Claude-code-sdk file-reading

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.

install

source · Clone the upstream repo

git clone https://github.com/SeifBenayed/cloclo

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/SeifBenayed/cloclo "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/file-reading" ~/.claude/skills/seifbenayed-claude-code-sdk-file-reading && rm -rf "$T"

manifest: .claude/skills/file-reading/SKILL.md

source content

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

Never

cat

a PDF — it prints binary garbage.

Quick first move — get the page count and check if text is extractable:

pdfinfo /mnt/user-data/uploads/report.pdf
pdftotext -f 1 -l 1 /mnt/user-data/uploads/report.pdf - | head -20

Then peek at the text content:

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.

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

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 in enumerate(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

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 not

cat

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"

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

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.