Claude Code Best Practice

Practical patterns for Claude Claude Code

github.com/shanraisshan/claude-code-best-practice

Shayan Rais

Software Architect at

Education

Master’s in Computer Science

FAST NUCES — 2019

Bachelor’s in Computer Information Systems

NED University — 2014

Contributions

Creator of claude-code-best-practice

The most starred 🇵🇰 Pakistani AI repo

with almost 50,000 stars ★

claude-code-best-practice GitHub stars growth

🚨 Problem Statement

Fetch weather of Karachi

Single source of truth
Repetitively

By solving this problem statement, we’ll learn different concepts of agentic engineering along the way.

Jargon you'll hear

I'll unpack each of these as we go — for now, just let them wash over you.

agentic engineering progressive disclosure orchestration dumb zone agentic workflows harness compaction context window ralph wiggum loop MCP hooks context rot prompt engineering AI slop inference hallucination context bloat one-shot prompting token burn vibe coding

scroll for glossary ↓

progressive disclosure — feeding the AI only what it needs right now, not everything upfront

orchestration — coordinating several AI agents like a conductor leads a band

dumb zone — the stretch where AI has too much context and starts thinking worse, not better

agentic workflows — AI that plans, acts, checks its work, and adapts — multi-step on its own

harness — the scaffolding around the model — files, terminal, tools — that turns a chatbot into a worker

compaction — auto-summarizing old chat so the AI keeps going without hitting its memory ceiling

context window — the AI’s working memory — how much it can “see” at once before older details fall off

ralph wiggum loop — when the AI repeats the same broken step in circles, like a confused kid who can’t stop

MCP — a universal adapter letting AI talk to your tools (GitHub, Slack, databases)

hooks — auto-triggers that run your rules before or after the AI does anything

context rot — quality decay as the conversation drags on and earlier details blur

prompt engineering — the craft of phrasing requests so the AI understands exactly what you mean

AI slop — low-effort, generic AI output that looks polished but says nothing

inference — the moment the model actually runs to produce an answer. Training is when the model learned (once, long ago). Inference is the model answering you, right now

hallucination — when AI confidently makes up facts that sound true but aren’t

context bloat — overstuffing the AI’s memory so it slows down and loses focus

one-shot prompting — giving the AI one example and asking it to follow the same pattern

token burn — wasting expensive AI “words” on unnecessary back-and-forth or bloated prompts

vibe coding — describing what you want in plain English and hoping the AI nails it

agentic engineering — building guardrails so AI acts like a reliable teammate, not a gamble

Difference between GPT and ChatGPT?

discuss

Boris Cherny slides showing the spectrum of Claude Code usage styles — Boris Cherny (creator of Claude Code) — different teams use Claude Code completely differently.
There is no single “correct” way. But there *are* patterns worth understanding.

What is Claude Code?

Model (Brain 🧠 — e.g. Opus, GPT) + Harness (Body 💪 — e.g. tools, MCP, memory)

🦜

“Stochastic Parrots”

Stochastic means random/probabilistic — models don’t know the answer, they sample from a probability distribution.

Prompt: “The sky is ___”

blue

62%

clear

14%

dark

8%

falling

0.3%

Each run the model samples — temperature controls how widely it samples.

Bender, Gebru, McMillan-Major, Mitchell — On the Dangers of Stochastic Parrots (2021)

stochastic sycophantic hallucinating stateless biased confident pattern-matching

💬 Models are stateless

User Model

“My name is Shayan.” ➜ to model

“Okay, your name is Shayan.” ➜ to user

“What is my name?” ➜ to model

“I don’t know your name — I have no memory of what you just said.” ➜ to user

Every turn is a fresh API call.

Memory only exists if the harness replays the transcript.

🧠 Models — e.g. Opus, GPT, Gemini

✅ Can answer

“What is the capital of Japan?”

“When did Pakistan gain independence?”

“Who wrote Romeo and Juliet?”

❌ Can’t answer

“Who won yesterday’s match?”

“What’s today’s USD → PKR rate?”

“What did Anthropic release yesterday?”

Every model — no matter how new — has a knowledge cut-off. Events after that date simply do not exist inside the model.

Opus 4.7 — Anthropic

Knowledge cut-off: January 2026

Released 2026-04-17

GPT-5.5 — OpenAI

Knowledge cut-off: December 1, 2025

Released 2026-04-23 — brand-new, but still has a cut-off.

Gemini 3.1 Pro — Google

Knowledge cut-off: January 2025

Released 2026-02-19

🧠 Limitations

The raw model has no real-time access — no internet, no files, no clock.

