| name | file-reading |
| description | Use this skill when a file has been uploaded but its content is NOT in your context — only its path at /mnt/user-data/uploads/ is listed in an uploaded_files block. This skill is a router: it tells you which tool to use for each file type (pdf, docx, xlsx, csv, json, images, archives, ebooks) so you read the right amount the right way instead of blindly running cat on a binary. Triggers: any mention of /mnt/user-data/uploads/, an uploaded_files section, a file_path tag, or a user asking about an uploaded file you have not yet read. Do NOT use this skill if the file content is already visible in your context inside a documents block — you already have it. |
| compatibility | claude.ai, Claude Desktop, Cowork — any surface where uploads land at /mnt/user-data/uploads/ |
| license | Proprietary. LICENSE.txt has complete terms |
Reading Uploaded Files
Why this skill exists
When a user uploads a file in claude.ai, Claude Desktop, or Cowork,
the file is written to /mnt/user-data/uploads/<filename> and you are told the path
in an <uploaded_files> block. The content is not in your context.
You must go read it.
The naive thing — cat /mnt/user-data/uploads/whatever — is wrong for
most files:
- On a PDF it prints binary garbage.
- On a 100MB CSV it floods your context with rows you will never use.
- On a DOCX it prints the raw ZIP bytes.
- On an image it does nothing useful at all.
This skill tells you the right first move for each type, and when to
hand off to a deeper skill.
General protocol
- Look at the extension. That is your dispatch key.
- Stat before you read. Large files need sampling, not slurping.
stat -c '%s bytes, %y' /mnt/user-data/uploads/report.pdf
file /mnt/user-data/uploads/report.pdf
- Read just enough to answer the user's question. If they asked
"how many rows are in this CSV", don't load the whole thing into
pandas —
wc -l gives a fast approximation (it counts newlines,
not CSV records, so it may over-count if quoted fields contain
embedded newlines).
- If a dedicated skill exists, go read it. The table below tells
you when. The dedicated skills cover editing, creating, and advanced
operations that this skill does not.
extract-text
For docx, odt, epub, xlsx, pptx, rtf, and ipynb the first move is
extract-text <file>. It emits markdown for docx/odt/epub (headings,
bold, lists, links, tables), tab-separated rows under ## Sheet:
headers for xlsx, text under ## Slide N headers for pptx, fenced
code cells for ipynb, and plain text for rtf. Pass --format <fmt>
when the extension is wrong or absent (e.g., --format xlsx on an
.xlsm). If it errors on a file, pandoc <file> -t plain is a
fallback; for xlsx/pptx, fall back to the dedicated skill's
Python-based approach (openpyxl / python-pptx).
Dispatch table
| Extension | First move | Dedicated skill |
|---|
.pdf | Content inventory (see PDF section) | /mnt/skills/public/pdf-reading/SKILL.md |
.docx | extract-text | /mnt/skills/public/docx/SKILL.md |
.doc (legacy) | Convert to .docx first | /mnt/skills/public/docx/SKILL.md |
.xlsx | extract-text | /mnt/skills/public/xlsx/SKILL.md |
.xlsm | extract-text --format xlsx | /mnt/skills/public/xlsx/SKILL.md |
.xls (legacy) | pd.read_excel(engine="xlrd") — openpyxl rejects it | /mnt/skills/public/xlsx/SKILL.md |
.ods | pd.read_excel(engine="odf") — openpyxl rejects it | /mnt/skills/public/xlsx/SKILL.md |
.pptx | extract-text | /mnt/skills/public/pptx/SKILL.md |
.ppt (legacy) | Convert to .pptx first | /mnt/skills/public/pptx/SKILL.md |
.csv, .tsv | pandas with nrows | — (below) |
.json, .jsonl | jq for structure | — (below) |
.jpg, .png, .gif, .webp | Already in your context as vision input | — (below) |
.zip, .tar, .tar.gz | List contents, do not auto-extract | — (below) |
.gz (single file) | zcat | head — no manifest to list | — (below) |
.epub, .odt | extract-text | — (below) |
.rtf | extract-text | — (below) |
.ipynb | extract-text | — (below) |
.txt, .md, .log, code files | wc -c then head or full cat | — (below) |
| Unknown | file then decide | — |
PDF
Never cat a PDF — it prints binary garbage.
Quick first move — get the page count and determine whether the PDF
has an extractable text layer:
pdfinfo /mnt/user-data/uploads/report.pdf
pdffonts /mnt/user-data/uploads/report.pdf
pdffonts tells you whether text extraction will work before you try it:
The reason to check pdffonts first is user-facing: running
pdftotext on a scan produces an empty result, and in a visible
transcript that reads as a failed first attempt before you fall back
to OCR. The two-line diagnostic above costs one tool call and avoids
that — you arrive at the right method on the first try, which is what
a user perceives as "it just read my file."
That also shapes how to open your reply. The diagnostic commands are
plumbing, not content; lead with what the user asked about. On a
scanned receipt that might be "This is a 3-page scanned invoice; the
amount due on page 2 is $1,845.00," and on a digitally-authored report
it might be "The Q3 report runs 28 pages; revenue on p. 4 is $12.3M,
up 9% YoY." What you're steering away from is the "I'll examine the
PDF" / "Let me check if this is extractable" preamble — the answer to
their question is the first thing they should see.
For anything beyond a quick peek — figures, tables, attachments,
forms, scanned PDFs, visual inspection, or choosing a reading strategy
— go read /mnt/skills/public/pdf-reading/SKILL.md. It covers
content inventory, text extraction vs. page rasterization, embedded
content extraction, and document-type-aware reading strategies.
For PDF form filling, creation, merging, splitting, or watermarking,
go read /mnt/skills/public/pdf/SKILL.md.
