Mmcp.market

token-efficiency skill

by rohitg00·rohitg00/pro-workflow·2.9k stars

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.

A100/100content scan

Is the token-efficiency skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.

No findings.

Install the token-efficiency skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p ~/.claude/skills
cp -r /tmp/pro-workflow/skills/token-efficiency ~/.claude/skills/token-efficiency
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

Tool-Call Budgets

Set explicit budgets by task complexity:

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:

  1. Read all relevant files including tests first
  2. Understand what tests assert before coding
  3. Write complete solution in one pass — not incrementally
  4. Run tests once — if pass, STOP immediately
  5. If fail: read the error, fix once, retest
  6. Never iterate more than twice on the same failure — rethink approach
  7. 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:

  1. Never write a file you haven't read in this session
  2. Never re-read a file already read unless it was modified
  3. Read tests before coding — understand what passes before writing
  4. 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).

More skills from rohitg00/pro-workflow

  • Aagent-teamsCoordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.
  • Aauto-setupAuto-configure quality gates, hooks, and settings for a new project. Detects project type and sets up appropriate tooling. Use when onboarding a new codebase.
  • Abatch-orchestrationDecompose large-scale changes into independent units and spawn parallel agents in isolated worktrees. Use for migrations, refactors, codemods, and any change touching 10+ files with the same pattern.
  • Cbug-captureCapture a user-reported defect as a durable GitHub issue written in the project's own domain language. Explores the codebase in parallel for context but never leaks file paths or line numbers into the issue. Use when the user reports a bug conversationally, runs a QA pass, or says "file an issue", "log this as a bug", "capture this".
  • Acompact-guardSmart context compaction with state preservation. Saves critical files, task progress, and working state before compaction, restores after. Use before manual compact or when auto-compact triggers.
  • Acontext-engineeringMaster the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
  • Acontext-optimizerOptimize token usage and context management. Use when sessions feel slow, context is degraded, or you're running out of budget.
  • Acost-trackerTrack session costs, set budget alerts, and optimize token spend. Use to check costs mid-session or set spending limits.
  • Adesign-engineeringApply interface craft when building or reviewing UI - motion, easing, timing, springs, component feel, and visual foundations. Use when building a component, animation, transition, hover or press state, modal, drawer, toast, or when polishing an interface so it feels right. Says "make this feel better", "add an animation", "polish the UI", "review this component".
  • AdeslopRemove AI-generated code slop, unnecessary comments, and over-engineering from the current branch diff. Cleans up boilerplate, simplifies abstractions, strips defensive code, and in skill-file mode lints SKILL.md files for quality. Use when cleaning up code, simplifying, removing boilerplate, before committing, or when reviewing a skill before promoting it.
  • Adomain-modelingBuild the project's shared language and bounded contexts before writing code, so names stay consistent and the agent stops paraphrasing domain concepts. Produces a CONTEXT.md glossary and decision records. Use at the start of a project or feature, or when the codebase and the people describing it speak different languages.
  • Afile-watcherConfigure file watching hooks to auto-react to config changes, env file updates, and dependency modifications. Use to set up reactive workflows.

All agent skills → · MCP servers