A horse harness. A model harness.

The model is the horse. Raw power, no direction. The harness is everything else.

The origin is Old French harneis — gear, equipment, armor.

⚡ Tool Calling — how the harness reaches the world

In the diagram above: Turn × 1 · Inference × 2

Turn — one round from the user’s view: you ask, the assistant answers. The entire flow above — your request, the assistant’s tool calls, and the final reply — is one turn.

Inference — one call to the language model. The model wakes up, reads the input it was given, writes a reply, then forgets everything. Every arrow touching the “Language Model” column above is a separate inference. One turn can contain many inferences.

💪 Harness — the body around the brain

🧑‍💼

Agents — the specialists

A dedicated Claude worker — own context, tools, focus.

✅ fresh working memory per run

🎯

Skills — the know-how

What the specialist (or Claude) can actually do.

✅ progressive disclosure — loaded on demand

📘

Workflows — the instruction manual

Repeatable step-by-step recipes — like an AC install guide.

✅ reproducible recipes

📝

CLAUDE.md — your memory

Knowledge you provide to the model.

⚠️ 200-line problem

💭

Context — the working memory

What Claude holds in his head now — fresh every new chat session.

20% of 1M tokens

⚠️ dumb-zone problem

💪 Harness — the body around the brain

✋

Tools — the hands

Built-in: Read, Edit, Bash, WebSearch.

🔌

MCP — the adapters

USB-C for AI — plug in external tools (databases, browsers, APIs).

e.g. 👁️ Claude in Chrome

🛡️

Permissions — the guardrails

Allow / ask / deny for tool use.

🪝

Hooks — the reflexes

Deterministic scripts that fire on events.

📝

System Prompt — Claude’s memory

Knowledge Anthropic bakes in.

e.g. identity · tone · safety

✅ always on

🎉 Yayyyyy! Problem solved with harness

The harness reaches out via WebSearch and fetches a real answer from live sources.

?

Really?

💪 Non-determinism — Doesn’t always use its tools

Similar prompt — but this time the model decided not to use the tool.

💪 Non-determinism — Tools can fail

The model first tried one source — it failed (403) — so it fell back to another.

🚨 Problem Statement

❌ Single source of truth
The model does not always follow the same path.
❌ Repetitively
Model sometimes failed/forgot to use the tool.

Vibe Coding

Andrej Karpathy — OpenAI founding team · former Director of AI at Tesla · founder of Eureka Labs.

Vibe Coding vs Agentic Engineering

# Plain TodoApp
todoapp/
  backend/
    main.py
    routes/
      todos.py
      users.py
    models/
      todo.py
      user.py
    tests/
      test_todos.py
  frontend/
    components/
      TodoList.tsx
      Sidebar.tsx
    pages/
      index.tsx
      todos.tsx
    lib/
      api.ts

# With Claude Code Best Practices
todoapp/
  .claude/                  # Claude Code config
    agents/               # Custom subagents
    skills/               # Domain knowledge
    commands/             # Slash commands
    hooks/                # Lifecycle scripts
    rules/                # Modular instructions
    settings.json         # Team settings
    settings.local.json   # Personal settings
  backend/
    main.py
    routes/
      todos.py
      users.py
    models/
      todo.py
      user.py
    tests/
      test_todos.py
    CLAUDE.md             # Backend instructions
  frontend/
    components/
      TodoList.tsx
      Sidebar.tsx
    pages/
      index.tsx
      todos.tsx
    lib/
      api.ts
    CLAUDE.md             # Frontend instructions
  .mcp.json               # Managed MCP servers
  CLAUDE.md               # Project instructions

👤 Agents

/agents

Examples: weather reporter, front-end engineer, QA engineer.

✅ Fresh working memory per run 🎯 Scoped tools, model, permission ⚡ Parallelizable

root/
├── .claude/
│   └── agents/
│       └── weather-agent.md
└── README.md

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

Create your first agent — `/agents`

The Command

Type /agents.

Opens an interactive menu — pick "Create new agent" and the CLI drafts the agent file for you.

Creates .claude/agents/<name>.md — a plain markdown file anyone can edit.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

Demo

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

🎉 Yayyyyy! Problem solved with agents

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

?

Not so fast...

