mirror of
https://github.com/kellymichels/zscripts-token-savers
synced 2026-10-06 07:08:17 +00:00
Kelly Michels
b26be3cd2a
* chore: write the site name as evomedia.net, lowercase The name is a domain and is written as one. Script headers, the README, CHANGELOG and elevator pitch, their .txt twins, and the site page -- matching the same sweep in the private evo.scripts so the mirror does not drift. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> * chore: refresh CHECKSUMS.txt for the lowercase sweep Every script's header changed, so every hash did. The repo's own Checksums test caught it -- which is what it is for. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
35 lines
2.7 KiB
Markdown
35 lines
2.7 KiB
Markdown
<!--
|
|
evomedia.net Token Savers — https://github.com/evomedia-net/evo.zscripts
|
|
Created by Kelly Michels · dev@evomedia.net
|
|
Licensed under the MIT License. See LICENSE.
|
|
-->
|
|
|
|
# Elevator Pitch
|
|
|
|
## The one-liner
|
|
|
|
**AI coding agents waste thousands of tokens a day on infrastructure orchestration. Token Savers gives you one-word commands to run those parts yourself — so your agent spends tokens on code, not on SSH.**
|
|
|
|
## The 30-second version
|
|
|
|
Every time your AI coding agent runs your infrastructure for you — a deploy, a restart, a health check — the full output lands in its context window: Docker layers, SSH banners, health-check chatter. We measured it per run — at a typical active-day cadence that's **~26,500 tokens of pure script output** through the agent, and a single full Docker rebuild adds ~35,000 more. The dollars are small; the context is not — every line of infrastructure noise crowds out the code your agent is supposed to be reasoning about.
|
|
|
|
Token Savers collapses the infrastructure side into short, one-word commands you run yourself: `zdeploy myapp`, `zrepair myapp`, `zstart myapp`. Describe each project once in `zconfig.json` — where it lives, what kind it is, where it deploys — and every command just knows. You run the deploy; your agent edits the code. You run the health check; your agent reads the result and fixes whatever's wrong.
|
|
|
|
**Measured per-run; ~26,500 tokens of script output per active development day at a typical cadence** — kept out of your agent's context entirely when you run the commands yourself. See [TOKEN_SAVINGS.md](TOKEN_SAVINGS.md) for the per-script measurements and method.
|
|
|
|
## Why it's different
|
|
|
|
- **Built around the AI-agent workflow.** The commands are short on purpose — fewer keystrokes for you, fewer tokens when an agent invokes them. But the real saving is the operations you *don't* hand to the agent at all.
|
|
- **The project name IS the command.** `zstart blog`, `zdeploy api`, `zbackup store` — no flags to memorize, no switches to wire up.
|
|
- **One config file, zero secrets in git.** Server IP, SSH key, paths, and project definitions live in one gitignored JSON. Clone it anywhere, drop in your config, go.
|
|
- **It verifies the deploy actually landed.** Not "did the server return 200" (a stale cache does that too) — it checks that the *build number* went live, so you know the code you just shipped is the code that's running.
|
|
|
|
## Who it's for
|
|
|
|
Solo developers and small teams running several containerized web apps (Python, Vite, Next.js, plus edge proxies and stock Docker images) on a single VPS or EC2 box, from a Windows dev machine, over SSH — and using AI coding agents to write the code.
|
|
|
|
## The tagline
|
|
|
|
*Fewer keystrokes. Fewer tokens. One config to rule your fleet.*
|