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.

Token Savings: Why You Should Run These Scripts Yourself
========================================================

Running these scripts manually keeps their output out of your AI coding agent's
context window. Every line the agent doesn't have to read is a token you don't
pay for — and a token the agent can spend on the actual problem instead of on
deployment sequencing, SSH output, and Docker health checks.

This document reports two baselines side by side:

- You run it → Claude runs the script. What Claude ingests if it invokes the
  z-script as a single command. These figures are measured (see method below).
- You run it → Claude orchestrates raw. What Claude would ingest if the scripts
  didn't exist and it drove scp / ssh / docker compose step by step itself.
  These figures are estimates — the same command output plus the agent's
  reasoning and retry logic across every discrete step.

The savings from running a script yourself is the first column: if you run it,
Claude ingests zero. The extra value of having the scripts at all is the gap
between the two columns.

Dollar equivalents use a blended input/output rate: Sonnet 5 ≈ $9/1M | Opus 4.8 ≈ $15/1M | Fable 5 ≈ $30/1M

> Measurement note: "Measured" figures come from token-count.ps1, which runs
> each script under Start-Transcript and counts output characters ÷ 3.5
> chars/token. Captured in Claude Code (Sonnet 4.6) against the sp project.
> Strictly speaking that's measured output volume with estimated tokenization:
> ÷3.5 is a prose heuristic, and code-heavy output (paths, JSON, container IDs)
> fragments into more tokens per character under a real BPE tokenizer — so the
> token figures here are likely conservative. "Estimated (raw)" figures are not
> measured — they approximate manual orchestration and are marked est.
> throughout. Other models/interfaces tokenize differently.

---

Measured per-run output (script-run baseline)
---------------------------------------------

The bold figures below are real captures from token-count.ps1 sp; rows flagged est. are not:

| Script | Measured tokens/run | Notes |
|--------|--------------------:|-------|
| zec2online | 267 | reachability + version check |
| zec2 | 331 | EC2 TCP/HTTP + build match |
| zbackup_ec2 | 334 | pull server backup |
| zrepair | 364 | clean audit; more if it restarts containers |
| zkill | 377 | free the dev port |
| zbackup | 436 | local project snapshot |
| zrestart | 724 | kill + restart (detached) |
| zstart | 762 | start dev server (detached) |
| zsync | 769 | mirror backups offsite |
| zdeploy (cached) | ~810 | 53s deploy, layers cached |
| zdeploy (full rebuild) | ~34,600 est. | packages changed; streams full docker build |
| zstart_docker | not measured | est. ~500–1,500 |

Cache state is what drives zdeploy. A cached deploy is ~810 tokens; the
large number only appears on a full rebuild (dependencies changed), which streams
the entire docker build. During rapid deploy → test → fix iteration almost every run
is cached, so ~810 is the realistic per-run cost — with occasional spikes when you
change packages.

---

Local Development Control
-------------------------

zstart — Start dev servers
--------------------------
Measured: ~762 tokens/run | est. raw orchestration: ~1,500–3,000 | typical 2–3 runs/day

Run it yourself and Claude sees none of the version-bump, MOTD, and startup output.
If Claude started the server raw, it would also wait on health checks and confirm
the port is listening — reasoning the script does deterministically.

    zstart viteapp               # start Vite dev server on its configured port
    zstart pyapp -Port 3000      # override the port
    zstart nextapp -Detached     # start in background, prompt returns

---

zkill — Stop dev servers
------------------------
Measured: ~377 tokens/run | est. raw orchestration: ~1,000–2,000 | typical 2–3 runs/day

Raw, Claude would enumerate processes, kill them, and re-check the port is free.
The script collapses that to one command.

    zkill viteapp
    zkill pyapp nextapp

---

zrestart — Restart in one command
---------------------------------
Measured: ~724 tokens/run | est. raw orchestration: ~2,500–4,500 | typical 10–15 runs/day

