Data Table Manager
n8n-io/n8n
Load before calling data-tables or parse-file. An agent skill from n8n-io/n8n.
A skill your agent uses when preparing or validating ColBERT collection/query/ranking/qrels/LoTTE data, evaluating MSMARCO-style or LoTTE rankings, converting documents into passage TSVs, or…
Install Data And Evaluation
$ npx skills add VectorSpaceLab/AREX-Skill --skill data-and-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-evaluation --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/colbert/sub-skills/data-and-evaluation .claude/skills/data-and-evaluation && 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 "data-and-evaluation" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation into .claude/skills/data-and-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-evaluation", 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/colbert/sub-skills/data-and-evaluationType 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 data-and-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-evaluation --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/colbert/sub-skills/data-and-evaluation .agents/skills/data-and-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-and-evaluation" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation into .agents/skills/data-and-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-evaluation", 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 data-and-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-evaluation --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/colbert/sub-skills/data-and-evaluation .cursor/skills/data-and-evaluation && 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 "data-and-evaluation" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation into .cursor/skills/data-and-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-evaluation", 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/colbert/sub-skills/data-and-evaluation--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 data-and-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill data-and-evaluation --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/colbert/sub-skills/data-and-evaluation .gemini/skills/data-and-evaluation && 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 "data-and-evaluation" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation into .gemini/skills/data-and-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-evaluation", 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 data-and-evaluationInstalls 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 data-and-evaluation -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/colbert/sub-skills/data-and-evaluation .github/skills/data-and-evaluation && 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 "data-and-evaluation" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation into .github/skills/data-and-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-evaluation", 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 data-and-evaluation -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 data-and-evaluation --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/colbert/sub-skills/data-and-evaluation .opencode/skills/data-and-evaluation && 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 "data-and-evaluation" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation into .opencode/skills/data-and-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-and-evaluation", 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
data-and-evaluation- GitHub stars
- 328
- Token cost
- ~1.1k tokens
- SKILL.md length
- 361 words
- Files
- 8 (incl. scripts, references)
- Skills in repo
- 157
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when preparing or validating ColBERT collection/query/ranking/qrels/LoTTE data, evaluating MSMARCO-style or LoTTE rankings, converting documents into passage TSVs, or…
- Validating ColBERT collection/query/ranking/qrels/LoTTE data
- SKILL.md covers Quick Start, References and Scripts and Operating Notes
- Runs Python scripts from its folder; calls python
- Evaluating MSMARCO-style
What it does
Data And Evaluation is an agent skill from VectorSpaceLab/AREX-Skill. Use when preparing or validating ColBERT collection/query/ranking/qrels/LoTTE data, evaluating MSMARCO-style or LoTTE rankings, converting documents into passage TSVs, or troubleshooting data-format utility workflows. Excludes running retrieval, index updates, and training mechanics.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/api-reference.md`, `references/data-formats.md` and `references/evaluation-and-rankings.md`).
It sits in Documents & Office, covering CSV and tabular files. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is MIT.
When your agent uses it
- Validating ColBERT collection/query/ranking/qrels/LoTTE data
- Evaluating MSMARCO-style
- Converting documents into passage TSVs
- Troubleshooting data-format utility workflows
Example prompts
- “/data-and-evaluation”
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 3 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
Data And Evaluation loads about 1.1k tokens when it runs, and up to ~7.1k if it reads all its reference files. Until then it costs about 76 tokens; SKILL.md has 361 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 MIT licence (© VectorSpaceLab). 361 words, ~1,068 tokens.
Download SKILL.mdSave it as
.claude/skills/data-and-evaluation/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- data-and-evaluation
- description
- Use when preparing or validating ColBERT collection/query/ranking/qrels/LoTTE data, evaluating MSMARCO-style or LoTTE rankings, converting documents into passage TSVs, or troubleshooting data-format utility workflows. Excludes running retrieval, index updates, and training mechanics.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- MIT
ColBERT Data and Evaluation
Use this sub-skill when a task is about ColBERT data artifacts rather than model execution:
- Prepare or validate
collection.tsv,queries.tsv,ranking.tsv, qrels, LoTTE QA JSONL, or tiny fixtures. - Read, write, or reason about
Collection,Queries, andRankingobjects. - Evaluate MSMARCO-style rankings with
MRR@10andRecall@k. - Evaluate or debug LoTTE-style
Success@klayouts and ranking files. - Adapt preprocessing workflows such as converting document TSV rows into passage TSV rows.
