xhigh
/effort auto                    # reset to model default

# All sessions, low/medium/high/xhigh — settings.json
echo '{ "effortLevel": "high" }' > .claude/settings.json

# All sessions, max — only the env var works
export CLAUDE_CODE_EFFORT_LEVEL=max

⚠️ Three gotchas worth knowing:

• Anthropic's own guidance for Opus 4.7 max: "shows diminishing returns and is more prone to overthinking" on routine work. Don't default to it.
• "effortLevel": "max" in settings.json silently downgrades — only CLAUDE_CODE_EFFORT_LEVEL=max env var persists max.
• Context quality often beats more effort. If you're reaching for max on a task that shouldn't need it, ~80% of the time the fix is upstream — sharper CLAUDE.md, atomic plan, named files. Full breakdown →

💡 Pattern: plan-with-Opus / execute-with-Sonnet. Plan in Opus xHigh or Max; hand the atomic, zero-ambiguity plan to Sonnet at lower effort to execute. Sonnet follows clear plans without drift, so the cheap execution is reliable when the plan is sharp.

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Claude Commands

Claude Code ships ~30 built-in slash commands plus the ability to define your own as skills (markdown files in .claude/commands/). The two work together — built-ins for common operations, custom skills for your team's workflows.

Day-1 essentials

Command	Purpose
/init	Generate a CLAUDE.md for your project — your "house rules" Claude reads every session
/help	List all available commands
/clear	Reset conversation history when you want a clean slate
/cost	Track token usage in this session
/model	Switch between Opus 4.7, Sonnet 4.6, Haiku 4.5 (4.6/4.5 still available)

→ Full slash-command reference in docs/reference/commands.md (~30 commands including /auth, /fast, /hooks, /mcp, /teleport, …)

Custom slash commands

Define a frequently-used prompt once as a markdown file, invoke it forever with /skill-name:

mkdir -p .claude/commands
echo "Analyze this code for performance issues and suggest optimizations:" \
  > .claude/commands/optimize.md

💡 Next level: custom slash commands and Skills are the same thing. Head to Claude Skills for the deep dive — built-in skills, the 7 custom skills in this repo, workflow recipes, and how to write your own.

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Claude Skills

~3 min read · Full guide in docs/skills.md →

Mental model: Skills package a workflow into a markdown file. Two flavors:

• Slash skills — .claude/commands/<name>.md, you invoke them with /<name>
• Agent Skills — .claude/skills/<name>/SKILL.md with YAML frontmatter; Claude auto-invokes when the description matches the task

⚠️ Security: Skills are executable instructions running with your shell permissions. Read every third-party skill before adding it — exactly like reviewing a shell script before sourcing it.

Your first skill in 3 minutes

mkdir -p .claude/commands

cat > .claude/commands/analyze.md << 'EOF'
# Code Analysis

Analyze the current code for:
- Potential bugs and edge cases
- Performance optimizations
- Code quality improvements
- Security vulnerabilities

Provide specific, actionable recommendations.
EOF

claude       # then type: /analyze

That's it — a working slash skill. Promote it to an Agent Skill later by moving it to .claude/skills/analyze/SKILL.md and adding name/description frontmatter.

Want more depth?

The full Skills guide in docs/skills.md covers:

• The 7 custom slash skills in this repo's .claude/commands/: /pr, /review, /tdd, /test, /five, /ux, /todo
• The 2 built-in skills (/keybindings-help, /mermaid)
• Slash skills vs Agent Skills — when to use each, frontmatter contract, conversion path
• Workflow recipes — feature dev with TDD + PR, bug investigation, UX-first dev
• How to write your own skills (file format, scope, examples)
• Skills FAQ, troubleshooting, and best practices

Beyond your own skills — the ecosystem

The community has built thousands of Agent Skills. Three places to start browsing:

Resource	What it offers
anthropics/skills	Anthropic's official skills — PDF, slides, brand guidelines, document creation
SkillHub · SkillsMP · Smithery	Searchable marketplaces — thousands of community Agent Skills indexed from GitHub
travisvn/awesome-claude-skills · ComposioHQ/awesome-claude-skills	Curated lists for high-signal picks

Notable community skills: skill-creator, skill-installer, mcp-builder, systematic-debugging, pair-programming, github-code-review, pptx, react, frontend-design, prompt-engineering-patterns, superpowers, brainstorming, market-research-reports, senior-data-engineer, and many more — see the full ecosystem section in docs/skills.md for categorized tables and install paths.