The most-used command during rapid iteration. Raw, it's stop → wait → start with
error handling at each hop — several tool calls and their reasoning. As one script
it's a single call, and the -Detached switch now propagates correctly through the
kill→restart chain so the server backgrounds cleanly.

    zrestart viteapp
    zrestart pyapp -Detached

---

Build & Deployment
------------------

zdeploy — Deploy to EC2
-----------------------
Measured: ~810 tokens/run cached (spikes to ~34,600 on a full rebuild) | est. raw orchestration: ~5,000–12,000 cached, ~35,000+ full rebuild | typical 10–15 runs/day

The biggest lever — and the one where cache state matters most. The script streams
the docker/SSH output whether Claude runs it or not, so a cached deploy really is only
~810 tokens even through Claude. The raw-orchestration cost is higher not because of
extra output but because Claude would reason between ~15 discrete steps (zip, preflight
cleanup, scp, unzip, build, up, version bump, restart, verify) and handle retries
itself. Running it yourself zeroes out all of that.

Measured cached: three runs at 808 / 858 / 808 tokens (53–54s each). The full-rebuild
figure (~34,600) is an estimate for package-change deploys — treat it as the upper
bound.

    zdeploy pyapp -Note "Fix nav alignment"
    zdeploy edge                 # reload edge nginx config
    zdeploy all -Note "weekly release"

---

zstart_docker — Start local Docker stack
----------------------------------------
Not measured (est. ~500–1,500 tokens/run) | typical 1 run/day

One-time setup per session; doesn't need agent involvement.

---

Backup & Sync
-------------

zbackup — Backup projects locally
---------------------------------
Measured: ~436 tokens/run | est. raw orchestration: ~1,200–2,500 | typical 1–2 runs/day

Raw, Claude enumerates files, decides exclusions, compresses, and stamps timestamps.
You decide when to snapshot.

    zbackup                          # everything + scripts folder
    zbackup pyapp -Tag "pre-refactor"

---

zsync — Sync backups offsite
----------------------------
Measured: ~769 tokens/run | est. raw orchestration: ~1,500–3,000 | typical 1 run/day

Raw, Claude tracks file diffs, runs robocopy, and verifies the copy. You manage
cadence independently.

    zsync
    zsync viteapp                    # build + mirror dist to $env:ZSYNC_DEST

---

zbackup_ec2 — Pull backups from the server
------------------------------------------
Measured: ~334 tokens/run | est. raw orchestration: ~1,000–2,000 | typical 1 run/day

Separates database/app backup from code changes. Claude focuses on code; you manage
infrastructure snapshots.

    zbackup_ec2

---

Diagnostics & Troubleshooting
-----------------------------

zec2 — Check EC2 reachability
-----------------------------
Measured: ~331 tokens/run (zec2online: ~267) | est. raw orchestration: ~1,000–2,000 | typical 5–8 runs/day

When a deploy fails you run this first to confirm EC2 is reachable and the right
build is live — before asking Claude to debug. Raw, that's blind network diagnostics
over SSH. Runs frequently alongside zdeploy.

    zec2 viteapp
    zec2                             # check all projects
    zec2online sp                    # lightweight HTTP-only variant

---

zrepair — Audit & repair container routing
------------------------------------------
Measured: ~364 tokens/run (clean audit) | est. raw orchestration: ~2,000–4,000 | typical 1–2 runs/day

When a page 502s, this isolates routing vs. DNS vs. app logic across several
containers — rather than handing Claude an SSH session to figure out blind. The
364-token figure is a healthy run with nothing to repair; a run that actually
restarts containers emits more. Raw, Claude would SSH per container and reason
across each check.

    zrepair viteapp

---

Daily Token Savings Summary
---------------------------

Per-run × runs/day. The per-run figures are measured; the daily totals multiply
them by assumed typical run counts (midpoints) — zdeploy and zrestart at
10–15/day dominate the sum, so scale the total to your own cadence. The est. raw
column approximates what Claude would burn orchestrating the same work with no
scripts.