- Split, merge, inspect, or annotate ranking files before downstream evaluation.
For retrieval that creates rankings from an index, route to indexing-and-search. For training triples, hard-negative distillation, or trainer input validation beyond basic file shape, route to training-and-distillation.
Quick Start
Validate core files before indexing, searching, training, or evaluation:
python scripts/validate_colbert_data.py --collection collection.tsv --queries queries.tsv --ranking ranking.tsv --qrels qrels.tsvRequire LoTTE-compatible scored rankings and QA JSONL:
python scripts/validate_colbert_data.py --ranking writing.search.ranking.tsv --lotte-qas qas.search.jsonl --require-score --require-sequential-qidsConvert document TSV rows into a standard passage collection with deterministic whitespace splitting:
python scripts/prepare_collection_tsv.py --input documents.tsv --output collection.tsv --format docid,text --nwords 100 --overlap 20Evaluate a tiny or full MSMARCO-style ranking without importing ColBERT:
python scripts/evaluate_tiny_ranking.py --qrels qrels.tsv --ranking ranking.tsv --depths 10 50 100Evaluate a tiny LoTTE-style QA/ranking pair:
python scripts/evaluate_tiny_ranking.py --lotte-qas qas.search.jsonl --ranking ranking.tsv --success-at 5References and Scripts
references/data-formats.mdexplains TSV, qrels, JSONL QA, LoTTE layout, and tiny fixture conventions; use it before creating or converting data files.references/api-reference.mdsummarizesCollection,Queries, andRankingbehavior; use it when writing Python code against ColBERT data wrappers.references/evaluation-and-rankings.mdexplains MSMARCO evaluation, LoTTE Success@k, annotation, split/merge helpers, and ranking utility adaptations.references/troubleshooting.mdmaps common data/config/API/workflow failures to checks and fixes; use it when validation or native utilities fail.scripts/validate_colbert_data.pyperforms deterministic local validation of collection/query/ranking/qrels/LoTTE files without Torch, FAISS, CUDA, or ColBERT imports.scripts/prepare_collection_tsv.pyconverts document TSV rows into ColBERT passage TSV rows with safe whitespace splitting and optional mapping columns.scripts/evaluate_tiny_ranking.pycomputes fixture-friendly MSMARCO-style metrics or LoTTE Success@k and can write annotated ranking rows.
Show full SKILL.md (89 more words)Show less
Operating Notes
- ColBERT package imports verified for
colbert,colbert.infra,colbert.data,colbert.modeling.checkpoint,utility, andbaleen; CPU import checks work, but indexing/training usually require CUDA/GPU. - The public package is
colbert-aiand the verified distribution version is0.2.22; import the package ascolbert. - Core data signatures are
Collection(path=None, data=None),Queries(path=None, data=None), andRanking(path=None, data=None, metrics=None, provenance=None). - Retrieval APIs produce rankings through
Searcher(index, checkpoint=None, collection=None, config=None, index_root=None, verbose=3), but retrieval execution belongs in the indexing/search sub-skill. - Native utilities often assert that output paths do not already exist; decide overwrite/delete policy before long runs.
© VectorSpaceLab, MIT. 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 7 other files (scripts, references) in skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/api-reference.md
- references/data-formats.md
- references/evaluation-and-rankings.md
- references/troubleshooting.md
- scripts/evaluate_tiny_ranking.py
- scripts/prepare_collection_tsv.py
- scripts/validate_colbert_data.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Data And Evaluation 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 |
|---|---|---|---|---|---|---|
| Data And Evaluation this skillVectorSpaceLab/AREX-Skill | 328 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Data Table Managern8n-io/n8n | 207k | — | ~2.3k | Automated safety check: Pass | Custom licence | |
| Instrument Data To Allotropeaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | 2 repos | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Abuse Hunternexu-io/harness-engineering-guide | 663 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Markitshift-labs-ai/markit | 1.3k | — | ~299 | Automated safety check: Pass | MIT | |
| Sector Analysttradermonty/claude-trading-skills | 3k | 1 repos | ~2.3k | Automated safety check: Pass | MIT |
Similar skills
- Official
- Official
Instrument Data To Allotrope
aws-samples/amazon-bedrock-agents-healthcare-lifesciences
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV.
