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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.

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Install command

npx skills add https://github.com/Hemkumar247/Ai-Skills --skill file-reading

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Hemkumar247/Ai-Skills

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UpdatedApril 2, 2026 at 09:17

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SKILL.md

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docx

Hemkumar247/Ai-Skills

Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

2026-04-020

pdf-reading

Hemkumar247/Ai-Skills

Use this skill when you need to read, inspect, or extract content from PDF files — especially when file content is NOT in your context and you need to read it from disk. Covers content inventory, text extraction, page rasterization for visual inspection, embedded image/attachment/table/form-field extraction, and choosing the right reading strategy for different document types (text-heavy, scanned, slide-decks, forms, data-heavy). Do NOT use this skill for PDF creation, form filling, merging, splitting, watermarking, or encryption — use the pdf skill instead.

2026-04-020

pptx

Hemkumar247/Ai-Skills

Use this skill any time a .pptx file is involved in any way — as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; reading, parsing, or extracting text from any .pptx file (even if the extracted content will be used elsewhere, like in an email or summary); editing, modifying, or updating existing presentations; combining or splitting slide files; working with templates, layouts, speaker notes, or comments. Trigger whenever the user mentions "deck," "slides," "presentation," or references a .pptx filename, regardless of what they plan to do with the content afterward. If a .pptx file needs to be opened, created, or touched, use this skill.

2026-04-020

xlsx

Hemkumar247/Ai-Skills

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

2026-04-020

competitive-intelligence

Hemkumar247/Ai-Skills

Run this skill whenever the user wants to analyze competitors, understand the competitive landscape, find market gaps, or position their product against others. Trigger on phrases like "who are my competitors", "competitive analysis", "how do I differentiate", "what's the competition doing", "competitor mapping", "market positioning", "where's the gap in the market", or "how do I stand out". Produces a senior analyst-level competitive intelligence report with positioning maps, moat analysis, and strategic recommendations.

2026-04-020

cursor-project-starter

Hemkumar247/Ai-Skills

Generates a complete Cursor AI IDE project starter kit — 7 documents — from a brainstorming conversation with the user. Use this skill whenever the user wants to start a new project, says "let's start a new project", "I have a project idea", "help me plan a new app/system/tool", "set up my Cursor project", or wants to brainstorm and immediately get working project files. Also trigger when the user says things like "I want to build X from scratch" or "help me plan out X before I start coding". Always use this skill proactively when a new project is being initiated — don't wait for the user to ask for documentation explicitly.

2026-04-020

Source

Hemkumar247

Hemkumar247/Ai-Skills

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Useful forSOC

File ClerksOffice and Administrative Support Occupations43-4071L4

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.

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 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 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

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.