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

🚨 Problem Statement

✅ Single source of truth
The agent will always call the Open-Meteo API — no more source drift.
❌ Repetitively
It’s not guaranteed that Claude will always call this agent.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

Agent config fields with frontmatter

Field Description Enforced by

name Recommended Unique identifier — e.g. weather-agent harness

description Required When to invoke. Use "PROACTIVELY" for auto-invocation prompt

model haiku, sonnet, opus, or inherit (default). weather-agent uses sonnet harness

tools Allowlist of tools. weather-agent allows WebFetch, Read, Write, etc. harness

skills Skills preloaded into agent context at startup — weather-agent preloads weather-fetcher harness

maxTurns Maximum agentic turns before the agent stops. weather-agent uses 5 harness

memory Persistent memory: user, project, or local. weather-agent uses project harness

color Visual color in task list. weather-agent uses green harness

The skills: field is what makes the agent special. It preloads any-skill directly into the agent’s brain at startup — before the agent has received a single instruction.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

`claude-code-best-practice` Tips & Tricks

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

`claude-code-best-practice` Tips & Tricks

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

📝 CLAUDE.md

CLAUDE.md

Knowledge you provide to the model — read every session.

✅ Loaded into every session 👥 Team-shareable via git ⚠️ 200-line problem

root/
├── CLAUDE.md
└── README.md

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

Create your first CLAUDE.md — `/init`

The Command

Type /init in Claude Code.

Claude scans your codebase and drafts a starter CLAUDE.md for you — project conventions, key patterns, common commands.

Creates CLAUDE.md in your repo root — a plain markdown file you edit and commit.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

📝 CLAUDE.md — the real thing

A real CLAUDE.md file showing project rules and agent invocation instructions

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

`claude-code-best-practice` Tips & Tricks

Claude Code tips and tricks for CLAUDE.md

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

⚠️ CLAUDE.md problem — keep it under 200 lines

Illustration of the CLAUDE.md 200-line adherence problem

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

🎯 Skills

.claude/skills/

Examples: weather fetching, sorting CSV rows, generating SVG cards.

✅ Progressive disclosure — loaded on demand ♻️ Reusable across agents

root/
├── .claude/
│   └── skills/
│       └── weather-fetcher/
│           └── SKILL.md
└── README.md

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

Create your first skill

Write it by prompting

Just describe what you want Claude to do. It drafts the skill file for you.

"Create a skill that fetches weather from Open-Meteo for a given city."

Use Anthropic's skill-creator

Anthropic ships an official skill-creator skill. Invoke it and it walks you through generating a properly-structured skill.

Recommended — always produces the correct SKILL.md format.

⚠️ Watch out (method 1)

The prompting method sometimes creates the wrong structure. Instead of generating a folder with SKILL.md inside (e.g. weather-fetcher/SKILL.md), it creates a plain weather-fetcher.md file. The wrong form isn’t recognized as a skill by Claude Code.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

🎯 Skills — a real one

A real skill file on disk — SKILL.md inside a named folder inside .claude/skills/

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

Skill config fields with frontmatter

Most fields control how and when the skill loads — enforced by the harness. Only description lives in prompt-land.

Field Description Enforced by

name Recommended Display name and /slash-command (defaults to directory name) harness

description Required When to invoke — used for auto-discovery. Write it clearly. prompt

argument-hint Autocomplete hint shown in the / menu (e.g. [city-name]) harness

disable-model-invocation Set true to prevent Claude from invoking this skill automatically harness

user-invocable Set false to hide from / menu — background knowledge only harness

allowed-tools Tools allowed without permission prompts when this skill is active harness

model Model to use when skill is active: haiku, sonnet, opus harness

context Set to fork to run skill in an isolated subagent context harness

Small description, full body loaded on demand — this is progressive disclosure. Claude’s main context stays lean until the skill is actually needed.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

💭 Context

/context

The model's working memory — what it can see in this moment.

✅ Fresh every chat ⚠️ Dumb-zone problem ⚠️ /compact

20% of 1M tokens

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

🧠 Claude's Brain

Think of it like this

Imagine Claude has a brain that holds everything it's aware of right now — your question, every file it's opened, every tool result, every word it's said back to you. If a thought isn't in the brain, Claude can't use it. Simple as that.

Context window diagram showing how the 1M token limit is divided across system prompt, tools, files, and conversation

💬

Your messages Every question, clarification, and follow-up

📄

Every file Claude opens Read a 500-page PDF? All of it is in the brain now.

🔧

Every tool result Web searches, command output, screenshots — all held in memory

Two Things to Know

1. The brain is finite. It can hold about 1 million tokens — roughly 750,000 words. Big, but not infinite. 2. The brain empties at the end of every chat. When you start a new conversation, Claude remembers nothing from the last one unless you tell it again.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

What Loads at Session Start

The moment you open Claude Code, certain things land in Claude's brain before you've typed a word. The rest waits in the wings — only loaded when you actually need it. This is called progressive disclosure.

