CSV Data Summarizer
coffeefuelbump/csv-data-summarizer-claude-skill
Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.
Query and analyze structured CSV datasets on critical minerals production, trade, and supply chains
Install Minerals Data
$ npx skills add lamm-mit/scienceclaw --skill minerals-data -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install lamm-mit/scienceclaw minerals-data --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/minerals-data .claude/skills/minerals-data && rm -rf skills-srcUse ~/.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/
Install the "minerals-data" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/minerals-data into .claude/skills/minerals-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "minerals-data", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/lamm-mit/scienceclaw/tree/main/skills/minerals-dataType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add lamm-mit/scienceclaw --skill minerals-data -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install lamm-mit/scienceclaw minerals-data --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/minerals-data .agents/skills/minerals-data && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "minerals-data" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/minerals-data into .agents/skills/minerals-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "minerals-data", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lamm-mit/scienceclaw --skill minerals-data -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install lamm-mit/scienceclaw minerals-data --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/minerals-data .cursor/skills/minerals-data && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "minerals-data" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/minerals-data into .cursor/skills/minerals-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "minerals-data", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/lamm-mit/scienceclaw.git --path skills/minerals-data--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add lamm-mit/scienceclaw --skill minerals-data -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install lamm-mit/scienceclaw minerals-data --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/minerals-data .gemini/skills/minerals-data && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "minerals-data" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/minerals-data into .gemini/skills/minerals-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "minerals-data", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install lamm-mit/scienceclaw minerals-dataInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add lamm-mit/scienceclaw --skill minerals-data -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/minerals-data .github/skills/minerals-data && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "minerals-data" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/minerals-data into .github/skills/minerals-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "minerals-data", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add lamm-mit/scienceclaw --skill minerals-data -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install lamm-mit/scienceclaw minerals-data --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/lamm-mit/scienceclaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/minerals-data .opencode/skills/minerals-data && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "minerals-data" agent skill from https://github.com/lamm-mit/scienceclaw/tree/main/skills/minerals-data into .opencode/skills/minerals-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "minerals-data", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
minerals-data- GitHub stars
- 244
- Token cost
- ~730 tokens
- SKILL.md length
- 161 words
- Files
- 3 (incl. scripts)
- Skills in repo
- 85
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Query and analyze structured CSV datasets on critical minerals production, trade, and supply chains
- Tasks that involve CSV and tabular files
- SKILL.md covers Usage, Parameters, Query DSL and Examples, plus 1 more section
- Runs Python scripts from its folder; calls python3
- Tasks that involve Supply chain security
What it does
Minerals Data is an agent skill from lamm-mit/scienceclaw. Query and analyze structured CSV datasets on critical minerals production, trade, and supply chains
Its SKILL.md is about 730 tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including scripts (for example `scripts/query_data.py`).
It sits in Data & Analytics, covering CSV and tabular files, Supply chain security and DataFrames. It works with pandas. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve CSV and tabular files
- Tasks that involve Supply chain security
- Tasks that involve DataFrames
Example prompts
- “/minerals-data”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit ab9aba1. 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
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Minerals Data loads about 730 tokens when it runs. Until then it costs about 28 tokens; SKILL.md has 161 words of instructions outside code blocks.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from lamm-mit/scienceclaw at commit ab9aba1, republished under its Apache-2.0 licence (© lamm-mit). 161 words, ~730 tokens.
Download SKILL.mdSave it as
.claude/skills/minerals-data/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- minerals-data
- description
- Query and analyze structured CSV datasets on critical minerals production, trade, and supply chains
Minerals Data — Structured CSV Querying
Query and analyze structured CSV datasets from the critical minerals corpus. Supports listing available datasets, describing schemas, filtering, grouping, and aggregation via pandas.
