Audit your existing Claude Code agent setups for wasted spend in about 5 seconds
Get a recommended model tier for a specific task without calling any AI model
Reduce prompt cost by fixing model tier mismatches and missing prompt caching
Install it as a Claude Code skill so cost checks run automatically in new sessions
| epoko77-ai/tokensave | 0petru/sentimo | alingalingling/akasha-wechat | |
|---|---|---|---|
| Stars | 17 | 17 | 17 |
| Language | Python | Python | Python |
| Setup difficulty | easy | moderate | hard |
| Complexity | 2/5 | 3/5 | 4/5 |
| Audience | developer | developer | developer |
Figures from each repo's GitHub metadata at analysis time.
Tokensave is a cost-checking tool for people who build multi-agent setups using Claude Code, the AI coding assistant. Rather than telling you not to use multiple AI agents together, it helps you spot when a multi-agent setup is wasting money because of poor choices, and shows you how to fix it. The README and most documentation are in Korean, with an English mirror available. The author built tokensave after auditing their own 27 real Claude Code agent catalogs. They found that 99 percent of agents defaulted to the most expensive model tier, none of the 27 setups used prompt caching, and 20 agents handed clearly mechanical tasks, like exact text matching or converting a file format, to an AI model instead of a simple script. In one case, a task that could have run in about 30 seconds of Python code instead ran for 91 minutes on the most expensive model and produced no usable result. The overall structure of these agent teams was usually fine. The waste came from paying for expensive models on jobs that did not need them, then multiplying that cost across many linked steps. Tokensave addresses this with a classification system covering 10 categories and 31 specific waste patterns, a script that scans your existing agent files and flags problems in about 5 seconds, a small decision tool that recommends which model tier fits a task without calling any AI model itself, a lookup table matching task types to models, and guidance for structuring agent teams efficiently. It only uses Python's standard library, so no extra installs are needed beyond Python 3.10 or newer. It can be copied directly into a Claude Code skills folder so it activates automatically inside future sessions. A companion project by the same author checks the structural side of these agent setups, while tokensave focuses specifically on cost. The author notes that headline statistics come from a single person's own agent catalogs rather than a larger sample, and that the tool's cost estimates use pricing current as of May 2026, which may change over time.
Tokensave audits AI agent setups built with Claude Code to find wasted spending, such as using expensive models for simple tasks, and suggests cheaper alternatives.
Mainly Python. The stack also includes Python.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.