Claude Code Longform Guide

Overview

Advanced techniques for productive Claude Code sessions, covering token economics, memory persistence, verification patterns, parallelization strategies, and the compound effects of building reusable workflows. Builds on the Shortform Guide — assumes skills, agents, hooks, and MCPs are already configured.

--------|-------|-----| | Exploration/search | Haiku | Fast, cheap, good enough for finding files | | Simple edits | Haiku | Single-file changes, clear instructions | | Multi-file implementation | Sonnet | Best balance for coding | | Complex architecture | Opus | Deep reasoning needed | | PR reviews | Sonnet | Context understanding, catches nuance | | Security analysis | Opus | Can't afford to miss vulnerabilities | | Writing docs | Haiku | Structure is simple | | Debugging complex bugs | Opus | Needs entire system in mind |

Default to Sonnet for 90% of coding tasks. Upgrade to Opus when: first attempt failed, task spans 5+ files, architectural decisions, or security-critical code.

Tool-Specific Optimizations

Replace grep with mgrep — ~50% token reduction on average. Use modular codebases with files in hundreds (not thousands) of lines.

Context & Memory Management

Cross-Session Memory

Create a skill/command that summarizes progress and saves to a .tmp file in .claude/. Each session creates a new file. The file should contain:

• What approaches worked (with evidence)
• What was tried but didn't work
• What remains to be done

Strategic Context Clearing

After setting your plan, clear exploration context. Disable auto-compact; manually compact at logical intervals.

Dynamic System Prompt Injection

claude --system-prompt "$(cat memory.md)"

# Aliases for different modes
alias claude-dev='claude --system-prompt "$(cat ~/.claude/contexts/dev.md)"'
alias claude-review='claude --system-prompt "$(cat ~/.claude/contexts/review.md)"'

System prompt content has highest authority, followed by user messages, then tool results.

Memory Persistence Hooks

• PreCompact Hook: Save important state before context compaction
• Stop Hook: Persist learnings at session end
• SessionStart Hook: Auto-load previous context on new session

Continuous Learning

When you repeat the same prompt and Claude hits the same problem — those patterns must be appended to skills.

Design decision: Use a Stop hook (not UserPromptSubmit). UserPromptSubmit adds latency to every message. Stop runs once at session end — lightweight, no slowdown during sessions.

Verification Loops & Evals

Benchmarking Skills

Fork the conversation, create a worktree without the skill in one branch, compare diffs at the end.

Eval Pattern Types

• Checkpoint-Based: Set explicit checkpoints, verify against criteria, fix before proceeding
• Continuous: Run every N minutes or after major changes (full test suite + lint)

Key Metrics

pass@k: At least ONE of k attempts succeeds
        k=1: 70%  k=3: 91%  k=5: 97%

pass^k: ALL k attempts must succeed
        k=1: 70%  k=3: 34%  k=5: 17%

Use pass@k when you need it to work. Use pass^k when consistency is essential.

Parallelization

Principles

• Main chat for code changes, forks for questions/research
• Goal: maximum output with minimum viable parallelization
• Use Git worktrees for overlapping parallel Claude instances
• Name all chats with /rename

Git Worktrees

git worktree add ../project-feature-a feature-a
git worktree add ../project-feature-b feature-b
# Each worktree gets its own Claude instance

The Cascade Method

• Open new tasks in new tabs to the right
• Sweep left to right, oldest to newest
• Focus on at most 3-4 tasks at a time

Groundwork: The Two-Instance Kickoff

Instance 1 (Scaffolding Agent): Project structure, configs (CLAUDE.md, rules, agents)

Instance 2 (Deep Research Agent): Service connections, detailed PRD, architecture diagrams, documentation references

llms.txt Pattern

Append /llms.txt to documentation URLs for clean, LLM-optimized text.

Philosophy: Build Reusable Patterns

Invest in: subagents, skills, commands, planning patterns, MCP tools, context engineering patterns. Tedious to build, but produces a wild compounding effect.

Agents & Sub-Agents Best Practices

The Sub-Agent Context Problem

Sub-agents save context by returning summaries, but they lack the orchestrator's semantic context. Pass objective context, not just the query.

Iterative Retrieval Pattern

1. Orchestrator evaluates every sub-agent return
2. Ask follow-up questions before accepting
3. Sub-agent goes back to source, returns answers
4. Loop until sufficient (max 3 cycles)

Orchestrator with Sequential Phases

Phase 1: RESEARCH (Explore agent) → research-summary.md
Phase 2: PLAN (planner agent) → plan.md
Phase 3: IMPLEMENT (tdd-guide agent) → code changes
Phase 4: REVIEW (code-reviewer agent) → review-comments.md
Phase 5: VERIFY (build-error-resolver if needed) → done or loop back

Rules: Each agent gets ONE input and produces ONE output. Outputs become inputs for the next phase. Never skip phases. Use /clear between agents. Store intermediate outputs in files.

Resources

• claude-flow — Enterprise orchestration with 54+ agents
• continuous-learning skill
• Session reflection pattern
• System prompts collection (110k stars)
• Anthropic Academy: anthropic.skilljar.com