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Hooks

Mental model: Hooks are programmable checkpoints on Claude Code's lifecycle (before/after a tool call, session start, prompt submit, etc.). Your script inspects the proposed action and returns allow / deny / modify.

Three cases that win most teams over:

Use case	What the hook does
Auto-format on save	Runs prettier / ruff / gofmt after every Edit so Claude's output matches your style
Block sensitive paths	Refuses changes to .env, secrets/, infra/prod/ regardless of what Claude tries
Action audit log	Records every tool call to a file — paper trail of what Claude did and when

If none of those resonate, skip ahead.

Setting up hooks

Hooks live in settings files at four scopes (later overrides earlier):

Scope	Path
User-wide	~/.claude/settings.json
Project (committed)	.claude/settings.json
Project (local, gitignored)	.claude/settings.local.json
Enterprise managed policy	platform-specific

Quickest setup — use the interactive menu added in 2026:

/hooks    # browse, enable, configure hooks without touching JSON

Manual setup — for the hook scripts in this repo:

1. Copy .claude/hooks/ into your project's .claude/ folder.
2. Delete the hook scripts you don't need; keep the rest.
3. Install uv (required to run the Python hook scripts).
4. Copy .claude/settings.json into your project's .claude/ folder.
5. In settings.json, replace any hardcoded uv path with the output of $(which uv).

project-root/
└── .claude/
    ├── hooks/
    │   ├── notification.py
    │   ├── post_tool_use.py
    │   └── ...
    └── settings.json

Hook Events

Hooks run in response to various events within Claude Code's lifecycle: examples

• PreToolUse: Runs after Claude creates tool parameters but before processing the tool call.
• PostToolUse: Runs immediately after a tool completes successfully.
• Notification: Runs when Claude Code sends notifications, such as when permission is needed to use a tool or when prompt input has been idle.
• UserPromptSubmit: Runs when the user submits a prompt, before Claude processes it.
• Stop: Runs when the main Claude Code agent has finished responding (does not run if stopped by user interrupt).
• SubagentStop: Runs when a Claude Code subagent (Task tool call) has finished responding.
• SessionEnd: Runs when a Claude Code session ends.
• PreCompact: Runs before Claude Code is about to run a compact operation.
• SessionStart: Runs when Claude Code starts a new session or resumes an existing session.
• TeammateIdle: Runs when an agent teammate becomes idle (new 2026, for Agent Teams).
• TaskCompleted: Runs when a task is marked as completed (new 2026).

Hook input

Hooks receive JSON via stdin. Every event includes session_id, transcript_path, and cwd. Event-specific fields:

Hook Event	Event-specific fields
PreToolUse	tool_name, tool_input
PostToolUse	tool_name, tool_input, tool_response
Notification	message
UserPromptSubmit	prompt
Stop / SubagentStop	stop_hook_active
PreCompact	trigger, custom_instructions
SessionStart	source
SessionEnd	reason
TeammateIdle	teammate_id, last_activity
TaskCompleted	task_id, task_name, completion_time

Hook output

Two ways to communicate back: exit codes for simple control, JSON in stdout for fine-grained behavior.

Exit code	Effect
0 (success)	stdout shown in transcript mode (CTRL-R). For UserPromptSubmit / SessionStart, stdout is added to Claude's context.
2 (blocking)	stderr fed back to Claude (or shown to user) to block the action. Stops tool calls in PreToolUse; stops prompt processing in UserPromptSubmit.
Other	stderr shown; execution continues.

Advanced: structured JSON in stdout. Per-event decision fields:

Event	JSON output
PreToolUse	permissionDecision: "allow" / "deny" / "ask"; updatedInput to modify tool parameters (new 2026)
PostToolUse	decision: "block" or undefined; additionalContext can be returned
UserPromptSubmit	decision: "block" or undefined; additionalContext can be returned
Stop / SubagentStop	decision: "block" or undefined
SessionStart	additionalContext

Security considerations

Hooks run arbitrary shell commands automatically with your user permissions — they can read, modify, or delete any file you can. Anthropic provides no warranty for what your hooks do.

