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    AntiGravity Starter Kit — Context Optimizer & Lean Core

    1

    Eliminates context bloat, audits rule files for token bleed, and enforces the under-40-line Lean Core standard.

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    AntiGravity Starter Kit — Context Optimizer & Lean Core

    AntiGravity Starter Kit — Context Optimizer & Lean Core

    Example session with this skill installed

    Audit my 450-line AGENTS.md and .cursorrules file for context bloat, identify token waste per conversation turn, and convert it to the Lean Core standard.

    • Read your context and instructions
    • Compiled the antigravity starter kit

    Rule File Token Audit

    • Original Root Rule Size: 462 lines (14,800 tokens per turn)
    • Attention Degradation Risk: 🔴 HIGH (Lost-in-the-Middle trigger detected)
    • Token Bleed Projection: ~296,000 wasted tokens over a 20-step debugging session

    Refactored Lean Core Architecture:

    1. Root AGENTS.md: Stripped down to 24 lines of universal invariants (720 tokens).
    2. Modular References (references/): 4 specialized markdown files created for on-demand loading.
    3. Subagent Router (rules/model_routing.md): Configured for automatic task delegation.
    
# AGENTS.md — Lean Core Standard (<30 lines)
1. Invariant: Maintain 100% test pass rate before committing code.
2. Routing: Load specialized references on-demand using view_file.
3. Subagents: Heavy refactoring runs in isolated subagents (context: fork).
    AGENTS.md

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    About this skill

    AntiGravity Starter Kit — Context Optimizer & Lean Core Rules

    Tired of your AI coding assistant forgetting earlier instructions, burning tokens at lightning speed, or drifting into hallucinations?

    The AntiGravity Starter Kit diagnoses and solves Context Bloat across Claude Code, Cursor, Windsurf, OpenClaw, Codex CLI, and Google Antigravity.

    ⚠️ The Problem: Monolithic Instructions Degrade LLMs

    When your root rule files (AGENTS.md, .cursorrules, CLAUDE.md) exceed 80–100 lines:

    1. Attention Degradation (Lost-in-the-Middle): The LLM dilutes constraints, causing the assistant to violate safety rules or repeat mistakes.
    2. First-Token Latency (TTFT) & Token Waste: Every turn re-reads the full system prompt. Over 125,000 wasted tokens in a 50-step session.
    3. Premature Context Overflow: Heavy instructions push the session into early context exhaustion.

    ⚡ The Solution: The AntiGravity 3-Tier Lean Core (<40 lines)

    • Tier 1: Lean Root Core (<40 lines): Universal behavioral invariants only.
    • Tier 2: Modular Reference Modules: Domain knowledge loaded on-demand from references/.
    • Tier 3: Isolated Subagents (context: fork): Heavy refactoring in sandboxed sub-windows.

    📦 What's Included:

    • scripts/token_inspector.py (CLI diagnostic audit)
    • assets/AGENTS.template.md (18-line production starter)
    • references/ (Architecture guides and recipes)
    • agents/openai.yaml (Codex CLI metadata)

    🔒 100% Local-First & Zero Telemetry

    Runs entirely on your local machine using your existing CLI environment. Zero data telemetry, zero cloud egress.

    What's included (2 skills)

    • antigravity-starter-kit

      Optimizes AI agent context, audits instructions for token bloat, and enforces the ultra-lean rules architecture (<40 lines) across Cursor, Windsurf, Claude Code, and Antigravity. Use when the user asks to reduce token usage, optimize agent context, audit rules for bloat, inspect prompt size, or set up lean assistant rules.

    • antigravity-starter-kit

      Optimizes AI agent context, audits instructions for token bloat, and enforces the ultra-lean rules architecture (<40 lines) across Cursor, Windsurf, Claude Code, and Antigravity. Use when the user asks to reduce token usage, optimize agent context, audit rules for bloat, inspect prompt size, or set up lean assistant rules.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Python

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