meltify

Melt files of any format into prompt-ready text where every block cites its source

Agents misread blurry digits, miss text a PDF hides, can't watch video, and paraphrase the one condition that mattered. meltify turns documents, spreadsheets, slides, mail, chat exports, archives, web pages, images, video and audio into markdown an agent can quote, puts a citation on every block, and checks answers before they go out.

Quickstart

meltify runs on macOS and Linux with Python 3.11.9 or newer.

Claude Code:

/plugin marketplace add jeon-jihyeon/meltify
/plugin install meltify@meltify

Codex, Gemini CLI, Cursor and other agents that read Agent Skills:

npx skills add jeon-jihyeon/meltify

Command line only:

uvx meltify read ./inputs

From Python, after pip install meltify:

import meltify

env = meltify.read("./inputs", out="melted", shallow=True)
for row in env.results:
    print(row["cite"], row["out"], row["needs"])

meltify.read takes the same flags as the command as keywords and returns the same result envelope that --json prints. A string option gets the flag's own type, so password_file="pw.txt" arrives as a path and budget="60" as a number. For encrypted files, pass password=: it outranks password_file= and never touches argv. Only the names exported from meltify are public. Every other module is internal and can change in any release.

The skills call the meltify command. If it isn't installed, the plugin launcher runs it through uvx, so uv is the only thing you need.

What you get

A folder with a PDF and a mail that carries a spreadsheet:

$ meltify read inputs
kind   chars  needs              out                                         cite
mail   133                       meltify-out/read/handover.eml.md            inputs/handover.eml
sheet  151    hidden sheets Old  meltify-out/read/handover.eml__cal.xlsx.md  inputs/handover.eml#att=cal.xlsx
pdf    99                        meltify-out/read/report.pdf.md              inputs/report.pdf
artifact results: meltify-out/read/index.jsonl
3 items melted into meltify-out/read, 0 errors, 1 need more work

The attachment became an item of its own, and every block in its markdown starts with the cite to quote:

<!-- meltify source: inputs/handover.eml#att=cal.xlsx -->
## inputs/handover.eml#att=cal.xlsx#Calendar
| row | A | B |
|---|---|---|
| 1 | date | event |
| 2 | 2025-07-21 | handover meeting |
| 7 |  | budget review |

--json prints the whole result, and meltify.read returns it as an Envelope:

A read row has these keys:

URL rows add final_url, fetched_at, sha256 and etag. A number two OCR engines read differently gets a row of its own with type set to disputed, plus the value and how many times each engine read it in counts.

A rerun into the same --out folder replaces the last run there. Markdown for an item the previous index.jsonl listed and this run didn't name at all is deleted, along with its saved attachments, so grepping the folder finds only this run's output. An item that failed this run keeps its previous markdown, and files you put there yourself stay.

What it does

Each command has a skill of the same name, such as meltify-ocr, that tells the agent when to run it and what to do with the result.

What read handles

Attachments, archive members, files attached to a PDF and downloaded files are melted again as items of their own, up to three levels deep.

Templates, macro-enabled and slideshow files read like the document they belong to, and a document zip under an unfamiliar name is still recognized by its content.

A binary file no converter reads gets one more try, in this order:

• Formats LibreOffice imports, such as Works, Lotus 1-2-3, Visio and Publisher, are rendered to PDF and cited by page. These rows show kind rendered
• Any other picture format Pillow decodes, like PCX or ICNS, goes to OCR as kind image
• Audio or video under an odd name is transcribed as kind media
• On macOS, a full Quick Look preview (kind rendered), the text Spotlight indexes (kind text) or a first-page thumbnail (kind rendered) is the last resort

Compiled programs and files of one repeated byte skip all of that and return unsupported format right away. Word, Excel and PowerPoint binaries, HWP and iWork files whose own parser gives up also get this fallback, unless they're encrypted or DRM-locked. --shallow skips this step, and read.fallback = false turns it off.

Pictures get read in the same pass. Images, scanned pages, pictures inside PDF, Word, PowerPoint, Excel, ODF, RTF, EPUB, HTML and mail, and the speech and scene frames of recordings all go through local OCR and speech engines, and the text lands right where the picture was. Native text always comes first, and OCR only covers what parsing can't reach:

• Charts in Word, PowerPoint, Excel (.xlsb too) and ODF become cited tables from the values the file saved. A Word or PowerPoint chart with no cache reads the workbook embedded beside it, and an Excel chart built on formulas that were never calculated asks LibreOffice to calculate them. SmartArt text is read as an indented list
• EMF and WMF pictures, standalone or inside a document, give up their text records directly. Bitmaps and text they can't decode are drawn by LibreOffice, Quick Look on macOS or the pure-Python metafile-render and then OCR'd. With none of them, they're listed in needs
• .ppt slides, notes and pictures, and .doc inline and floating pictures, are read natively. Pages and Keynote text, tables and pictures are read from the document itself, older iWork '09 files and bundle folders included
• A scan under a visible header still gets OCR'd, PDF sticky notes become cited blocks, and multi-page TIFFs and animated images are read frame by frame up to 50 frames

A few more things to know about that pass:

