cheap-recall

Implement tiered cached lookups and token budget constraints for knowledge retrieval.

Updated May 11, 2026

Search Tags:#caching #embeddings #rag #retrieval #token-optimization #cost-efficiency

Ready to run

npx skills add https://github.com/AesopScott/mojo --skill cheap-recall

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cheap-recall
Source: https://github.com/AesopScott/mojo/tree/main/harnesses/skills/cheap-recall
Command: npx skills add https://github.com/AesopScott/mojo --skill cheap-recall

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses excessive token consumption and compute waste during knowledge retrieval by enforcing strict boundaries on search breadth and prioritizing cached or low-cost data sources.

Core Features & Use Cases

Cost-Aware Retrieval: Implements a tiered search strategy that attempts cheap, cached lookups before triggering expensive broad searches.
Context Budgeting: Automatically bounds the amount of retrieved information to fit within defined token limits, preventing runaway costs.
Use Case: When building a RAG system, use this to ensure that the retrieval process stays within a specific budget by prioritizing high-confidence cached embeddings over full-index scans.

Quick Start

Use the cheap-recall skill to audit the current knowledge retrieval harness and apply a cost-efficiency constraint to the search process.

Frequently Asked Questions about cheap-recall

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce token usage during RAG knowledge retrieval?▼

To reduce token usage during RAG knowledge retrieval, implement cost-aware search strategies that attempt cached embedding lookups before triggering expensive broad index scans, bounding the retrieved context to fit defined token limits.

What is context budgeting in AI-driven information systems?▼

Context budgeting in AI-driven information systems automatically bounds the amount of retrieved information to fit within predefined token limits, preventing runaway compute costs and ensuring deterministic, cost-controlled data access.

How do I apply a cost-efficiency constraint to a knowledge retrieval harness?▼

Apply a cost-efficiency constraint to a knowledge retrieval harness by auditing the current process and enforcing a tiered search strategy that prioritizes high-confidence cached embeddings over full-index scans.

Does cached lookup work with RAG systems to lower compute overhead?▼

Cached lookup works with RAG systems to lower compute overhead by serving as a cheap, high-confidence preliminary data source, ensuring expensive full-index scans are only triggered when cached data is insufficient.

What is the best way to prevent runaway costs in AI knowledge retrieval?▼

The best way to prevent runaway costs in AI knowledge retrieval is implementing a tiered search strategy that enforces strict boundaries on search breadth and prioritizes low-cost cached data sources.

When should I not use cost-aware retrieval strategies?▼

You should not use cost-aware retrieval strategies when your application requires exhaustive full-index scans or unconstrained search breadth, as context budgeting enforces strict, deterministic boundaries on retrieved information.