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.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
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.
Dispatch table
Extension
First move
Dedicated skill
.pdf
Content inventory (see PDF section)
/mnt/skills/public/pdf-reading/SKILL.md
.docx
pandoc to markdown
/mnt/skills/public/docx/SKILL.md
.doc (legacy)
Convert to .docx first — pandoc cannot read it
/mnt/skills/public/docx/SKILL.md
.xlsx, .xlsm
openpyxl sheet names + head
/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
python-pptx slide count
/mnt/skills/public/pptx/SKILL.md
.ppt (legacy)
Convert to .pptx first — python-pptx rejects it
/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
pandoc to plain text
— (below)
.rtf
pandoc (needs 3.1.7+) or soffice via docx skill
— (below)
.txt, .md, .log, code files
wc -c then head or full cat
— (below)
Unknown
file then decide
—
PDF
Nevercat a PDF — it prints binary garbage.
Quick first move — get the page count and check if text is extractable:
from pypdf import PdfReader
r = PdfReader("/mnt/user-data/uploads/report.pdf")
print(f"{len(r.pages)} pages")
print(r.pages[0].extract_text()[:2000])
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:
pandoc /mnt/user-data/uploads/memo.docx -t markdown | 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 .xlsx / .xlsm:
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.
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
from itertools import islice
from pptx import Presentation
p = Presentation("/mnt/user-data/uploads/deck.pptx")
print(f"{len(p.slides)} slides")
for i, slide inenumerate(islice(p.slides, 3), 1):
texts = [s.text for s in slide.shapes if s.has_text_frame]
print(f"Slide {i}:", " | ".join(t for t in texts if t))
p.slides is not subscriptable — p.slides[:3] raises
AttributeError. Use islice or list(p.slides)[:3].
Legacy .ppt — python-pptx only reads OOXML. 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 notcat or head these blindly. A CSV with a 50KB quoted cell
in row 1 will wreck your head -5. Use pandas with nrows:
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):
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 notjq 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):
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:
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
pandoc /mnt/user-data/uploads/book.epub -t plain | head -200
For long ebooks, pipe through head — you rarely need the whole thing
to answer a question.
RTF
Pandoc's RTF reader was added in 3.1.7 (Oct 2023). Debian Bookworm
ships 2.17, so try pandoc first but expect it may fail:
pandoc /mnt/user-data/uploads/notes.rtf -t plain | head -200
If you see Unknown input format rtf, convert via LibreOffice using
the sandbox-safe wrapper — see /mnt/skills/public/docx/SKILL.md for
scripts/office/soffice.py (do not call bare soffice; see the PPTX
section above for why).
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.
