alt-text-writer
Skill2 starsChanged 23 days agoMITView on GitHub
Write accurate alt text and a figure caption for any image file — a vision model looks at the actual pixels and returns JSON with a 125-char alt string and a one-sentence caption. Use when filling in alt="" attributes, writing figure captions, or describing screenshots the agent cannot see.
- Reads credentials
In this repository10 other files it gives its agents.
- character-spritesSkill
- jingle-makerSkill
- narrated-poemSkill
- og-card-makerSkill
- photo-restylerSkill
- poster-generatorSkill
- pr-describerSkill
- ui-mockupSkill
- video-teaserSkill
- visual-judgeSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 27
Never print the key.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Alt-text writer
Runs the bundled nanoodle workflow workflows/alt-text.noodle-graph.json against the
NanoGPT API: an image input (Image) feeds a vision model whose question is assembled from
two text nodes — the alt-text rules (Alt rules) and an optional house style guide
(Style guide) — and the output (Alt text) is JSON with alt_125char and
numbered_caption. Requires Node.js 22.23.4+, 24.22.1+, or 26.12.1+ and the nanoodle npm package (npx nanoodle
fetches it).
Typical agent use: mid-PR, glob the repo's images, call this once per file, and write the
returned alt_125char into the markup instead of guessing at pixels you cannot see.
API key
The run needs a NanoGPT API key. Use whichever is available:
NANOGPT_API_KEYalready set in the environment — prefer this; no extra flags.- A
.envfile containingNANOGPT_API_KEY=...— pass--env-file <path>only when the key is not already in the environment. (With this CLI,--env-fileoverrides ambientNANOGPT_API_KEY.)
Never print the key.
Run
From this skill's directory (or prefix paths if running from elsewhere). Replace the image
path with the real file (@path attaches a local file; an https:// URL also works):
npx nanoodle run workflows/alt-text.noodle-graph.json \
--input "Image=@/path/to/image.jpg"The output is text, so no --out directory is needed. For a machine-readable payload
(outputs, cost, balance), the run summary JSON is always printed on stdout.
Optional overrides (both have baked-in defaults):
--input "Alt rules=<different output instructions>"
--input "Style guide=@docs/style-guide.md"Inspect the interface anytime with:
npx nanoodle inspect workflows/alt-text.noodle-graph.jsonInputs
| Key | Required | What to pass |
Image | yes | Image file path (@path) or https URL |
Alt rules | no (default baked in) | Alternative instructions for the metadata format |
Style guide | no (default baked in) | House wording rules to apply |
Outputs
Alt textis a text output: the CLI prints it on theAlt text:stderr line and includes it in the stdout JSON asoutputs["Alt text"]— a JSON string withalt_125charandnumbered_captionkeys. Parse it from stdout.
Serve as an MCP tool
The same graph file works unchanged as a typed MCP tool — copy it into the folder
nanoodle-mcp serves and the agent gets an alt-text tool with Image required:
mkdir -p ~/noodles && cp workflows/alt-text.noodle-graph.json ~/noodles/alt-text.json
claude mcp add nanoodle -- npx nanoodle-mcp --graphs ~/noodlesCost
Each run costs a fraction of a cent (one gemini-2.5-flash-lite vision call). Cheap
enough to fan out over a whole docs folder, but each call still spends real NanoGPT
credit — the CLI prints the metered cost after every run.
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.

