Agent skill

Sn Da Large File Analysis

by MichaelYang-lyx in MichaelYang-lyx/AIDABench

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 /…

No licenceAuto-check passedDocuments & Office

Install Sn Da Large File Analysis

skills CLI
$ npx skills add MichaelYang-lyx/AIDABench --skill sn-da-large-file-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install MichaelYang-lyx/AIDABench sn-da-large-file-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/MichaelYang-lyx/AIDABench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sn-da-large-file-analysis .claude/skills/sn-da-large-file-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sn-da-large-file-analysis
GitHub stars
111
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
438 words
Files
1
Skills in repo
39
Repo updated
First seen
Licence
None found

At a glance

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 /…

  • Works in 5 steps: Parquet is king: For any file >= 10k… → Streaming is the safety net: For 100k+… → Release memory promptly: del df;… → …
  • Tasks that involve DataFrames
  • SKILL.md covers Mandatory Rules, Environment Setup, Core Method 1: Inspect File… and Core Method 2: Streaming Read…, plus 8 more sections
  • Calls pip

What it does

Sn Da Large File Analysis is an agent skill from MichaelYang-lyx/AIDABench. 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 pd.readexcel() 导致超时或内存溢出;④用户明确要求对大规模数据集进行高性能处理。仅不用于:小于 10k 行的常规 Excel 分析(使用 sn-da-excel-workflow 即可)。

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering DataFrames and Excel spreadsheets. It works with Microsoft Excel and openpyxl. The repository describes itself as: Code for paper AIDABench: AI Data Analytics Benchmark.

When your agent uses it

  • Tasks that involve DataFrames
  • Tasks that involve Excel spreadsheets

Example prompts

  • “/sn-da-large-file-analysis”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Parquet is king: For any file >= 10k rows, convert to Parquet before analysis. Parquet supports columnar reads, compressed storage, and…
  2. Streaming is the safety net: For 100k+ rows, always use openpyxl read_only + iter_rows. Never pd.read_excel() for full load.
  3. Release memory promptly: del df; gc.collect() after every intermediate DataFrame.
  4. Excel row limit: Max 1,048,576 rows per sheet. Auto-split to multiple sheets or export as CSV when exceeded.
  5. Use write_only for output: Files with >50k cells must use openpyxl Workbook(write_only=True).

What it can do on your machine

Read from SKILL.md and the folder at commit 6dd4206. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Sn Da Large File Analysis loads about 3.1k tokens when it runs. Until then it costs about 112 tokens; SKILL.md has 438 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

Without a licence we can't republish the file, so here is its outline and opening line. It has 438 words (~3,149 tokens).

name
sn-da-large-file-analysis

Read the full SKILL.md on GitHub

Files

Just SKILL.md in skills/sn-da-large-file-analysis of MichaelYang-lyx/AIDABench.

Open the folder on GitHubat commit 6dd4206

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in MichaelYang-lyx/AIDABench, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Sn Da Large File Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Sn Da Large File Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Sn Da Large File Analysis this skillMichaelYang-lyx/AIDABench1111 repos~3.1kAutomated safety check: PassNone
Office XLSXsingula-ai/alego1091 repos~2.3kAutomated safety check: PassMIT
XLSX Parsingbenchflow-ai/skillsbench1.8k—~1.4kAutomated safety check: PassApache-2.0
Convert Fileduckdb/duckdb-skills6031 repos~720Automated safety check: NotesMIT
Excel ParserHarryoung/efka104—~2.3kAutomated safety check: PassApache-2.0
XLSXNousResearch/hermes-agent252k—~2.4kAutomated safety check: PassMIT

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Questions about Sn Da Large File Analysis

What does Sn Da Large File Analysis do?

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 /…. Sn Da Large File Analysis is an agent skill from MichaelYang-lyx/AIDABench.

When should I use Sn Da Large File Analysis?

Sn Da Large File Analysis fits situations like: tasks that involve DataFrames; tasks that involve Excel spreadsheets.

How do I install Sn Da Large File Analysis in Claude Code?

Run `npx skills add MichaelYang-lyx/AIDABench --skill sn-da-large-file-analysis -a claude-code`. Or copy the skill folder (skills/sn-da-large-file-analysis in MichaelYang-lyx/AIDABench) into .claude/skills/sn-da-large-file-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Sn Da Large File Analysis in Codex?

Run `npx skills add MichaelYang-lyx/AIDABench --skill sn-da-large-file-analysis -a codex`. Or copy the skill folder (skills/sn-da-large-file-analysis in MichaelYang-lyx/AIDABench) into .agents/skills/sn-da-large-file-analysis in your project. Codex loads it when a task matches its description.

Can I use Sn Da Large File Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add MichaelYang-lyx/AIDABench --skill sn-da-large-file-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sn-da-large-file-analysis, .gemini/skills/sn-da-large-file-analysis, .github/skills/sn-da-large-file-analysis and .opencode/skills/sn-da-large-file-analysis in your project.

What does Sn Da Large File Analysis need to run?

Going by SKILL.md and its folder, Sn Da Large File Analysis needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Sn Da Large File Analysis access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Sn Da Large File Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Sn Da Large File Analysis use?

No licence was found for Sn Da Large File Analysis or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.

How many tokens does Sn Da Large File Analysis use?

About 3.1k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Sn Da Large File Analysis?

Skills that share tags, products or a category with Sn Da Large File Analysis: Office XLSX (singula-ai/alego, 109 stars), XLSX Parsing (benchflow-ai/skillsbench, 1.8k stars), Convert File (duckdb/duckdb-skills, 603 stars) and Excel Parser (Harryoung/efka, 104 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Sn Da Large File Analysis?

MichaelYang-lyx (a GitHub user) maintains it in MichaelYang-lyx/AIDABench, which has 111 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on September 28, 2026.

Source: MichaelYang-lyx/AIDABench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.