agy
Skill0 starsChanged 34 days agoMITView on GitHub
Generate and iterate images with Google Antigravity's agy CLI in headless mode, and script non-interactive agent runs with machine-readable output. Use whenever the user asks for a generated or edited image (illustration, texture, icon, concept art, variation), mentions agy or Antigravity, or wants an agent run from a script, a batch or CI.
10 other projects use a version of this.
Adopted by
Most recently taken up by
- d4rkwinz/agy-plugintheir own version, first seen Nov 8, 2026
- fmind/dottheir own version, first seen Nov 8, 2026
- fmind/dotfilestheir own version, first seen Nov 8, 2026
- letitbk/claude-academic-setuptheir own version, first seen Nov 1, 2026
- mustafarslan/skillstheir own version, first seen Nov 1, 2026
- darshitpp/x-agenttheir own version, first seen Oct 13, 2026
Used with
Projects with it also use codex.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
engtySkillcodex
Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing
Also in 4 projects that use this
Other versions10 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 10
- letitbk/claude-academic-setup★ 48same name
- fmind/dot★ 13same name
- fmind/dotfiles★ 13same name
- phuongduyphan/cc-tmux-agents★ 8same name
- darshitpp/x-agent★ 2same name
- mustafarslan/skills★ 2same name
- shauryaaojha/claude-code-delegate★ 2same name
- ssv445/claude-skills★ 2same name
- d4rkwinz/agy-plugin★ 1same name
- dT-Tb-labs/babel-orchestration★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository58 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agySkill
- animate-expoSkill
- animateSkill
- animation-performanceSkill
- animation-vocabularySkill
- apple-designSkill
- ask-sonnerSkill
- break-uiSkill
- docs-fishingSkill
- emil-design-engSkill
- explainerSkill
- ffmpeg-skillSkill
- find-animation-opportunitiesSkill
- frontend-designSkill
- good-cssSkill
- improve-animationsSkill
- minimal-change-engineerSkill
- mobile-nativeSkill
- moli-webfetchSkill
- pick-ui-librarySkill
- project-dashboardSkill
- prototypeSkill
- reality-checkerSkill
- review-animationsSkill
- rust-refactoringSkill
- skill-creatorSkill
- using-superpowersSkill
- voice-ai-engineeringSkill
- write-swiftSkill
- animate-expoSkill
- animateSkill
- animation-performanceSkill
- animation-vocabularySkill
- apple-designSkill
- ask-sonnerSkill
- break-uiSkill
- docs-fishingSkill
- emil-design-engSkill
- explainerSkill
- ffmpeg-skillSkill
- find-animation-opportunitiesSkill
- frontend-designSkill
- good-cssSkill
- improve-animationsSkill
- minimal-change-engineerSkill
- mobile-nativeSkill
- moli-webfetchSkill
- pick-ui-librarySkill
- project-dashboardSkill
- prototypeSkill
- reality-checkerSkill
- review-animationsSkill
- rust-refactoringSkill
- skill-creatorSkill
- using-superpowersSkill
- voice-ai-engineeringSkill
- write-swiftSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.
0
Common
Most of the 8 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 35
Verify THREE things before claiming success - a denied run still exits 0 with status SUCCESS:
- Very specific: line 96
Do not assume jq is installed - parse with Python:
“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
agy - headless Antigravity CLI
agy (Google Antigravity) runs an agent non-interactively: one prompt in
(-p), the response on stdout, diagnostics on stderr, process exits. It
reuses the logged-in account's cached credentials - authenticate once with an
interactive agy session before any headless use.
Everything below was verified live on agy 1.2.16 (Windows 11, Git Bash), 2026-10-04. Official reference: https://antigravity.google/docs/cli/headless/
Generate an image (the core recipe)
Image generation is an agent TOOL (generate_image, delegated to an
image-generator subagent), not a model slug: agy models lists text models
only, any of them can drive the generation. The image bytes are produced
server-side; the agent then copies the file into the working directory with a
shell command - and that copy is the whole permission problem.