Usage
List available datasets:
python3 {baseDir}/scripts/query_data.py --listDescribe a dataset:
python3 {baseDir}/scripts/query_data.py --dataset usgs/production.csv --describeQuery with DSL:
python3 {baseDir}/scripts/query_data.py --dataset usgs/production.csv --query "groupby:commodity|agg:value:sum|sort:value:desc|head:10"Filter with pandas expression:
python3 {baseDir}/scripts/query_data.py --dataset usgs/production.csv --filter "year >= 2022"Combine filter and query:
python3 {baseDir}/scripts/query_data.py --dataset usgs/trade.csv --filter "commodity == 'lithium'" --query "groupby:country|agg:value:sum|sort:value:desc|head:5"Parameters
| Parameter | Description | Default |
|---|---|---|
--list | List all available CSV datasets | - |
--dataset | Path to CSV file (relative to corpus dir) | - |
--describe | Show schema, dtypes, sample rows, statistics | - |
--query | Pipe-delimited DSL for pandas operations | - |
--filter | Pandas query expression for filtering | - |
--corpus-dir | Directory containing data files | ~/critical-minerals-data/ |
--format | Output format: table, json, csv | table |
Query DSL
Pipe-delimited operations that map to pandas:
| Operation | Syntax | Example |
|---|---|---|
| Group by | groupby:col | groupby:commodity |
| Aggregate | agg:col:func | agg:value:sum |
| Sort | sort:col:dir | sort:value:desc |
| Head | head:n | head:10 |
| Select columns | select:col1,col2 | select:commodity,value |
Functions: sum, mean, count, min, max, median, std
Examples
# Top producing countries for lithium
python3 {baseDir}/scripts/query_data.py --dataset usgs/production.csv \
--filter "commodity == 'lithium'" \
--query "groupby:country|agg:value:sum|sort:value:desc|head:10"
# Year-over-year trade data
python3 {baseDir}/scripts/query_data.py --dataset comtrade/exports.csv \
--query "groupby:year|agg:value:sum|sort:year:asc"
# Dataset overview
python3 {baseDir}/scripts/query_data.py --dataset worldbank/indicators.csv --describeNotes
- Requires
pandas>=2.0.0(already in ScienceClaw requirements) - CSV catalog is cached at
~/critical-minerals-data/.csv_catalog.json - Handles encoding fallbacks: UTF-8, Latin-1, CP1252
- Filter expressions are sanitized to prevent code injection
© lamm-mit, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (scripts) in skills/minerals-data of lamm-mit/scienceclaw.
- SKILL.md
- requirements.txt
- scripts/query_data.py
Open the folder on GitHubat commit ab9aba1
Compare with similar skills
Minerals Data 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Minerals Data this skilllamm-mit/scienceclaw | 244 | — | ~730 | Automated safety check: Pass | Apache-2.0 | |
| CSV Data Summarizercoffeefuelbump/csv-data-summarizer-claude-skill | 468 | 2 repos | ~1.4k | Automated safety check: Pass | None | |
| CSV Processingbenchflow-ai/skillsbench | 1.8k | — | ~455 | Automated safety check: Pass | Apache-2.0 | |
| Verified Data Analysis with pandaspipeshub-ai/pipeshub-ai | 3.8k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Vaex Out-of-Core DataFramesdavila7/claude-code-templates | 32k | 12 repos | ~1.6k | Automated safety check: Pass | MIT | |
| CSV and Excel MergerOneWave-AI/claude-skills | 322 | — | ~1.6k | Automated safety check: Pass | MIT |
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Related
Works with
Questions about Minerals Data
What does Minerals Data do?
Query and analyze structured CSV datasets on critical minerals production, trade, and supply chains. Minerals Data is an agent skill from lamm-mit/scienceclaw.
When should I use Minerals Data?
Minerals Data fits situations like: tasks that involve CSV and tabular files; tasks that involve Supply chain security; tasks that involve DataFrames.
How do I install Minerals Data in Claude Code?
Run `npx skills add lamm-mit/scienceclaw --skill minerals-data -a claude-code`. Or copy the skill folder (skills/minerals-data in lamm-mit/scienceclaw) into .claude/skills/minerals-data in your project. Claude Code loads it when a task matches its description.
How do I install Minerals Data in Codex?
Run `npx skills add lamm-mit/scienceclaw --skill minerals-data -a codex`. Or copy the skill folder (skills/minerals-data in lamm-mit/scienceclaw) into .agents/skills/minerals-data in your project. Codex loads it when a task matches its description.
Can I use Minerals Data 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 lamm-mit/scienceclaw --skill minerals-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/minerals-data, .gemini/skills/minerals-data, .github/skills/minerals-data and .opencode/skills/minerals-data in your project.
What does Minerals Data need to run?
Going by SKILL.md and its folder, Minerals Data needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Minerals Data access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Minerals Data 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Minerals Data use?
Minerals Data is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Minerals Data use?
About 730 tokens (SKILL.md is roughly 2.9k 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 Minerals Data?
Skills that share tags, products or a category with Minerals Data: CSV Data Summarizer (coffeefuelbump/csv-data-summarizer-claude-skill, 468 stars), CSV Processing (benchflow-ai/skillsbench, 1.8k stars), Verified Data Analysis with pandas (pipeshub-ai/pipeshub-ai, 3.8k stars) and Vaex Out-of-Core DataFrames (davila7/claude-code-templates, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Minerals Data?
lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.
Source: lamm-mit/scienceclaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.