DOCX / DOC
The docx skill covers editing, creating, tracked changes, images.
Read it if you need any of those. For a quick look:
extract-text /mnt/user-data/uploads/memo.docx | head -200
Legacy .doc (not .docx) must be converted first — see the docx
skill.
XLSX / XLS / spreadsheets
The xlsx skill covers formulas, formatting, charts, creating. Read
it if you need any of those. For a quick look at an .xlsx:
extract-text /mnt/user-data/uploads/data.xlsx | head -100
For .xlsm, add --format xlsx (same zip structure; only the
extension differs). When you need a structured preview in Python:
from openpyxl import load_workbook
wb = load_workbook("/mnt/user-data/uploads/data.xlsx", read_only=True)
print("Sheets:", wb.sheetnames)
ws = wb.active
for row in ws.iter_rows(max_row=5, values_only=True):
print(row)
read_only=True matters — without it, openpyxl loads the entire
workbook into memory, which breaks on large files. Do not trust
ws.max_row in read-only mode: many non-Excel writers omit the
dimension record, so it comes back None or wrong. If you need a row
count, iterate or use pandas.
Legacy .xls — openpyxl raises InvalidFileException. Use:
import pandas as pd
df = pd.read_excel("/mnt/user-data/uploads/old.xls", engine="xlrd", nrows=5)
.ods (OpenDocument) — openpyxl also rejects this. Use:
import pandas as pd
df = pd.read_excel("/mnt/user-data/uploads/data.ods", engine="odf", nrows=5)
PPTX
extract-text /mnt/user-data/uploads/deck.pptx | head -200
Legacy .ppt — convert to .pptx first via LibreOffice; see
/mnt/skills/public/pptx/SKILL.md for the sandbox-safe
scripts/office/soffice.py wrapper (bare soffice hangs here because
the seccomp filter blocks the AF_UNIX sockets LibreOffice uses for
instance management).
For anything beyond reading, go to /mnt/skills/public/pptx/SKILL.md.
CSV / TSV
Do not cat or head these blindly. A CSV with a 50KB quoted cell
in row 1 will wreck your head -5. Use pandas with nrows:
import pandas as pd
df = pd.read_csv("/mnt/user-data/uploads/data.csv", nrows=5)
print(df)
print()
print(df.dtypes)
Approximate row count without loading (over-counts if the file has
RFC-4180 quoted newlines — the same quoted-cell case this section
warned about above):
wc -l /mnt/user-data/uploads/data.csv
Full analysis only after you know the shape:
df = pd.read_csv("/mnt/user-data/uploads/data.csv")
print(df.describe())
TSV: same, with sep="\t".
JSON / JSONL
Structure first, content second:
jq 'type' /mnt/user-data/uploads/data.json
jq 'if type == "array" then length elif type == "object" then keys else . end' /mnt/user-data/uploads/data.json
(keys errors on scalar JSON roots — a bare "hello" or 42 is valid
JSON per RFC 7159 — so guard the branch.)
Then drill into what the user actually asked about.
JSONL (one object per line) — do not jq the whole file; work line
by line:
head -3 /mnt/user-data/uploads/data.jsonl | jq .
wc -l /mnt/user-data/uploads/data.jsonl
Images (JPG / PNG / GIF / WEBP)
You can already see uploaded images. They are injected into your
context as vision inputs alongside the <uploaded_files> pointer. You
do not need to read them from disk to describe them.
The disk copy is only needed if you are going to process the image
programmatically:
from PIL import Image
img = Image.open("/mnt/user-data/uploads/photo.jpg")
print(img.size, img.mode, img.format)
For OCR on an image (text extraction, not description):
import pytesseract
print(pytesseract.image_to_string(img))
Note: the client resizes images larger than 2000×2000 down to that
bound and re-encodes as JPEG before upload, so the disk copy may not
be the user's original bytes. For most processing this doesn't matter;
if the user is asking about original-resolution pixel data, flag it.
Archives (ZIP / TAR / TAR.GZ)
List first. Extract never — unless the user explicitly asks.
Archives can be huge, contain path traversal, or nest forever.
unzip -l /mnt/user-data/uploads/bundle.zip
tar -tf /mnt/user-data/uploads/bundle.tar
GNU tar auto-detects compression — tar -tf works on .tar,
.tar.gz, .tar.bz2, .tar.xz alike. Don't hard-code -z.
If the user wants one file from inside, extract just that one:
unzip -p /mnt/user-data/uploads/bundle.zip path/inside/file.txt
Standalone .gz (not a tar) compresses a single file — there is
no manifest to list. Just peek at the decompressed content:
zcat /mnt/user-data/uploads/data.json.gz | head -50
EPUB / ODT
extract-text /mnt/user-data/uploads/book.epub | head -200
For long ebooks, pipe through head — you rarely need the whole thing
to answer a question.
RTF / IPYNB
extract-text /mnt/user-data/uploads/notes.rtf | head -200
extract-text /mnt/user-data/uploads/notebook.ipynb | head -200
Plain text / code / logs
Check the size first:
wc -c /mnt/user-data/uploads/app.log
- Under ~20KB:
cat is fine.
- Over ~20KB:
head -100 and tail -100 to orient. If the user
asked about something specific, grep for it. Load the whole thing
only if you genuinely need all of it.
For log files, the user almost always cares about the end:
tail -200 /mnt/user-data/uploads/app.log
Unknown extension
file /mnt/user-data/uploads/mystery.bin
xxd /mnt/user-data/uploads/mystery.bin | head -5
file identifies most things. xxd head shows magic bytes. If file
says "data" and the hex doesn't match anything you recognize, ask the
user what it is instead of guessing.