Best practices:

• Validate and sanitize all inputs from stdin JSON
• Quote shell variables ("$var", not $var)
• Block path traversal (.., absolute paths outside the project)
• Use absolute paths for invoked scripts so PATH attacks don't redirect
• Explicitly skip sensitive files (.env, .git/, secrets/)

Claude Code snapshots your hook configuration at session start and warns if hooks change mid-session — review before applying.

Execution & debugging

• Timeout — 60s per hook by default; configurable per command.
• Parallelization — all matching hooks run in parallel.
• Environment — hooks run in the current dir with Claude Code's env; CLAUDE_PROJECT_DIR is available.
• Debug — /hooks shows current config; claude --debug shows hook execution logs; test scripts manually with the JSON payload piped to stdin.

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Subagents

Three building blocks for going beyond a single Claude session:

Building block	What it gives you	When to reach for it
Git worktrees	Multiple branches checked out simultaneously, each in its own folder + Claude session	You want to work on feature-A while Claude finishes feature-B
General-purpose subagents	Spawn isolated Claude sub-sessions from your main session for parallel sub-tasks	A task is large or context-heavy enough that the main session shouldn't carry it
Specialized subagents	Pre-written role prompts (security-reviewer, frontend-engineer, etc.) you drop into .claude/agents/	You want focused expertise without writing the role prompt yourself

1. Git worktrees — parallel branches, parallel sessions

Git worktrees let one repo have multiple branches checked out at the same time, each in its own folder. Pair them with one Claude Code session per worktree to run independent streams of work.

git worktree add -b feature-a ../feature-a    # create the worktree
cd ../feature-a && claude                     # start Claude in it
# Repeat in another terminal for feature-b. Each session is independent.
git worktree remove ../feature-a              # clean up when done

💡 Use tmux to keep each worktree's session attached even when you close the terminal.

2. General-purpose subagents — when one Claude isn't enough

From your main session, ask Claude to spawn subagents for a parallel sub-task. Each subagent runs in its own context window and reports a summary back, so the main session stays focused.

Analyze the implementation of the payment feature.
Spawn 5 subagents to accelerate the work.
Ultrathink.

3. Specialized subagents — drop-in role prompts

Specialized agents are pre-written role prompts you drop into .claude/agents/. Each one carries its own focus and tooling, so a security-reviewer agent stays focused on threats instead of also opining on code style.

This repo ships 10 production-ready specialist prompts you can drop into .claude/agents/:

Role	System prompt	Role description
Backend Engineer	prompt	description
Frontend Engineer	prompt	description
Database Engineer	prompt	description
Tech Lead	prompt	description
Code Reviewer	prompt	description
Security Reviewer	prompt	description
UX Engineer	prompt	description
Design Reviewer	prompt	—
Project Manager	prompt	description
Business Analyst	prompt	description

📐 The two .canvas flowcharts in specialized-agents/ (agent-creation-workflow.canvas, agent-orchestration-workflow.canvas) visualize the full pipelines. Open in Obsidian or any canvas-compatible viewer.

Orchestrating specialists from the main session

Once you have specialized agents, the main session orchestrates them:

Have backend-engineer suggest UI improvements; have frontend-engineer
implement them; have code-reviewer review the changes; have
frontend-engineer address the review feedback.

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Agent Teams (Experimental - 2026)

Agent Teams is an experimental feature that lets a single Claude Code session coordinate multiple specialist agents through a shared task list. The main session acts as the team lead; teammates work on their tasks (sometimes in parallel), report progress, and update the shared list. Reach for it on full-stack features, large refactors, or anything where multiple perspectives genuinely help. Skip it for single-file edits and quick fixes.

Enable it

export CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1   # add to ~/.zshrc to persist
claude

How a team is structured

• Team lead (your main session) — assigns tasks, sets dependencies, reviews completed work.
• Teammates (specialist sub-agents) — focus on a single task each, update the shared list, can request help.
• Shared task list — single source of truth visible to everyone, tracks dependencies and status (pending / in progress / completed).

