Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oaustegard/claude-skills --skill transcribing-imagesgit clone --depth 1 https://github.com/oaustegard/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oaustegard/claude-skills/transcribing-images)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/transcribing-images"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/transcribing-images/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oaustegard/claude-skills/transcribing-images"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/transcribing-images.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00151 | $0.01163 |
| Opus 5 | $0.00076 | $0.00581 |
| Sonnet 5 | $0.00030 | $0.00233 |
| Haiku 4.5 | $0.00015 | $0.00116 |
Grade A, and why
transcribing-images scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcribing Images
Read what a slide, page, or image actually shows — text plus charts,
diagrams, screenshots, and layout — by rasterizing it and sending the picture
to a vision model. This is the fix for the gap the built-in pptx and pdf
skills leave: they extract embedded text only, so an image slide, a chart, or a
scanned figure reads as empty. Visual transcription reads it the way a person
looking at the slide would.
When to reach for this vs. the built-in skills
Use the pptx / pdf skills first for text-native documents — a normal
deck or report where the content is real text boxes. They are faster and exact.
Switch to this skill when text extraction comes back thin or empty on a file
you can see is visually rich, or whenever the meaningful content is a picture:
chart, graph, diagram, screenshot, photo, scanned page, or a slide exported as
one flat image. Don't guess which case you're in — if pptx/pdf returned
little from a file that clearly has content, that is the signal.
The pipeline
Everything routes through scripts/transcribe_pages.py, which handles all
three ingress paths and one bad page never aborts the rest:
.pptx/.ppt→ LibreOffice headless → PDF →pdftoppm→ one PNG per slide.pdf→pdftoppm→ one PNG per page- image file → used directly as a single page
Each page image is then transcribed by a vision model. Run it directly:
python3 scripts/transcribe_pages.py deck.pptx # all slides
python3 scripts/transcribe_pages.py report.pdf --pages 3-7 # subset
python3 scripts/transcribe_pages.py slide.png --model opus # one image
python3 scripts/transcribe_pages.py deck.pptx --json out.json # structured
Or import transcribe_file(...) for programmatic use; it returns a list of
{page, image, text, error} dicts.
Choosing the model
The transcription core and its empirical cost/recall data are reused from
browsing-bluesky/scripts/image_transcribe.py — same registry, kept in sync.
Pick with --model:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +2 lines 8e4f09118ecb
- 9d ago First seen · 92 lines · 151 tokens per session scan A 5ab2c123e2c7
transcribing-images is a skill published in the GitHub repository oaustegard/claude-skills (148 stars, last pushed yesterday), licensed MIT. It adds 151 tokens to every session and 1,163 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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