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Agent skill

Io Formats

by VectorSpaceLab in VectorSpaceLab/AREX-Skill

A skill your agent uses when reading, writing, filtering, converting, or troubleshooting GeoPandas vector files, GeoJSON, WKB/WKT, Arrow, Parquet, Feather, and PostGIS data.

BSD-3-ClauseAuto-check passedData & Analytics

Install Io Formats

skills CLI
$ npx skills add VectorSpaceLab/AREX-Skill --skill io-formats -a claude-code

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

GitHub CLI
$ gh skill install VectorSpaceLab/AREX-Skill io-formats --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/geopandas/sub-skills/io-formats .claude/skills/io-formats && 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
io-formats
GitHub stars
331
Token cost
~1k tokens
SKILL.md length
417 words
Files
6 (incl. scripts, references)
Skills in repo
157
Repo updated
First seen
Licence
BSD-3-Clause

At a glance

A skill your agent uses when reading, writing, filtering, converting, or troubleshooting GeoPandas vector files, GeoJSON, WKB/WKT, Arrow, Parquet, Feather, and PostGIS data.

  • Works in 6 steps: Prefer the base pyogrio engine when… → Preserve and validate CRS after every… → Use columns, rows, bbox, or mask filters… → …
  • Troubleshooting GeoPandas vector files
  • SKILL.md covers Read First, Route Here When, Route Elsewhere and Default Operating Rules, plus 2 more sections
  • Runs Python scripts from its folder; calls python

What it does

Io Formats is an agent skill from VectorSpaceLab/AREX-Skill. Use when reading, writing, filtering, converting, or troubleshooting GeoPandas vector files, GeoJSON, WKB/WKT, Arrow, Parquet, Feather, and PostGIS data.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/io-reference.md`, `references/troubleshooting.md` and `references/workflows.md`).

It sits in Data & Analytics, covering Geospatial analysis and DataFrames. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is BSD-3-Clause.

When your agent uses it

  • Troubleshooting GeoPandas vector files
  • Tasks that involve Geospatial analysis
  • Tasks that involve DataFrames

Example prompts

  • “/io-formats”

Requirements

  • Python 3

Workflow steps

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

  1. Prefer the base pyogrio engine when available; use Fiona only when the task specifically needs Fiona behavior or installed environment…
  2. Preserve and validate CRS after every round trip. File formats and engines differ in metadata support.
  3. Use columns, rows, bbox, or mask filters to reduce read size before expensive analysis.
  4. Treat Parquet/Feather/Arrow as optional pyarrow workflows; provide a GeoJSON/GPKG fallback when pyarrow is unavailable.
  5. Treat PostGIS as a service-backed workflow: Python imports are not enough; a live database, credentials, schema privileges, and geometry…
  6. Never assume a file driver from extension alone when a task has strict format requirements; set driver= for writes when needed.

What it can do on your machine

Read from SKILL.md and the folder at commit ac3fe1a. 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 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    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

Io Formats loads about 1k tokens when it runs, and up to ~3.7k if it reads all its reference files. Until then it costs about 41 tokens; SKILL.md has 417 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.7k

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 VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its BSD-3-Clause licence (© VectorSpaceLab). 417 words, ~1,000 tokens.

Download SKILL.mdSave it as .claude/skills/io-formats/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
io-formats
description
Use when reading, writing, filtering, converting, or troubleshooting GeoPandas vector files, GeoJSON, WKB/WKT, Arrow, Parquet, Feather, and PostGIS data.
disable-model-invocation
true
metadata.disco-role
operating
license
BSD 3-Clause

I/O and Formats

Use this sub-skill when the task is about moving geospatial vector data into or out of GeoPandas, choosing a storage format or engine, preserving CRS/geometry metadata, or diagnosing optional I/O dependencies.

Read First

  • I/O reference: verified signatures and behavior notes for read_file, to_file, Arrow/Parquet/Feather, WKB/WKT, GeoJSON-like records, and SQL/PostGIS APIs.
  • Workflows: recipes for safe file round trips, filtered reads, GeoParquet/Feather decisions, WKT/WKB conversions, and PostGIS handoffs.
  • Troubleshooting: symptoms and fixes for missing pyarrow, Fiona/pyogrio engine differences, CRS metadata loss, SQL service errors, and unsupported drivers.
  • io_roundtrip_smoke.py: tiny temporary GeoDataFrame round-trip check for GeoJSON or GPKG.
  • check_optional_io_dependencies.py: optional dependency reporter for I/O, database, visualization, and geocoding packages.

Route Here When

  • The user asks for geopandas.read_file, GeoDataFrame.to_file, list_layers, engine="pyogrio", engine="fiona", file drivers, archive/URL reads, bbox, mask, columns, or rows filters.
  • The task involves GeoJSON dictionaries, __geo_interface__, iterfeatures, to_json, or to_geo_dict.
  • The task mentions read_parquet, to_parquet, read_feather, to_feather, to_arrow, from_arrow, GeoParquet, GeoArrow, pyarrow, or covering bbox metadata.
  • The task needs GeoSeries.from_wkt, to_wkt, from_wkb, to_wkb, or tabular geometry serialization.
  • The task uses read_postgis, to_postgis, SQLAlchemy connections, PostGIS geometry columns, or database write policies.