Multi-agent collaboration patterns

1. Parallel development — three independent streams, three teammates, one team lead synthesizing.

Task 1: frontend-engineer  – Build login UI
Task 2: backend-engineer   – Implement auth API
Task 3: qa-engineer        – Write integration tests

2. Sequential pipeline — one teammate's output feeds the next.

Task 1: backend-engineer   – Create database schema
  ↓ (blocks Task 2)
Task 2: backend-engineer   – Implement CRUD endpoints
  ↓ (blocks Task 3)
Task 3: frontend-engineer  – Build admin dashboard

3. Code review workflow — feature lands, multiple reviewers, then revision.

Task 1: feature-developer  – Implement new feature
  ↓ (completed)
Task 2: security-reviewer  – Check for vulnerabilities
Task 3: perf-reviewer      – Analyze optimization opportunities
  ↓ (both complete)
Task 4: feature-developer  – Address review feedback

4. Research → implementation — one analyst, multiple workers applying findings.

Task 1: research-agent     – Analyze existing codebase patterns
  ↓ (generates recommendations)
Task 2: implementation team – Apply findings across modules
  - Teammate A: auth module
  - Teammate B: payment module
  - Teammate C: notifications module

Example: full feature implementation

Team lead: coordinate overall strategy.

Teammates:
- planner: requirements → technical spec
- backend-dev: API endpoints
- frontend-dev: UI components
- db-specialist: schema design + migration
- qa-engineer: unit + integration tests
- security-reviewer: security audit

Dependencies:
1. planner → spec (blocks all)
2. db-specialist → schema (blocks backend-dev)
3. backend-dev + frontend-dev in parallel
4. qa-engineer waits for implementation
5. security-reviewer waits for all code

Best practices

✅ Do	❌ Don't
Give teammates clear, focused, single-purpose tasks	Spawn more than 3–5 teammates per session
Use descriptive names (frontend-specialist, not agent1)	Assign vague or overlapping tasks
Set explicit task dependencies (blockedBy)	Skip dependencies — they prevent merge conflicts
Let teammates work in parallel when possible	Micromanage; trust the role prompt
Use /agents to inspect team status	Mix different project contexts in one team

Common issues

Issue	Fix
Teammates conflicting on the same files	Add explicit blockedBy dependencies
Team too slow	Reduce team size; increase parallelization
Tasks stuck	Check for circular dependencies
Context drift	Make task descriptions and teammate names more specific

Limitations (it's experimental)

• Multiple agents consume more tokens — budget accordingly.
• Large teams add coordination overhead; 3–5 teammates is the sweet spot.
• Teams are session-scoped — they don't persist across restarts.
• Top performance with Opus 4.7; works on Sonnet 4.6 with shallower reasoning.

🔮 Roadmap: persistence, visual dashboards, inter-team communication. See docs/reference/changelog.md → Coming soon.

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Model Context Protocol (MCP)

Mental model: MCP is a universal translator that lets any AI tool talk to any data source through one open protocol — USB-C for AI integrations.

Featured MCP servers

Full setup walkthroughs in mcp-servers/. New to MCP? mcp-servers/playwright.md has a working three-line setup.

Server	What it adds	Walkthrough
Serena	Symbol-level code navigation and editing across many languages	serena.md
Sequential Thinking	Step-by-step reasoning that breaks complex problems into manageable steps	sequential-thinking.md
Memory	Persistent context across sessions	memory.md
Playwright	Browser automation — interaction, scraping, testing, accessibility	playwright.md

See mcp-servers/README.md for the comparison matrix, install commands, and troubleshooting.

More MCP servers worth knowing

Install from the registry or follow the source link. No walkthrough yet — source READMEs cover setup.