Abuse Hunter
nexu-io/harness-engineering-guide
Detect and investigate bulk registration abuse on SaaS platforms.
Markit
shift-labs-ai/markit
Convert files and URLs to Markdown. An agent skill from shift-labs-ai/markit.
Sector Analyst
tradermonty/claude-trading-skills
This skill should be used when analyzing sector rotation patterns and market cycle positioning.
Uap Release Analyzer
ckpxgfnksd-max/uap-release-analyzer
Inventory, extract, and analyze tranches of declassified UAP/UFO files — including war.gov/UFO/ "PURSUE" releases, FBI Vault, NARA boxes, and AARO publications.
More from VectorSpaceLab/AREX-Skill
All 157 skills in this repoAgent Lightning
VectorSpaceLab/AREX-Skill
Use this repo skill for Agent Lightning package tasks: authoring trainable agents, tracing rewards and spans, running LightningStore/Trainer loops, using agl CLI services, choosing examples, and…
Agent Tools
VectorSpaceLab/AREX-Skill
A skill your agent uses when configuring LiteLLM for MCP tools, A2A agents, Claude Code/Cursor agent gateway traffic, MCP auth/OAuth, tool permissions, semantic filtering, or agent-specific proxy…
Agents And Awel
VectorSpaceLab/AREX-Skill
Build and debug DB-GPT agents, tools, skills, teams, and AWEL workflows, including deterministic local DAG runs and HTTP-trigger topology without assuming an LLM, credential, or external service.
Agents And Middleware
VectorSpaceLab/AREX-Skill
Work on the actively maintained LangChain v1 agent package: initchatmodel, createagent, structured output, tools, middleware, embeddings initialization, provider routing, and agent runtime…
Agents Workflows
VectorSpaceLab/AREX-Skill
A skill your agent uses for giskard.agents async chat workflows, tools, prompt templates, structured outputs, retries, rate limiting, embeddings, and optional LiteLLM backend.
Alphafold3
VectorSpaceLab/AREX-Skill
A skill your agent uses for AlphaFold 3 input preparation, prediction command planning, output interpretation, and Python API inspection.
Related
Categories
Questions about Data And Evaluation
What does Data And Evaluation do?
A skill your agent uses when preparing or validating ColBERT collection/query/ranking/qrels/LoTTE data, evaluating MSMARCO-style or LoTTE rankings, converting documents into passage TSVs, or…. Data And Evaluation is an agent skill from VectorSpaceLab/AREX-Skill. Use when preparing or validating ColBERT collection/query/ranking/qrels/LoTTE data, evaluating MSMARCO-style or LoTTE rankings, converting documents into passage TSVs, or troubleshooting data-format utility workflows.
When should I use Data And Evaluation?
Data And Evaluation fits situations like: validating ColBERT collection/query/ranking/qrels/LoTTE data; evaluating MSMARCO-style; converting documents into passage TSVs; troubleshooting data-format utility workflows.
How do I install Data And Evaluation in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill data-and-evaluation -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation in VectorSpaceLab/AREX-Skill) into .claude/skills/data-and-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Data And Evaluation in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill data-and-evaluation -a codex`. Or copy the skill folder (skills/repositories/repo-skills/colbert/sub-skills/data-and-evaluation in VectorSpaceLab/AREX-Skill) into .agents/skills/data-and-evaluation in your project. Codex loads it when a task matches its description.
Can I use Data And Evaluation 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 data-and-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-and-evaluation, .gemini/skills/data-and-evaluation, .github/skills/data-and-evaluation and .opencode/skills/data-and-evaluation in your project.
What does Data And Evaluation need to run?
Going by SKILL.md and its folder, Data And Evaluation needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Data And Evaluation 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 Data And Evaluation 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 Data And Evaluation use?
Data And Evaluation is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Data And Evaluation use?
About 1.1k tokens (SKILL.md is roughly 4.3k 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 6.1k tokens, read only when the agent opens those files.
What are the alternatives to Data And Evaluation?
Skills that share tags, products or a category with Data And Evaluation: Data Table Manager (n8n-io/n8n, 207k stars), Instrument Data To Allotrope (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Abuse Hunter (nexu-io/harness-engineering-guide, 663 stars) and Markit (shift-labs-ai/markit, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data And Evaluation?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 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.