一个命令：
autooptm

$ curl -fsSL https://autooptm.com/install.sh | sh
autooptm v0.1.0-beta… for linux/amd64
  checksum ok
  installed ~/.local/bin/autooptm (ao-cli is another name for it)

$ autooptm login
signed in as you@example.com; token saved to ~/.config/autooptm/token (valid 30 days, and it slides on use)

# a server with no browser: print the approval URL, open it anywhere
$ autooptm login --no-browser

# or the email code (QQ / 163 mail work): one command sends it, the next verifies
$ autooptm login --email you@example.com
$ autooptm login --email you@example.com --code 482913

$ autooptm balance
12.4 credits (≈ $12.4)
$ autooptm logout

$ autooptm keys create "CI pipeline" --scopes read,submit --expires-in-days 90
$ autooptm keys list
$ autooptm keys revoke <keyId>
$ export AUTOOPTM_TOKEN=ao_…

$ curl -fsSL https://autooptm.com/install.sh | sh
$ cd ~/my-project                  # with the env your program runs in active (conda / venv)
$ autooptm                         # the first time: approve this machine in the browser

> python train.py --epochs 1      # how you run it, or what you want, in your words

# a fresh machine: no torch yet, and the program needs it
> python infer.py --images ./samples
  ✗ CANNOT START: torch is not installed …  set it up now? › Yes   # → /env setup, in autooptm's own env

# the balance ran out and the session paused: top up, then carry on
> /resume

# step away: pause now, pick it up later (or on another day)
ctrl+c ctrl+c
$ autooptm
> /resume

# done: the patch is in the project
$ git apply autooptm-*.patch

$ autooptm run --cloud . --entrypoint train.py --workload training --gpu auto
skipped: .git (1), __pycache__ (14)
y = into the code archive · n = leave out · d = upload as the private dataset instead (sandbox only, 7 days, up to 1 GB, one per run)
  checkpoints/                 weight files: 2, 1.9 GB (.pt×2)  include? [y/N/d] n
  data/                        crowded directory: 1204 files, 310 MB (0 of 1204 files are code)  include? [y/N/d] d
  data/ goes up as the dataset, unpacked at ./data

packing 87 files, 1.4 MB before compression:
  src/                                         62 files     1.1 MB
  configs/                                     14 files      38 KB
  train.py                                      1 files      12 KB
dataset: /home/you/repo/data -> ./data in the sandbox
upload? [Y/n] y
uploading the code: 87 files, 412 KB compressed
uploading dataset [############################] 100.0%    310.0/310.0 MB
job 7f3c2a1b queued
  [00:00] queued (estimate)
estimate: expected 1.6x · range 1.2x - 2.3x · lossless 1.4x
next: autooptm decide 7f3c2a1b continue   (takes the deposit, credited against the unlock)
      autooptm decide 7f3c2a1b stop       (free)
continue? takes the analysis deposit (2 credits) [y/N] y
continuing: the deposit is taken and the job is queued for a GPU
  [00:52] running (run) · preparing measurements
  …
1.61x  (937.8s -> 582.5s)
patch: locked. unlock for 4.6 credits (autooptm unlock 7f3c2a1b)
report: https://api.autooptm.com/api/my/reports/…

$ autooptm unlock 7f3c2a1b
Unlock spends 4.6 credits. Continue? [y/N] y
charged 4.6 credits; patch -> autooptm.patch
apply with: git apply autooptm.patch
$ git apply autooptm.patch

# only show the list; nothing leaves the machine
$ autooptm run --cloud . --entrypoint train.py --dry-run

# answer from the command line instead of the prompts
$ autooptm run --cloud . --entrypoint train.py --include checkpoints --exclude outputs --yes

# send a directory as the dataset, unpacked at ./data in the sandbox
$ autooptm run --cloud . --entrypoint train.py --dataset ./data --dataset-path data --yes

$ curl -fsSL https://autooptm.com/install.sh | sh
$ autooptm login
$ autooptm install-skill
installed autooptm-optimize -> ~/.claude/skills/autooptm-optimize/SKILL.md

# somewhere else, e.g. a project-level skills directory
$ autooptm install-skill --dest ./.claude/skills