lean-claude

name	lean-claude
description	Activate token-efficient mode: caveman output, right model selection, MCP discipline, compaction strategy, cavecrew agents — all in one

Lean Claude Skill

When to activate

• Starting any session where cost or speed matters
• Long sessions where context is getting bloated
• Running multiple parallel agents or batch workloads
• Working on a tight token budget
• Before a complex multi-step task that will consume a lot of context

When NOT to use

• Writing external-facing documentation — brevity can hurt clarity
• Security decisions, irreversible actions, or multi-step sequences where misreading a fragment causes damage
• Onboarding a new team member to a codebase

Instructions

Paste this activation prompt at the start of any session to enable all lean optimisations at once:

Activate lean mode for this session:

OUTPUT: caveman full — drop articles, use fragments, short synonyms.
Auto-revert to full prose for: security warnings, irreversible actions,
multi-step sequences where fragment ambiguity is risky.

MODEL: use Haiku 4.5 for any task where output is constrained and verifiable
(linting, formatting, simple renames, single-function edits, classification).
Stay on Sonnet 4.6 for multi-file reasoning. Only escalate to Opus for deep
architectural decisions or complex security analysis.

CONTEXT: do not read full files when a line range will do. Prefer targeted
reads over whole-file reads. Tell me before reading any file >500 lines.

AGENTS: for repetitive or isolated tasks, spawn a Haiku subagent rather than
doing the work in the main session. Each subagent gets a fresh context window.

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1. Model selection — the biggest lever

Task	Model	Savings
Linting, formatting, simple renames	Haiku 4.5	~60% vs Sonnet
Single-function edits, boilerplate generation	Haiku 4.5	~60%
Multi-file changes, debugging, code review	Sonnet 4.6	baseline
Architecture decisions, security analysis	Opus 4.7	— (pay for it)
Classification, routing, extraction at scale	Haiku 4.5	~60%

Rule: Default to Sonnet 4.6. Drop to Haiku when the output is constrained and verifiable. Only escalate to Opus when you genuinely need deep reasoning.

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2. Caveman output compression

Instructs Claude to respond in terse fragment-style prose. Benchmarked at ~65% output token reduction with 100% technical accuracy maintained.

Compression levels:

Level	Rule	Example
lite	Drop filler, keep full sentences	"The function handles edge cases."
full (default)	Drop articles, fragments OK	"func handles edge cases"
ultra	Abbreviate, strip conjunctions, arrows	"fn→edge cases handled"

Activate: add caveman full to your session prompt (already included in the activation prompt above).

Compress your memory files — files Claude re-reads every session become input tokens:

/caveman-compress .claude/memory/project-context.md

~46% input token savings per session on compressed files. Full implementation: github.com/JuliusBrussee/caveman

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3. MCP discipline — 30k tokens before you type a word

Each enabled MCP server loads all its tool definitions into context at session start. 10 MCP servers ≈ 80 tools ≈ 30,000 tokens consumed before you type a word.

Audit your active MCPs:

# Check what's enabled
cat ~/.claude/settings.json | grep -A2 mcpServers
cat .claude/settings.json | grep -A2 mcpServers 2>/dev/null

Disable any server you won't use in this session. Disabling 5 unused MCP servers saves ~15,000 tokens — more than most CLAUDE.md files consume.

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4. Context management

CLAUDE.md:

• Keep project CLAUDE.md under 300 lines
• Remove rules that no longer apply
• Never duplicate user-level rules in project CLAUDE.md

File reads:

• Ask for specific line ranges: "read auth.py lines 45–90" not "read auth.py"
• Avoid reading the same large file twice
• Use subagents for tasks that require reading many files not needed by the main session

Compaction — trigger it early, not at 95%:

/compact

Compact before switching to a new major task, after a long debugging session, or before starting work that requires reading many large files. Don't wait for automatic compaction.

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5. Cavecrew — cheap agents for cheap tasks

Spawn Haiku-based subagents for bounded tasks instead of burning Sonnet context:

Role	Model	Use for
Investigator	Haiku 4.5	Locate files, grep codebase, read-only tasks
Builder	Sonnet 4.6	Surgical 1–3 file changes
Reviewer	Haiku 4.5	Review a diff or file for issues
Orchestrator	Opus 4.7	Complex multi-step coordination only

~60% token savings vs using Sonnet for every subagent. Use Haiku for anything where the task is constrained and the output is verifiable.

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6. Prompt efficiency

Instead of	Use
"Fix the authentication"	"Fix JWT expiry check in auth/middleware.py:45 — not rejecting expired tokens"
Five separate "add a test for X"	"Add tests for all five functions in utils.py"
"Explain this codebase"	"Explain how auth flows from login to session creation, max 3 paragraphs"
Long back-and-forth	Batch related tasks into one prompt

Vague prompts → exploration → more tool calls → more context consumed. Specific prompts → focused responses → fewer tokens.

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7. Session cost tracking

Use the cost-tracker hook to see token usage per tool call:

npx claudient add hooks
# Then add hooks/lifecycle/cost-tracker.sh to .claude/settings.json

Gives you a running log of input/output tokens + estimated cost per session. Use it to identify which tasks consume the most — then optimise those first.

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Example

User: "Activate lean mode and fix the auth middleware"

Claude (lean mode):
→ Haiku 4.5 (constrained task)
→ Read auth/middleware.py:40-80 (line range, not full file)
→ Fix: reject expired tokens, add 401 response
→ Output: "Fixed. 3 lines changed. Rejects tokens past exp claim."
→ Tokens saved: ~60% vs standard Sonnet response

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Quick reference card

Situation	Action
Starting any session	Paste the activation prompt above
Simple edit, lint, format	Switch to Haiku 4.5
Memory files are large	Run caveman-compress on them
Context getting slow	/compact now, don't wait
Unused MCPs enabled	Disable them in settings.json
Repetitive task across files	Haiku subagent, not main session
Vague request, long response	Rewrite as specific, scoped prompt
Architecture / security decision	Escalate to Opus — worth the cost

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