Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/seifbenayed/cloclo/file-readingnpx skills add SeifBenayed/cloclo --skill file-readinggit clone --depth 1 https://github.com/SeifBenayed/clocloWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/seifbenayed/cloclo/file-reading)<a href="https://agentmods.dev/skills/seifbenayed/cloclo/file-reading"><img src="https://agentmods.dev/badge/skills/seifbenayed/cloclo/file-reading.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00154 | $0.03105 |
| Opus 5 | $0.00077 | $0.01553 |
| Sonnet 5 | $0.00031 | $0.00621 |
| Haiku 4.5 | $0.00015 | $0.00311 |
Grade A, and why
file-reading scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 -lgives 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 |
— |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 346 lines · 154 tokens per session scan A 07337367b164
file-reading is a skill published in the GitHub repository SeifBenayed/cloclo (114 stars, last pushed 5mo ago), licensed MIT. It adds 154 tokens to every session and 3,105 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
notion
Notion API for creating and managing pages, databases, and blocks. Use when the user wants to create a Notion page, query a Notion database, update Notion properties, search Notion, add content to Notion, manage Notion blocks, or interact with Notion data sources and workspaces via the API.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
ondb
A logical analysis and reasoning tool for AI. Use when decomposing documents into structured knowledge, querying entities and relations, validating consistency, or indexing files. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", "analyze this document", entity CRUD, or cross-skill…
sn-da-large-file-analysis
万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl readonly 流式读取（iterrows 支持 10 万行以上）、Parquet 转换加速、内存优化、分块处理和大文件写入模式。遇到以下任一情况就主动使用本 skill：①数据行数 ≥ 10k（由 sn-da-excel-workflow 的行数评估步骤触发）；②用户出现触发词：大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing；③直接使用…