General-purpose:

Server	What it adds	Source
Context7	Live, version-pinned library docs piped into the prompt — grounds answers in current API surfaces instead of training-cutoff guesses	upstash/context7
Tavily	Web search, page extraction, and research designed for AI agents	tavily-ai/tavily-mcp
Chrome DevTools	Drives a real Chrome instance — DOM inspection, network logs, console, performance traces, screenshots	ChromeDevTools/chrome-devtools-mcp
mem0	Hosted long-term memory with semantic recall across sessions and projects (richer than the local-only Memory server above)	mem0ai/mem0

Specialized — reach for these when the workflow fits:

Server	What it adds	When to reach for it	Source
context-mode	Curates and ships project context (rules, files, conventions) on demand	Multi-repo setups wanting consistent context without hand-rolled CLAUDE.md plumbing	mksglu/context-mode
Shadcn	Pulls shadcn/ui component source into the session for accurate scaffolding	Frontend work in a shadcn/ui codebase	ui.shadcn.com/docs/mcp
LangSmith	Trace, evaluate, and debug LLM apps from inside Claude Code	Building on LangChain / LangGraph and want observability without leaving the terminal	langchain-ai/langsmith-mcp-server
TrustGraph	Knowledge-graph-backed RAG with multi-source ingestion and graph-aware retrieval	Advanced RAG where flat vector search isn't enough — entity-rich corpora, agentic retrieval	trustgraph-ai/trustgraph

The N×M problem MCP solves

Before MCP, every AI app needed a custom integration for every tool: n apps × m tools = n × m brittle one-off connections. Teams inside the same company would reinvent the same Slack/GitHub/Postgres integration over and over.

MCP collapses this to N + M: each app implements MCP once, each tool exposes MCP once, and any combination works together. Same pattern Web APIs gave us for app-to-server and LSP gave us for editor-to-language tooling.

Three pillars

Each pillar makes ownership explicit, so it's always clear who's driving:

Pillar	Controlled by	Purpose
Tools	The model	Lets the AI take actions — query a DB, call an API, write a file
Resources	The application	Feeds the AI structured context — files, error logs, JSON objects
Prompts	The user	Slash-command shortcuts that kick off multi-step workflows

The MCP Registry & self-discovering agents

The official MCP Registry (live since September 2025) is the app-store-equivalent for MCP servers. An agent that needs to check Grafana logs but doesn't have a Grafana tool wired up can ping the registry, find the verified server, install it, and continue — teaching itself a new capability on the fly.

What's new in 2026

• Live registry — search official and community servers at registry.modelcontextprotocol.io or claude mcp search <keyword>.
• Linux Foundation governance — MCP donated to the Agentic AI Foundation for vendor-neutral development.
• MCP Apps — servers can ship interactive UI components, not just text tools.
• OAuth client credentials — built-in --client-id / --client-secret flags for authenticated services.
• Async operations — non-blocking server calls for long-running tasks.
• Server discovery — .well-known URLs for automatic discovery.

claude mcp add <server> npx '@<package>@latest'                    # install from registry
claude mcp add my-api --client-id ID --client-secret S npx '...'   # with OAuth
/mcp                                                                # list connected servers

📚 References: MCP roadmap · Official servers.

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Fast Mode ↯

/fast toggles 2.5× faster responses at 6× the price. The ↯ indicator confirms it's on.

⚠️ Fast Mode runs on Opus 4.6 — not 4.7. Even if your default model is Opus 4.7, /fast switches the underlying model to 4.6. Sonnet and Haiku don't have a Fast Mode.

	Standard Opus 4.6	Fast Mode (Opus 4.6)
Input (per MTok)	$5	$30
Output (per MTok)	$25	$150

/fast                                    # toggle on (↯ appears)
> fix the auth bug in src/login.ts       # ~2.5× faster
/fast                                    # toggle off when done

Decision rule: use it when latency matters (live debugging, demo prep, time-pressured fixes). Skip it for background work, exploration, or anything where 2.5× faster isn't worth 6× the cost. Use /cost to monitor the bill.

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Super Claude Framework

An open-source framework that bolts pre-built personas, commands, and workflows on top of Claude Code.

What it is. Super Claude ships 15+ ready-made specialist agents (architect, security, performance, frontend, etc.) plus a library of structured slash commands (/sc:analyze, /sc:improve, /sc:design) and behavioral flags. It installs into ~/.claude/ as a drop-in extension to Claude Code.

