Token economy

Three things drive cost in Claude Code: context size, cache hit rate, and model choice. Most waste comes from one of these being misconfigured and left alone.

Model tiering — match the model to the task

Claude Code's /fast or model selection lets you pick. Don't use Opus by reflex for a typo fix.

Cache hygiene — the 80% lever nobody talks about

Anthropic caches your prompt prefix for 5 minutes. A stable prefix = the next turn reads from cache at ~10% of input cost. A prefix that changes each turn = full price every time.

What busts cache:

• Timestamps in CLAUDE.md, in skill content, or in hook output
• Session IDs / UUIDs interpolated into system prompt
• Rotating "quotes of the day", random greetings
• MCP servers that return time-varying schema metadata

What preserves cache:

• CLAUDE.md that doesn't change day-to-day
• Skill/rule content that's additive, not mutating
• Hook additionalContext that's deterministic for a given input
• Turning off MCP servers you don't need (every schema change invalidates cache)

How to check: /holocron:cost — the cache hit rate line. Below 50% is a signal; above 70% is healthy.

Read discipline — the most common leak

Claude's Read tool pulls whole files by default. A 2000-line file is ~8k tokens. Do that 5 times in a session and you've spent ~40k tokens reading the same file repeatedly.

Pattern:

1. Grep to find the relevant range (cheap — returns only matching lines)
2. Read with offset + limit for the range you identified

Holocron's read-budget.js hook nudges this on files > 500 lines, but the discipline has to be yours.

MCP pruning — one-time setup, ongoing savings

Every MCP server's tool schemas ship to the model on every turn. An unused MCP server costs you tokens forever.

Audit with /holocron:mcp-audit. Disable servers you don't actively use. Two or three MCP servers is usually plenty; ten is a tax.

Subagent isolation — don't pollute the main context

Research / exploration / "find me all the callers" work bloats the main transcript with files and noise you won't need after. Run it in a subagent:

Use the Explore agent to find how `authenticateUser` is called across the repo.

The subagent's findings come back as a summary; its exploration stays in its own context. Main session stays lean.

Transcript discipline

Claude Code auto-compacts near the context limit, but compaction is lossy — it summarizes, and summaries forget things. Better:

• /holocron:handoff at logical boundaries — write state to a file, start fresh on the next task
• For long-lived work: /holocron:plan → plan file → execute → handoff. The plan file acts as durable memory.

Subtle things

• Large tool outputs stay in the transcript. A single 20k-token tool result inflates every subsequent turn's input cost until compaction.
• Write of a large file re-reads context. Edit prefers over Write for big files.
• Don't paste log output. Summarize, grep, or point to a file.
• Emojis are multi-token. Harmless unless you're generating content heavy in them.

Anti-patterns

• Running Opus on every turn "to be safe"
• Full-file Reads on generated/vendored files (dist/, node_modules/, generated protobufs)
• Keeping MCP servers installed that you tested once and never used again
• Timestamp-injecting SessionStart hooks (looking at you, naive console.log(new Date()) in a hook that emits additionalContext)

How to know it's working

Run /holocron:cost weekly. Healthy numbers:

• Cache hit rate > 60% (70%+ if you're disciplined)
• Median session cost under your per-session budget
• Per-model split skews Sonnet-heavy, with Haiku for small things, Opus for reasoning

If those aren't true, @cost-analyst can tell you why.