Chdb Datastore
vemetric/vemetric
A skill your agent uses when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas.
Use this Dask sub-skill for Dask DataFrame creation, CSV/Parquet/JSON/SQL IO, partitions and divisions, groupby/aggregation, joins/merge, shuffle, repartitioning, categorical/string/pyarrow…
Install Dataframe Workflows
$ npx skills add VectorSpaceLab/AREX-Skill --skill dataframe-workflows -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill dataframe-workflows --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows .claude/skills/dataframe-workflows && 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 "dataframe-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows into .claude/skills/dataframe-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataframe-workflows", 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflowsType 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 VectorSpaceLab/AREX-Skill --skill dataframe-workflows -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill dataframe-workflows --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows .agents/skills/dataframe-workflows && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dataframe-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows into .agents/skills/dataframe-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataframe-workflows", 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 VectorSpaceLab/AREX-Skill --skill dataframe-workflows -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill dataframe-workflows --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows .cursor/skills/dataframe-workflows && 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 "dataframe-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows into .cursor/skills/dataframe-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataframe-workflows", 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/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows--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 VectorSpaceLab/AREX-Skill --skill dataframe-workflows -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill dataframe-workflows --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows .gemini/skills/dataframe-workflows && 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 "dataframe-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows into .gemini/skills/dataframe-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataframe-workflows", 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 VectorSpaceLab/AREX-Skill dataframe-workflowsInstalls 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 VectorSpaceLab/AREX-Skill --skill dataframe-workflows -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows .github/skills/dataframe-workflows && 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 "dataframe-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows into .github/skills/dataframe-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataframe-workflows", 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 VectorSpaceLab/AREX-Skill --skill dataframe-workflows -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill dataframe-workflows --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows .opencode/skills/dataframe-workflows && 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 "dataframe-workflows" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows into .opencode/skills/dataframe-workflows/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dataframe-workflows", 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
dataframe-workflows- GitHub stars
- 328
- Token cost
- ~1k tokens
- SKILL.md length
- 382 words
- Files
- 7 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- BSD-3-Clause
At a glance
Use this Dask sub-skill for Dask DataFrame creation, CSV/Parquet/JSON/SQL IO, partitions and divisions, groupby/aggregation, joins/merge, shuffle, repartitioning, categorical/string/pyarrow…
- Tasks that involve DataFrames
- SKILL.md covers Route Here For, Route Elsewhere, Start With These References and Bundled Smoke Scripts, plus 1 more section
- Runs Python scripts from its folder; calls python
What it does
Dataframe Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Use this Dask sub-skill for Dask DataFrame creation, CSV/Parquet/JSON/SQL IO, partitions and divisions, groupby/aggregation, joins/merge, shuffle, repartitioning, categorical/string/pyarrow handling, and dask-expr query planning/optimizer behavior.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `references/api-reference.md`, `references/io-and-data-formats.md` and `references/troubleshooting.md`).
It sits in Data & Analytics, covering DataFrames. It works with Dask, SQL and pandas. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is BSD-3-Clause.
When your agent uses it
- Tasks that involve DataFrames
Example prompts
- “/dataframe-workflows”
Requirements
- Python 3
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
Dataframe Workflows loads about 1k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 382 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 VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its BSD-3-Clause licence (© VectorSpaceLab). 382 words, ~1,013 tokens.
Download SKILL.mdSave it as
.claude/skills/dataframe-workflows/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.- name
- dataframe-workflows
- description
- Use this Dask sub-skill for Dask DataFrame creation, CSV/Parquet/JSON/SQL IO, partitions and divisions, groupby/aggregation, joins/merge, shuffle, repartitioning, categorical/string/pyarrow handling, and dask-expr query planning/optimizer behavior.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- BSD 3-Clause
Dask DataFrame Workflows
Use this sub-skill when a task is about pandas-like tabular workflows with dask.dataframe or the dask_expr DataFrame implementation.
Route Here For
- Creating DataFrames with
dd.from_pandas,dd.from_map,dd.from_delayed,dd.from_dask_array,dd.read_csv,dd.read_parquet,dd.read_json, and SQL readers. - Planning CSV, Parquet, JSON, ORC, HDF, SQL, cloud-storage, and partitioned dataset reads/writes.
- Reasoning about
npartitions,divisions,known_divisions,set_index,repartition,shuffle, and partition sizing. - Implementing
groupby,Aggregation,split_out, joins, merges, index-aware operations, and shuffle-aware query plans. - Handling
meta, metadata inference, categorical known/unknown state, pandas/pyarrow string conversion, pyarrow-backed dtypes, and dataframe backends. - Inspecting or explaining dataframe query planning with
optimize(),pprint(),explain(), projection/filter pushdown, partition pruning, and shuffle avoidance.
Route Elsewhere
- Use
../configuration-diagnostics-cli/SKILL.mdfor generic Dask config mechanics, CLI commands, progress bars, profilers, install checks, and scheduler diagnostics. - Use
../array-workflows/SKILL.mdfor Dask Array creation, chunking, blockwise array operations, gufuncs, and array/dataframe conversion details beyondfrom_dask_arrayorto_dask_arrayrouting. - Use
../bag-bytes-workflows/SKILL.mdfor bag-first text/JSON records, bytes, Avro, and object pipelines before conversion to dataframe. - Use
../core-graphs-schedulers/SKILL.mdfor generic task graphs, delayed,compute,persist, custom collection protocol, and scheduler selection.