• Each unique picture or recording is read once, and every engine result is cached by content hash under ${XDG_CACHE_HOME:-~/.cache}/meltify, so a rerun only pays for what changed
• --budget SEC caps the time spent on uncached work (600 seconds by default). Whatever's left shows up as ocr (budget) in needs, and the next run picks up where this one stopped
• --jobs N sets how many files convert at once (8 by default). PDFs convert in worker processes of their own, except the first PDF of a run. Under meltify.read called from your own script, every PDF stays in the calling process
• --shallow skips OCR, speech, and drawing or recalculation work, and lists what it skipped in needs. Native text melts as usual
• When two OCR engines disagree on a number, the block ends with a > disputed line. With only one engine, it ends with > unchecked: one engine
• When the engines read every picture and recording of an item and find no text at all, its row says no text found, so a blank scan isn't mistaken for one nobody read

Encrypted files open with a password from --password-file PATH or MELTIFY_PASSWORD: PDF, Word, Excel and PowerPoint, HWP and HWPX, Pages, Keynote and Numbers, and zip, 7z and rar archives. HWP distribution documents, which only restrict copying and printing, open without one. --password works too, but other users on the machine can see it. If you set more than one, --password-file wins, then --password, then MELTIFY_PASSWORD.

Without a password, the row says encrypted in needs and no markdown file is written. A wrong one says wrong password. An archive still lists the members it could open.

URLs work like files. read saves a web page's main text with its heading anchors (--whole keeps the full page, --render runs it in a headless browser first), downloads documents and melts them by type, and sends video links through the media pipeline. Downloads land under meltify-out/read/web/ and are revalidated with ETags on the next run. By default it refuses private addresses, follows robots.txt and stops at 20 MB or 30 seconds. See SECURITY.md for the details.

Citations

Every result carries src and a one-line cite:

Exit codes

Every command prints a table by default, and the full result with --json. It exits with:

• 0: success
• 1: violations, failed checks or other errors
• 2: usage error or bad input
• 3: a needed engine, key, binary or base module is missing

An item read couldn't melt doesn't change the exit code. It shows up as a row with error and as a warning, and the rest of the run carries on. An unexpected failure still prints the envelope, with error code failed. Set MELTIFY_DEBUG=1 to see its traceback too.

Engines

auto uses local engines only. Paid engines run only when you name them in meltify.toml or on the command line. An agent can also feed in what it reads in an image with meltify ocr --reading agent=FILE, so its own reading gets cross-checked against the local engines without any API key.

Optional parts install on request with meltify doctor --install NAME. Under the plugin launcher, it installs into the launcher's own venv. Anywhere else it installs nothing and prints the command for your setup, like uv tool install 'meltify[office]' or pip install 'meltify[office]'. With uvx, use uvx --from 'meltify[office]' meltify .... The extras:

Recordings need ffmpeg and YouTube downloads need deno. PowerPoint 95, formula recalculation and the rendering fallback need LibreOffice, which meltify doctor --install libreoffice downloads as a portable copy on macOS and Linux. wpd2text from libwpd reads WordPerfect best, then LibreOffice, and a built-in reader covers the body text without either.

Configuration

Settings are layered, lowest precedence first:

1. The packaged defaults
2. $XDG_CONFIG_HOME/meltify/config.toml (usually ~/.config/meltify/config.toml)
3. The nearest meltify.toml, searched from the working directory up to the git root, or the file you pass with --config. Outside a git repository, only the working directory is searched
4. MELTIFY_<KEY> and MELTIFY_<SECTION>_<KEY> environment variables
5. Command-line flags

See examples/meltify.toml for a sample. A key no default names, like a typo, shows up as a warning instead of being ignored. A few keys worth knowing:

• lang is the language OCR expects (ko by default). --lang on read and ocr sets it for one run, and takes ko, en, ja, zh, de, fr or es
• asr.lang is the spoken language for speech engines. It's auto by default, separate from lang, so Whisper detects each recording's language instead of translating it
• read.parquet_rows sets how many Parquet rows melt per file
• render.quicklook = false keeps Quick Look and Spotlight out of every render

Limits

• Text drawn inside an image is just pixels, so hidden finds it only through --contrast rendering and OCR
• OCR agreement means the engines read the same value, not that the value is right. A value read only by LLM engines is never marked agreed
• Speech recognition and OCR quality depend on the engine and the input
• Linux has no Apple Vision, so with PaddleOCR alone every reading is marked unchecked
• Rendered rows, EMF bitmaps and Quick Look previews are only as good as the OCR that reads them, and Quick Look exists only on macOS
• Pages and Keynote charts are listed in needs instead of read, and a Pages or Keynote file with no text records falls back to its embedded preview image
• meltify can't read DRM-protected HWP files or password-protected WordPerfect files. It also can't read the binary Hancom formats: .cell and .show files from before 2014, and Hanshow .hpt files. Save those again as .xlsx or .pptx
• Parquet files show their first 200 rows, and the rest are counted in needs
• Encrypted 7z and rar need 7-Zip, which meltify doctor --install archive downloads. Plain ones open with the system libarchive, a separate package on Linux, with 7-Zip, or with bsdtar on macOS
• The plugin launcher is a POSIX shell script and doctor --install only downloads macOS and Linux builds, so Windows isn't supported

License

MIT. meltify depends on PyMuPDF, which is AGPL-3.0, so distributing a product that bundles it comes with AGPL obligations. The pi-heif wheels bundle libheif and libde265 (LGPL-3.0), and the iwork extra pulls in enum-tools (LGPL-3.0) through numbers-parser. The programs doctor --install downloads keep their own licenses: LibreOffice is MPL-2.0, and 7-Zip is LGPL-2.1 with the unRAR restriction.