token-efficiency

name token-efficiency description Reduce token waste by 40-60% through anti-sycophancy rules, tool-call budgets, one-pass coding, task profiles, and read-before-write enforcement. Inspired by drona23/claude-token-efficient. Token Efficiency Reduce output token waste and prevent iteration cycles that consume context. Trigger Use when: Sessions feel expensive or slow Output is verbose with filler text Claude is re-reading files or iterating unnecessarily Setting up a new project for token-efficient work Anti-Sycophancy Rules These patterns waste 30-60% of output tokens: Pattern Example Fix Sycophantic opener "Sure! Great question!" Delete. Lead with answer. Prompt restatement "You're asking about X..." Delete. Answer directly. Closing fluff "Let me know if you need anything!" Delete. Stop after the answer. Unsolicited suggestions "You might also want to..." Delete unless asked. AI disclaimers "As an AI model..." Delete entirely. Verbose preambles "I'll help you with that..." Delete. Start with the action. Tool-Call Budgets Set explicit budgets by task complexity: Task Type Tool-Call Budget Wrap-Up At Quick fix / lookup 20 calls 15 Bug fix 30 calls 25 Feature (small) 50 calls 40 Feature (large) 80 calls 65 Refactor 50 calls 40 Exploration / research 30 calls 25 At the wrap-up threshold: commit progress, assess remaining work, decide whether to continue or start fresh. One-Pass Coding Discipline For simple-to-medium tasks: Read all relevant files including tests first Understand what tests assert before coding Write complete solution in one pass — not incrementally Run tests once — if pass, STOP immediately If fail : read the error, fix once, retest Never iterate more than twice on the same failure — rethink approach Never refactor, improve, or polish passing code Task Profiles Switch profiles based on what you're doing: Coding Profile Return code first, explanation after (only if non-obvious) Simplest working solution, no over-engineering Read file before modifying — always No docstrings on unchanged code No error handling for impossible scenarios State bug, show fix, stop Agent/Pipeline Profile Structured output only: JSON, bullets, tables No prose unless targeting a human reader Every output must be parseable without post-processing Execute task, do not narrate actions Never invent file paths, API endpoints, or function names If unknown: return null or "UNKNOWN", never guess Analysis Profile Lead with finding, context and methodology after Tables and bullets over prose Numbers must include units Never fabricate data points Summary first (3 bullets max), caveats last Read-Before-Write Enforcement Hard rules: Never write a file you haven't read in this session Never re-read a file already read unless it was modified Read tests before coding — understand what passes before writing Read error output carefully before attempting a fix ASCII-Only Output Use ASCII characters only in all output: -- not — (em dash) " not " " (smart quotes) ' not ' ' (curly apostrophes) No emoji unless explicitly requested No Unicode decorators or special characters This ensures clean copy-paste for code and compatibility with downstream systems. Measuring Impact Track these metrics to measure token savings: Output length : average words per response (target: 30-50% reduction) Tool calls per task : should stay within budget tier Re-read count : should be near zero Write-without-read count : should be zero Iteration cycles : tests should pass in 1-2 attempts, not 5+ Attribution Token efficiency patterns adapted from drona23/claude-token-efficient (MIT).