| Script | Measured/run | Runs/day | Measured/day | Est. raw/day |
|--------|-------------:|:--------:|-------------:|-------------:|
| zstart | 762 | 2–3 | ~1,900 | ~3,800–9,000 |
| zkill | 377 | 2–3 | ~940 | ~2,500–6,000 |
| zrestart | 724 | 10–15 | ~9,050 | ~31,000–68,000 |
| zdeploy (cached) | ~810 | 10–15 | ~10,100 | ~62,000–180,000 |
| zec2 (+online) | ~330 | 5–8 | ~2,200 | ~6,500–16,000 |
| zbackup | 436 | 1–2 | ~650 | ~1,800–5,000 |
| zsync | 769 | 1 | ~770 | ~1,500–3,000 |
| zbackup_ec2 | 334 | 1 | ~330 | ~1,000–2,000 |
| zrepair | 364 | 1–2 | ~550 | ~3,000–6,000 |
| Total (active dev day) | | | ~26,500 | ~115,000–295,000 est. |

The ~26,500/day figure is measured per-run at an assumed typical cadence —
reproducible on the per-run side, workflow-specific on the multiplier. It reflects an
active tool-development day of mostly cached deploys. The
~115k–295k est. upper figure is what it would cost to have Claude drive the raw
ssh/docker sequences instead — dominated by per-step reasoning on zdeploy and
zrestart, not by output volume. Treat that column as an **upper bound, not a
prediction**: a capable agent asked to deploy might well write its own wrapper
script and ingest very little — the counterfactual depends entirely on how the
agent chooses to work. A day with several full-rebuild deploys pushes the measured
figure higher too, since each rebuild streams ~34,600 tokens.

Daily dollar savings during active tool development:

Script output the agent ingests is billed at input rates, so the measured column
uses input pricing. The est.-raw column keeps the blended rate, because raw
orchestration also generates agent output (reasoning and tool calls between steps).

| Model | Measured/day @ input rate | Est. raw/day @ blended rate |
|-------|--------------------------:|----------------------------:|
| Sonnet 5 | ~$0.08 ($3/1M) | ~$1.04–$2.66 ($9/1M) |
| Opus 4.8 | ~$0.13 ($5/1M) | ~$1.73–$4.43 ($15/1M) |
| Fable 5  | ~$0.27 ($10/1M) | ~$3.45–$8.85 ($30/1M) |

One-time ingest slightly understates the true cost: tokens that enter the context are
re-sent on every later turn of the session (at cheaper cache-read rates when prompt
caching applies), so the cumulative figure is somewhat higher than a single ingest.

Over a ~22-day working month, the measured savings run ~$2–$6/mo (Sonnet →
Fable); the raw-orchestration estimate runs ~$23–$195/mo. The honest dollar
figure is small — the real currency is context: every infrastructure token kept
out of the window is context your agent keeps for the actual problem, and that's
worth more than the dollars suggest.

---

Claude Model Token Costs (July 2026)
------------------------------------

| Model | Input | Output | Typical use |
|-------|-------|--------|-------------|
| Haiku 4.5 | $1/1M | $5/1M | Quick edits, small changes |
| Sonnet 5 | $3/1M | $15/1M | Daily coding, medium complexity |
| Opus 4.8 | $5/1M | $25/1M | Complex reasoning, multi-file refactors |
| Fable 5 | $10/1M | $50/1M | Advanced reasoning, agentic workflows |

---

When to Run Scripts Yourself vs. Ask the Agent
----------------------------------------------

Run yourself when:
- ✅ You know exactly what action is needed
- ✅ The script is deterministic (same input = same output)
- ✅ You want to parallelize — run zstart while asking Claude for code
- ✅ You're troubleshooting and need fast feedback loops

Ask the agent when:
- ❌ You need conditional logic ("if this test fails, try X")
- ❌ You're chaining operations that depend on each other's output
- ❌ You want the agent to interpret script output and decide next steps

Bottom line: These scripts are optimized for you to run directly. Use them. Save
tokens. Let Claude focus on coding.