From honey
Reads huge, dense, read-only text as rendered PNG pages to reduce token costs by ~60–75%. Use for skimming big files, vendored code, diffs, or logs you won't edit or byte-copy.
How this skill is triggered — by the user, by Claude, or both
Slash command
/honey:honey-pxThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
An image's token cost is fixed by its pixel area, not its character count.
An image's token cost is fixed by its pixel area, not its character count.
Dense text packs ~3 chars per image-token vs ~1 as text, so a 90k-char file
read as PNG pages costs a fraction of the text read. pxpipe export
(teamchong/pxpipe) does the rendering; the agent Reads the pages.
Lossy by design — misreads of exact strings are silent confabulations,
not errors. The export writes a factsheet.txt carrying the verbatim
precision tokens (paths, SHAs, ids, numbers) as text alongside the images;
always read it too.
% saved, skip if it's low. Sparse prose loses money.old_string must be byte-exact; imaged reads
aren't. Read the real file (with offset/limit) before any edit.Grep the source first.npx pxpipe-proxy export --json --out <scratchpad> <file-or-dir> # or --git / --diff <ref> / --stdin
Output dir pxpipe-export-<hash>/ contains page-*.png, factsheet.txt,
manifest.json (token report). Then:
Read every page-*.png and factsheet.txt. If instead you pass the
renders to a model over the raw API (subagent prompt, panel), include the
export's prompt.txt banner — naked dense renders can trip
stop_reason: refusal on Fable-class models; the banner prevents it.percentSaved as the go/no-go — if it printed low,
read the text instead.Grep.Cheaper still is not reading at all — Grep/offset/limit (Lever 3b) and
eson crush (CCR) come first. PX is for when you genuinely need the whole
bulk in view.
npx claudepluginhub green-pt/honey-for-devs --plugin honeyCompresses PDFs to Markdown, downscales large images, and digests verbose logs to reduce token costs. Run manually or let the Read hook auto-optimize heavy attachments.
Analyzes media files (PDFs, images, diagrams, screenshots) using a vision backend to extract structured data, descriptions, or summaries instead of literal file reading.
Parses complex documents with PaddleOCR to extract text, tables, formulas, charts, and layout structure. Use for invoices, academic papers, multi-column layouts, or any document needing structured understanding.