token-efficiency
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.
- Installs
- 212
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/rohitg00/pro-workflow --skill token-efficiency- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads token-efficiency automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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).
FAQ
What does the token-efficiency skill do?
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.
How do I install token-efficiency?
Run npx skills add https://github.com/rohitg00/pro-workflow --skill token-efficiency in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/token-efficiency/ (personal) or .claude/skills/token-efficiency/ (project).
Which AI agents can use token-efficiency?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is token-efficiency safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains token-efficiency?
It is published by rohitg00 in rohitg00/pro-workflow. It has 212 recorded installs.
More from rohitg00/pro-workflow
pro-workflow
rohitg00/pro-workflow
Complete AI coding workflow system. Orchestration patterns, 18 hook events, 8 agents, cross-agent support, reference guides, and searchable learnings. Works with Claude Code, Cursor, and 32+ agents.
wrap-up
rohitg00/pro-workflow
Open the skill to read its SKILL.md instructions.
deslop
rohitg00/pro-workflow
Open the skill to read its SKILL.md instructions.
learn-rule
rohitg00/pro-workflow
Open the skill to read its SKILL.md instructions.
context-optimizer
rohitg00/pro-workflow
Open the skill to read its SKILL.md instructions.
session-handoff
rohitg00/pro-workflow
Open the skill to read its SKILL.md instructions.
Popular productivity & automation skills
agent-browser
vercel-labs/agent-browser
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
lark-doc
open.feishu.cn
飞书云文档（Docx / Wiki）内容操作：读取、创建、编辑文档，插入或下载图片附件，以及操作思维笔记。用户提供文档 URL/token（包括 doubao.com 的 /docx/、/wiki/）时使用；按 URL 路径/token 而非域名路由；/page/ 是妙搭应用，非飞书云文档。文档内嵌资源按读取参考中的统一规则分流。独立评论操作走 lark-drive；随正文读取评论使用 docs +fetch。表格或 Base 内部数据操作不在本 skill。
lark-base
open.feishu.cn
飞书多维表格（Base）操作：建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式（BaseApp/AppMode 页面与组件）、Workspace 目录、workflow、角色权限、模板中心（多维表格模板分类/列表/搜索）；遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps；文件导入/导出转 lark-drive，认证/授权转 lark-shared。
lark-drive
open.feishu.cn
飞书云空间（云盘/云存储）：管理 Drive 文件和文件夹，包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签（secure labels）和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题，或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用；doubao.com 云空间 URL/token 也按资源路径和 token 路由，不回退 WebFetch。不负责：文档内容编辑（走 lark-doc）、表格/Base 表内数据操作（走 lark-sheets/lark-base）、知识空间节点/成员管理（走 lark-wiki）、原生 Markdown 文件读写/patch/diff（走 lark-markdown）。
lark-shared
open.feishu.cn
Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.
lark-im
open.feishu.cn
飞书即时通讯：收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片（Interactive Card）、监听卡片按钮回调（card.action.trigger）。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶（添加/移除/查询置顶会话）、管理标签数据、处理卡片回调时使用。