Start With These References
references/api-reference.mdfor public DataFrame APIs, method selection, signatures, and dask-expr inspection surfaces.references/io-and-data-formats.mdfor CSV, Parquet, JSON, SQL, cloud storage, backend dispatch, and format-specific pitfalls.references/workflows.mdfor practical workflow recipes covering divisions, joins, groupby, repartitioning,meta, categoricals, and optimizer-aware planning.references/troubleshooting.mdfor missing dependencies, pyarrow strings, unknown divisions, shuffles, metadata failures, categories, Parquet schema/filter issues, and import-time config.
Show full SKILL.md (147 more words)Show less
Bundled Smoke Scripts
Run these from this sub-skill directory or pass their paths explicitly:
python scripts/dataframe_smoke.py --help
python scripts/dataframe_smoke.py
python scripts/dataframe_demo_smoke.py --help
python scripts/dataframe_demo_smoke.pyThe scripts use tiny temporary or in-memory data, public dask.dataframe APIs, and local/synchronous computation. They do not depend on repository files or write persistent datasets unless you pass an output path.
Operating Rules
- Keep dataframe pipelines lazy while defining work; call
.compute()or.persist()only at execution boundaries or in small smoke checks. - Prefer Parquet for durable tabular datasets; use CSV/JSON for ingestion or interchange when schema and partitioning limits are acceptable.
- Preserve or create useful divisions for repeated
.loc, index joins, and groupby/apply on the index; avoid unnecessary full-data shuffles. - Provide explicit
metafor user functions, custom readers, empty/heterogeneous partitions, or workflows where metadata inference is expensive or wrong. - Treat
dataframe.query-planning,dataframe.convert-string, and dataframe backend config as import-time-sensitive choices; set them before importingdask.dataframein fresh processes when behavior must be deterministic.
© 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 6 other files (scripts, references) in skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/api-reference.md
- references/io-and-data-formats.md
- references/troubleshooting.md
- references/workflows.md
- scripts/dataframe_demo_smoke.py
- scripts/dataframe_smoke.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Dataframe Workflows 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 |
|---|---|---|---|---|---|---|
| Dataframe Workflows this skillVectorSpaceLab/AREX-Skill | 328 | — | ~1k | Automated safety check: Pass | BSD-3-Clause | |
| Chdb Datastorevemetric/vemetric | 394 | 2 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Querying Big Datasetsflyrank-bih/flyrank-ml-internship-starter | 140 | — | ~750 | Automated safety check: Pass | Custom licence | |
| Daskdavila7/claude-code-templates | 32k | 11 repos | ~3.5k | Automated safety check: Pass | MIT | |
| DaskK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~4.4k | Automated safety check: Notes | BSD-3-Clause | |
| Transforming Dataancoleman/ai-design-components | 526 | — | ~3k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Dataframe Workflows
What does Dataframe Workflows do?
Use this Dask sub-skill for Dask DataFrame creation, CSV/Parquet/JSON/SQL IO, partitions and divisions, groupby/aggregation, joins/merge, shuffle, repartitioning, categorical/string/pyarrow…. Dataframe Workflows is an agent skill from VectorSpaceLab/AREX-Skill. Use this Dask sub-skill for Dask DataFrame creation, CSV/Parquet/JSON/SQL IO, partitions and divisions, groupby/aggregation, joins/merge, shuffle, repartitioning, categorical/string/pyarrow handling, and dask-expr query planning/optimizer behavior.
When should I use Dataframe Workflows?
Dataframe Workflows fits situations like: tasks that involve DataFrames.
How do I install Dataframe Workflows in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill dataframe-workflows -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows in VectorSpaceLab/AREX-Skill) into .claude/skills/dataframe-workflows in your project. Claude Code loads it when a task matches its description.
How do I install Dataframe Workflows in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill dataframe-workflows -a codex`. Or copy the skill folder (skills/repositories/repo-skills/dask/sub-skills/dataframe-workflows in VectorSpaceLab/AREX-Skill) into .agents/skills/dataframe-workflows in your project. Codex loads it when a task matches its description.
Can I use Dataframe Workflows 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 dataframe-workflows -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dataframe-workflows, .gemini/skills/dataframe-workflows, .github/skills/dataframe-workflows and .opencode/skills/dataframe-workflows in your project.
What does Dataframe Workflows need to run?
Going by SKILL.md and its folder, Dataframe Workflows needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Dataframe Workflows 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 Dataframe Workflows 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 Dataframe Workflows use?
Dataframe Workflows 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 Dataframe Workflows use?
About 1k tokens (SKILL.md is roughly 4.1k 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 6.6k tokens, read only when the agent opens those files.
What are the alternatives to Dataframe Workflows?
Skills that share tags, products or a category with Dataframe Workflows: Chdb Datastore (vemetric/vemetric, 394 stars), Querying Big Datasets (flyrank-bih/flyrank-ml-internship-starter, 140 stars), Dask (davila7/claude-code-templates, 32k stars) and Dask (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dataframe Workflows?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 159 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.