Token Efficient

    2

    Force your AI agent to minimize token usage (-70% token usage across multiple scenarios), batch commands, and eliminate conversational filler for faster, cheaper ops.

    $9

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    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    token-efficient

    Example session with this skill installed

    Update the UserAuth class in src/api.py to include a logout method and run the tests. Keep it brief.

    • Read your context and instructions
    • Compiled the token-efficient

    $ grep -n "UserAuth" src/api.py
    12: class UserAuth:
    $ sed -n '12,25p' src/api.py
    [Code Block]
    Appended logout method to UserAuth. Tests passed (3/3).

    Connects securely to your tools. The creator never sees your data.

    About this skill

    What it does

    This skill transforms your AI agent into a high-efficiency developer that prioritizes low latency and minimal token consumption. It enforces strict constraints to eliminate conversational filler, avoid redundant file reads, and optimize tool usage through batching and targeted search.

    Why use this skill

    Standard LLM behavior is often verbose, re-explaining logic or re-reading files unnecessarily. This "token tax" compounds into significant costs and slower response times. This skill replaces that behavior with a "decide and act" philosophy. It ensures your agent only reads what it needs, only writes what changed, and executes multiple commands in single tool calls to maximize throughput while minimizing your API bill.

    Supported tools

    • File System: Optimized with partial reads (sed/head) and grep-first workflows.
    • Shell: Batching multiple bash commands and truncating noisy output.
    • Editing: Strict use of minimal diffs and str_replace over full-file rewrites.

    • May skip helpful explanations for beginners.
    • Aggressive truncation can hide relevant logs.
    • Risk of missing context with partial file reads.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Trust & safety

    Security scan

    Security scan passed 6 months ago

    Permissions

    Terminal / Shell
    Listed6 months ago

    What's inside

    Frequently Asked Questions

    This skill addresses the 'token tax' by forcing your agent to eliminate conversational fluff, batch multiple shell commands into single calls, and use targeted file-reading techniques like grep and sed instead of reading entire documents.

    Yes, the skill is compatible with any AI agent platform that utilizes standard file system and shell tools, such as Anthropic's Claude Dev, Open Interpreter, or custom-built agents.

    You receive the production-ready system configuration and optimized tool definitions; simply import them into your agent's configuration environment to activate the efficiency constraints.

    By minimizing token density per request and batching outputs, your agent can process complex tasks significantly faster, leading to lower latencies and a direct reduction in your API billing costs.

    Updates are included for the lifetime of the product version, ensuring compatibility as agent frameworks and LLM tool-calling capabilities evolve.

    Not at all; while it includes specific optimizations for bash and file editing, the core 'decide and act' logic improves the efficiency of nearly any text-based or code-heavy workflow.

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