- Work from a disposable directory (
scratch/..., a temp dir): the run needs--dangerously-skip-permissions(see below), so give it nothing valuable to touch. - One image, one command:
mkdir -p scratch/agy-images && cd scratch/agy-images
agy -p "Generate an image: <subject, color/material, background, lighting, framing>. Save it as <name>.png in the current directory." \
--dangerously-skip-permissions --output-format json- Verify THREE things before claiming success - a denied run still exits 0
with status SUCCESS:
- the envelope has
.status == "SUCCESS"ANDdenied_actionsabsent or empty, - the file exists on disk with the right magic bytes (PNG / JPEG),
- the image was actually LOOKED at (open it, screenshot it, vision pass): verified means seen.
- the envelope has
uv run --no-project python -c "import pathlib; b=pathlib.Path('<name>.png').read_bytes()[:8]; print('PNG' if b.startswith(b'\x89PNG\r\n\x1a\n') else b)"Why --dangerously-skip-permissions
- The final file copy runs through
run_command; in headless mode an unapproved command is SOFT-DENIED: the run "succeeds" (exit 0, status SUCCESS, empty or partial response) and the envelope carriesdenied_actions- the image never lands in the working directory. - Telling the agent to "use cp" does NOT reliably hit an existing
permissions.allowrule - verified twice, denied both times. write_to_filecannot carry binary image data (observed: junk test files, then a denial).
So keep the flag and contain the blast radius: disposable cwd, a prompt you
wrote, nothing sensitive in reach. Scoped permissions.allow rules are the
alternative when a run must work without the flag.
Salvage a denied run
The generation itself usually completed before the denied copy. The raw image
waits under the CLI's brain directory, named after the prompt with a
timestamp - and the extension can drift (a .png request landed as .jpg):
~/.gemini/antigravity-cli/brain/<conversation_id>/<prompt_name>_<timestamp>.jpgTake conversation_id from the envelope, copy the newest file out, rename it.
Iterate on an image
Conversations continue headless: -c continues the most recent one,
--conversation <id> a specific one. Reference-based edits (recolor,
restyle, variation) work, reading the earlier image from the conversation
context; expect roughly double the cost and duration of a fresh generation.
agy -c -p "Same subject, <change only>. Save it as <new-name>.png in the current directory." \
--dangerously-skip-permissions --output-format jsonMachine-readable output
--output-format text(default): the response text only.--output-format json: one envelope at the end -conversation_id,status,response,error,duration_seconds,num_turns,usage(token counts) anddenied_actions(undocumented in the official page, present in 1.2.16).--output-format stream-json: NDJSON events (init, thenstep_updatewith tool calls, then oneresult) - the way to see WHICH tools ran.
Do not assume jq is installed - parse with Python:
agy -p "..." --output-format json | uv run --no-project python -c "import json,sys; e=json.load(sys.stdin); print(e['status'], e['response'])"--json-schema constrains the answer to a schema; --model <slug> pins a
model and fails loudly (exit 1) on an unknown slug; --effort
low|medium|high trades depth for speed (1.2.16 also accepts xhigh|max).
Expectations and pitfalls (measured 2026-10-04, agy 1.2.16)
- One image: 45-100 s and 45k-80k tokens; one reference edit: ~200 s and up to 166k cumulative tokens. Budget before batching.
- The local binary can drift from the docs: 1.2.16 waits indefinitely by
default (
--print-timeout 0s; the docs describe a 5 m ceiling) - set the flag explicitly in scripts, and re-readagy --helpon the target machine before trusting memorized flags. - The enclosing repository leaks into the run: agy reads the workspace
AGENTS.md and
.agents/skills/of the cwd's repo (observed: replies in the repo's chat language, workspace skills auto-loaded). Run from a neutral directory unless that context is wanted. - CI or any terminal-less environment without cached credentials exits with
authentication requiredinstead of hanging. --disable-slash-commandsturns off slash-command and skill expansion in print mode.
Safety
--dangerously-skip-permissions approves every tool call of the run, file
writes and shell commands included. Disposable directory, self-written
prompt, nothing sensitive within reach - and never the user's working tree.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

