Claude Code Cost & Context Pack
Three local-first skills that attack the same problem from different angles: your AI coding sessions cost more than they should. 1. lean-context Compresses code before the model reads it. AST-aware Python pruning, low-token repo skeletons, git diff summaries, and a token-budget guard. 30-50% fewer tokens on typical read-heavy sessions. 2. Token-Guard Cuts agent input-token cost and stops runaway loops with cache-aware compaction, non-destructive offload, and deterministic loop guards. 3. data-pipeline-agent Turns any API or CSV into a ready-to-query SQLite database in minutes, with zero-config schema inference and type validation. Keeps the data work local. Why bundle them Individually, each saves tokens or time. Together they cover the full cost surface of an agent workflow: what the model reads (lean-context), what it re-sends (Token-Guard), and what it has to wrangle before it can help (data-pipeline-agent). All three are read-only or local, require no hosted account, and install by dropping a SKILL.md folder into your agent. Python 3.8+. 30-day refund.
Works with every agent that reads SKILL.md — Claude Code, Cursor, Codex CLI, Gemini CLI, GitHub Copilot, Windsurf, OpenClaw, and more.
One payment, lifetime access. 3 skills unlock instantly in your library.
What's included
3 skills
Cut Claude Code and Cursor token costs by 30-50% with AST-aware context compression, skeleton extraction, and budget guards.
Cut agent input-token cost up to 80% and hard-stop runaway loops with cache-aware compaction, non-destructive offload, and deterministic loop guards.
Turn any API or CSV into a ready-to-query SQLite database in minutes. Zero-config schema inference, type validation, and optional RAG vectors.
See it in action
lean-context
Example session with this skill installed
I need to understand the structure of the core engine in this repo, but the files are huge. Help me map it out without hitting context limits.
- Read your context and instructions
- Compiled the lean-context
I have scanned the repository and generated a compressed map of the core engine. I filtered out all build artifacts and pruned Python files to their signatures to stay within your token budget. ## Repository Context Summary | File Path | Original (Tokens) | Compressed (Tokens) | % Saved | | :--- | :--- | :--- | :--- | | `engine/core.py` | 1,840 | 420 | 77% | | `engine/utils/math.py` | 950 | 180 | 81% | | `engine/handlers/events.py` | 1,200 | 310 | 74% | | **Total** | **3,990** | **910** | **77%** | ```python # engine/core.py (Compressed AST View) class ExecutionEngine: """Main orchestrator for task execution.""" def __init__(self, config: dict): ... def register_handler(self, name: str, handler: Callable): """Registers a new event handler to the engine.""" ... def run_sync(self, task_id: str) -> Result: """Executes a task synchronously and returns the Result object.""" ... class Result: def __init__(self, status: str, data: Any): ... ``` ```text . └── engine/ ├── __init__.py ├── core.py ├── handlers/ │ ├── __init__.py │ └── events.py └── utils/ ├── __init__.py └── math.py ``` **Next steps** 1. Would you like me to expand the full body of a specific method in `core.py`? 2. I can perform a `lean-context` diff if you have pending changes to these files. 3. Type `show me the structure` at any time to refresh this map.
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 these skills for you. If a payment is needed, your agent shows it to you first.
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