Diagram showing what loads into Claude's context window at session start

📋

CLAUDE.md — full content Standing instructions, every line. Always pinned in the brain at startup.

🎓

Skills — descriptions only Claude knows which skills exist and what each does — the full content loads when the skill is actually invoked.

👤

Agents — descriptions only The roster of specialists. The full system prompt loads when the agent is dispatched.

The One-Liner

Only descriptions of skills and agents are loaded at startup — the rest is fetched on-demand. That's progressive disclosure. It keeps the brain light.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

📄 Lost in the Middle

by Nelson F. Liu · Stanford University · 2023

Primacy & recency work. Information at the start of the context (what Claude sees first) and at the end (what Claude saw most recently) gets strong attention. These are the safe zones.
The middle is the dumb zone. Content buried in the middle of a long context effectively disappears. The model has the tokens, but doesn't weight them strongly during generation. It's in the working memory — but not really read.
Practical implication. Keep critical instructions either at the very top (in CLAUDE.md) or near the user's most recent message. A bigger context window doesn't help if your payload lands in the middle.

This is the "dumb-zone problem" the deck has been warning about — now you know where it came from.

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

📘 Workflow

.claude/commands/

Repeatable step-by-step recipes — the instruction manual that makes Claude run the same playbook every time.

✅ Reproducible recipes 🔁 Same steps, every run 👥 Team-shareable via git

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

📋 Orchestration Workflow

Command → Agent → Skill architecture flow

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

📹 Workflow in action

Claude running the weather orchestration workflow end-to-end

🧑‍💼 Agents — the specialists A dedicated Claude worker — own context, own tools, own focus. Each runs in isolation. ✅ fresh working memory per run

📝 CLAUDE.md — your memory Knowledge you provide to the model. Read every session. Keep it under 200 lines. ⚠️ 200-line problem

🎯 Skills — the know-how What the specialist (or Claude) can actually do. Loaded on demand — only when needed. ✅ progressive disclosure

💭 Context — the working memory What Claude holds in his head now — fresh every new chat. Finite budget; compact at ~50%. ⚠️ dumb-zone problem

📘 Workflows — the instruction manual Repeatable step-by-step recipes — like an AC install guide. Deterministic, reproducible. ✅ reproducible recipes

🙏 Thank you!

Questions? Feedback?

github.com/shanraisshan

Claude Code Best Practice

Shayan Rais

🚨 Problem Statement

Jargon you'll hear

Difference between GPT and ChatGPT?

What is Claude Code?

“Stochastic Parrots”

💬 Models are stateless

🧠 Models — e.g. Opus, GPT, Gemini

✅ Can answer

❌ Can’t answer

Opus 4.7 — Anthropic

GPT-5.5 — OpenAI

Gemini 3.1 Pro — Google

🧠 Limitations

⚡ Tool Calling — how the harness reaches the world

💪 Harness — the body around the brain

Agents — the specialists

Skills — the know-how

Workflows — the instruction manual

CLAUDE.md — your memory

Context — the working memory

💪 Harness — the body around the brain

Tools — the hands

MCP — the adapters

Permissions — the guardrails

Hooks — the reflexes

System Prompt — Claude’s memory

🎉 Yayyyyy! Problem solved with harness

💪 Non-determinism — Doesn’t always use its tools

💪 Non-determinism — Tools can fail

🚨 Problem Statement

Vibe Coding

Vibe Coding vs Agentic Engineering

👤 Agents

Create your first agent — /agents

The Command

Demo

🎉 Yayyyyy! Problem solved with agents

🚨 Problem Statement

Agent config fields with frontmatter

claude-code-best-practice Tips & Tricks

claude-code-best-practice Tips & Tricks

📝 CLAUDE.md

Create your first CLAUDE.md — /init

The Command

📝 CLAUDE.md — the real thing

claude-code-best-practice Tips & Tricks

⚠️ CLAUDE.md problem — keep it under 200 lines

🎯 Skills

Create your first skill

Write it by prompting

Use Anthropic's skill-creator

🎯 Skills — a real one

Skill config fields with frontmatter

💭 Context

🧠 Claude's Brain

Think of it like this

Two Things to Know

What Loads at Session Start

The One-Liner

📄 Lost in the Middle

📘 Workflow

📋 Orchestration Workflow

📹 Workflow in action

🙏 Thank you!

Create your first agent — `/agents`

`claude-code-best-practice` Tips & Tricks

`claude-code-best-practice` Tips & Tricks

Create your first CLAUDE.md — `/init`

`claude-code-best-practice` Tips & Tricks