Why pair it with Claude Code. Skips the "write your own role prompts" phase. You get a curated library of expert personas and commands the community has already iterated on — useful as both a daily tool and a reference for how to write your own skills.

When to reach for it. You want pre-built specialist agents without building them yourself, or you're learning prompt patterns by reading well-crafted examples.

→ Source on GitHub

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The BMAD METHOD — AI Agent Framework

A multi-agent framework that walks a project from idea to working software using a team of Claude agents in defined roles.

What it is. BMAD (Breakthrough Method of Agile AI-driven Development) coordinates specialist agents — analyst, PM, architect, developer, QA, scrum master — through a full SDLC. Each role produces specific artifacts (PRD, architecture doc, story file) that the next role consumes. Works as a Claude Code extension via skills and agents.

Why pair it with Claude Code. Gives you a documented, repeatable workflow for greenfield builds. Instead of asking Claude "build me an app," you walk through analyst → PM → architect → developer → QA, each agent producing structured output the next one consumes.

When to reach for it. Greenfield projects, large feature builds, or any time you want planning rigor before code. Overkill for a quick fix or a one-file refactor.

→ Source on GitHub

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FAQ

→ Full FAQ in docs/reference/faq.md — covers models, pricing, tokens, plans, Fast Mode, worktrees, and Pro-plan optimization.

A few of the most-asked questions:

How many messages do I get on the Pro plan? ~45 messages per 5-hour rolling window. Full access to all models (Opus 4.7, Sonnet 4.6, Haiku 4.5; previous-gen 4.6/4.5 also available). Details →

What's the difference between Pro, Max 5x, and Max 20x? Pro $20/mo, Max 5x $100/mo (5× usage), Max 20x $200/mo (20× usage). All tiers include Claude Code and all models. Pricing details →

Should I use Fast Mode? Use it for rapid iteration and time-sensitive work; skip it for long background tasks (6× pricing). More →

What's the difference between custom slash commands and skills? Same thing — both are markdown files in .claude/commands/. See Skills FAQ in docs/skills.md.

Can I use Opus 4.7's 1M context window? Beta and API-only at the moment. Subscription plans are limited to 200K. Beta access details →

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Updates & Deprecations

→ Full changelog in docs/reference/changelog.md — major changes, new features, and deprecations through May 2026.

Recent highlights:

• 🆕 Claude Opus 4.7 (May 2026) — sharper reasoning and better long-context grounding than Opus 4.6 at the same price.
• 🆕 Claude Sonnet 4.6 (May 2026) — replaces Sonnet 4.5 as the everyday balanced tier.
• 🆕 Agent Teams — multi-agent collaboration with team leads and shared task lists (experimental).
• 🆕 MCP Registry is live at registry.modelcontextprotocol.io, now governed by the Linux Foundation.
• 🆕 New commands: /fast, /auth, /debug, /teleport, /rename, /hooks.
• 📝 Pro plan now includes every current and previous-generation model; usage measured in messages (~45 / 5hr), not tokens.
• ❌ /login and /logout deprecated in favor of /auth login / /auth logout.

💡 For Anthropic's authoritative release notes, see docs.anthropic.com/release-notes/claude-code.

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📖 Reading tips

• Reading on GitHub? The in-page anchor links work natively. Tap the table-of-contents button (top-left of the file view) for fast jumping.
• Want a richer markdown view? This repo plays well with Obsidian — handy if you also want to open the specialized-agents/*.canvas flowcharts.
• On a phone? Stick to the Choose your path section at the top; the wider tables read best on desktop.

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References

→ Full reading list in docs/reference/further-reading.md

A curated set of pointers — official Anthropic docs, MCP resources, hooks examples, workflow tutorials, pricing references, and adjacent tooling.

Most-clicked starting points:

• Claude Code overview (official)
• Claude Code best practices (Anthropic engineering)
• Building effective agents (Anthropic engineering)
• Official MCP Registry
• Hooks reference (official)

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Features, pricing, and availability change frequently. Always check the official Anthropic documentation for the most current information.

Last reviewed May 2026 · Spotted something stale? Open an issue or send a PR — see CONTRIBUTING.md.