Route Elsewhere

  • Use ../core-data-model/SKILL.md for geometry-column repair, CRS assignment versus reprojection, and object semantics before or after I/O.
  • Use ../spatial-operations/SKILL.md for analysis after loading layers.
  • Use ../mapping-geocoding/SKILL.md for plotting/geocoding after loading data.
  • Use ../validation-testing/SKILL.md when designing assertions for I/O results or selecting native tests.
Show full SKILL.md (191 more words)Show less

Default Operating Rules

  1. Prefer the base pyogrio engine when available; use Fiona only when the task specifically needs Fiona behavior or installed environment supports it.
  2. Preserve and validate CRS after every round trip. File formats and engines differ in metadata support.
  3. Use columns, rows, bbox, or mask filters to reduce read size before expensive analysis.
  4. Treat Parquet/Feather/Arrow as optional pyarrow workflows; provide a GeoJSON/GPKG fallback when pyarrow is unavailable.
  5. Treat PostGIS as a service-backed workflow: Python imports are not enough; a live database, credentials, schema privileges, and geometry column conventions are required.
  6. Never assume a file driver from extension alone when a task has strict format requirements; set driver= for writes when needed.

Minimal Checks

bash
python scripts/io_roundtrip_smoke.py --format geojson
python scripts/check_optional_io_dependencies.py --json

Expected signals: the round trip returns the expected row count, CRS, geometry column, and file path suffix in a temporary directory; the dependency checker reports missing optional modules without failing unless --require is used.

Handoff

  • After data is loaded, route CRS and active geometry validation to core-data-model when needed.
  • For joins, overlays, clips, dissolves, and metric operations, route to spatial-operations.
  • For final equality checks or test fixtures, route to validation-testing.

© VectorSpaceLab, BSD-3-Clause. 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 5 other files (scripts, references) in skills/repositories/repo-skills/geopandas/sub-skills/io-formats of VectorSpaceLab/AREX-Skill.

  • SKILL.md
  • references/io-reference.md
  • references/troubleshooting.md
  • references/workflows.md
  • scripts/check_optional_io_dependencies.py
  • scripts/io_roundtrip_smoke.py

Open the folder on GitHubat commit ac3fe1a

Compare with similar skills

Io Formats 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.

Io Formats compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Io Formats this skillVectorSpaceLab/AREX-Skill331—~1kAutomated safety check: PassBSD-3-Clause
Geopandas Geospatialjaechang-hits/SciAgent-Skills3741 repos~3.8kAutomated safety check: PassBSD-3-Clause
R Python Translationbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~5.5kAutomated safety check: PassCustom licence
Antv L7antvis/L74.1k—~1.4kAutomated safety check: PassMIT
Chdb Datastorevemetric/vemetric3952 repos~1.4kAutomated safety check: PassApache-2.0
Polar Python SDKpolarsource/polar10k—~1.8kAutomated safety check: PassApache-2.0

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    374 GitHub starsUsed in 1 repo~3.8k tokens
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Categories

Questions about Io Formats

What does Io Formats do?

A skill your agent uses when reading, writing, filtering, converting, or troubleshooting GeoPandas vector files, GeoJSON, WKB/WKT, Arrow, Parquet, Feather, and PostGIS data. Io Formats is an agent skill from VectorSpaceLab/AREX-Skill. Use when reading, writing, filtering, converting, or troubleshooting GeoPandas vector files, GeoJSON, WKB/WKT, Arrow, Parquet, Feather, and PostGIS data.

When should I use Io Formats?

Io Formats fits situations like: troubleshooting GeoPandas vector files; tasks that involve Geospatial analysis; tasks that involve DataFrames.

How do I install Io Formats in Claude Code?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill io-formats -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/geopandas/sub-skills/io-formats in VectorSpaceLab/AREX-Skill) into .claude/skills/io-formats in your project. Claude Code loads it when a task matches its description.

How do I install Io Formats in Codex?

Run `npx skills add VectorSpaceLab/AREX-Skill --skill io-formats -a codex`. Or copy the skill folder (skills/repositories/repo-skills/geopandas/sub-skills/io-formats in VectorSpaceLab/AREX-Skill) into .agents/skills/io-formats in your project. Codex loads it when a task matches its description.

Can I use Io Formats 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 VectorSpaceLab/AREX-Skill --skill io-formats -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/io-formats, .gemini/skills/io-formats, .github/skills/io-formats and .opencode/skills/io-formats in your project.

What does Io Formats need to run?

Going by SKILL.md and its folder, Io Formats needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Io Formats 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 Io Formats 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 Io Formats use?

Io Formats is published under the BSD-3-Clause licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Io Formats use?

About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.7k tokens, read only when the agent opens those files.

What are the alternatives to Io Formats?

Skills that share tags, products or a category with Io Formats: Geopandas Geospatial (jaechang-hits/SciAgent-Skills, 374 stars), R Python Translation (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), Antv L7 (antvis/L7, 4.1k stars) and Chdb Datastore (vemetric/vemetric, 395 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Io Formats?

VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 331 GitHub stars. The repository holds 157 skills in this directory. The repository was last updated on September 3, 2026.

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