Agent skill

Portaljs Check Data Quality

by datopian in datopian/portaljs

Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates.

MITAuto-check passedData & Analytics

Install Portaljs Check Data Quality

skills CLI
$ npx skills add datopian/portaljs --skill portaljs-check-data-quality -a claude-code

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

GitHub CLI
$ gh skill install datopian/portaljs portaljs-check-data-quality --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/datopian/portaljs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/portaljs-check-data-quality .claude/skills/portaljs-check-data-quality && 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
portaljs-check-data-quality
GitHub stars
2.4k
Used in
1 other repo
Token cost
~1.5k tokens
SKILL.md length
628 words
Files
2 (incl. references)
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates.

  • Works in 7 steps: Gather input — the file path or URL to… → Resolve the source: if it's an… → Validate the extension is .csv or .tsv.… → …
  • A dataset needs a quality check before publishing
  • SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
  • Calls bash

What it does

Portaljs Check Data Quality is an agent skill from datopian/portaljs. Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase renders wrong (blank cells, garbled numbers, an unsortable date column) and the cause needs isolating.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/reference.md`). Compatibility notes: Claude Code with PortalJS portals (Next.js 14, React 18, Node 18+). Runs from any project via the plugin, a personal ~/.claude/commands install, or a portaljs…

It sits in Data & Analytics, covering CSV and tabular files and Data cleaning. The repository describes itself as: 🌀 AI-native framework for building data portals. Scaffold a full portal from a brief and load datasets in minutes with agentic skills — any backend (CKAN, GitHub, Frictionless). The licence is MIT.

When your agent uses it

  • A dataset needs a quality check before publishing
  • A showcase renders wrong (blank cells
  • Garbled numbers
  • An unsortable date column) and the cause needs isolating

Example prompts

  • “/portaljs-check-data-quality”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Claude Code with PortalJS portals (Next.js 14, React 18, Node 18+). Runs from any project via the plugin, a personal ~/.claude/commands install, or a portaljs clone.
  • Pre-approved tools (allowed-tools): Bash(curl:*), Bash(awk:*), Bash(sort:*), Bash(head:*), Bash(wc:*)

Workflow steps

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

  1. Gather input — the file path or URL to audit. If missing, ask for it; never dead-end.
  2. Resolve the source: if it's an http/https URL, download it to a temp file first;
  3. Validate the extension is .csv or .tsv. If not, or the file is missing, or the
  4. Profile every column: null/blank counts, distinct values, sample values, inferred
  5. Derive findings from the profiles — duplicate rows, missing-value ratios, invalid
  6. Assemble the JSON report (status, file metadata, findings, recommendations,
  7. Relay the report to the user as-is; do not modify the source file, datasets.json,

What it can do on your machine

Read from SKILL.md and the folder at commit d5c096a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(curl:*)
    • Bash(awk:*)
    • Bash(sort:*)
    • Bash(head:*)
    • Bash(wc:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • bash

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

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.python.org

    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.

  • Compatibility

    Claude Code with PortalJS portals (Next.js 14, React 18, Node 18+). Runs from any project via the plugin, a personal ~/.claude/commands install, or a portaljs clone.

    From compatibility in the SKILL.md frontmatter.

Context cost

Portaljs Check Data Quality loads about 1.5k tokens when it runs, and up to ~2.6k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 628 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.6k

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

The full file from datopian/portaljs at commit d5c096a, republished under its MIT licence (© datopian). 628 words, ~1,484 tokens.

Download SKILL.mdSave it as .claude/skills/portaljs-check-data-quality/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
portaljs-check-data-quality
description
Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Read-only. Use when a dataset needs a quality check before publishing, or a showcase renders wrong (blank cells, garbled numbers, an unsortable date column) and the cause needs isolating.
allowed-tools
Bash(curl:*), Bash(awk:*), Bash(sort:*), Bash(head:*), Bash(wc:*)
compatibility
Claude Code with PortalJS portals (Next.js 14, React 18, Node 18+). Runs from any project via the plugin, a personal ~/.claude/commands install, or a portaljs clone.
version
1.0.0
author
Datopian <hello@datopian.com>
license
MIT
tags
portaljs, data-portal, data-quality, audit, csv, validation

PortalJS — Check Data Quality

Overview

Run a read-only quality audit of one CSV or TSV file, local or remote, and return a structured JSON report. The audit profiles every column — null/blank counts, inferred value types, numeric ranges, likely year/date fields — and flags duplicate rows, duplicate values in identifier-like columns, ambiguous overlapping year columns (e.g. calendar year vs fiscal year), and mixed-type columns. It never edits the source file, datasets.json, or any other project file; it only reads the target file (a remote URL is downloaded to a temp file that is deleted before the run ends) and prints a report. Use it before publishing a dataset with portaljs-add-dataset, or to diagnose why a showcase renders wrong.

Prerequisites

  • python3 on PATH — the audit logic runs as an embedded Python script; nothing is installed.
  • One CSV or TSV file, given as a local path or an http/https URL. Only one file per run.

Instructions

The canonical, full step-by-step workflow is .claude/commands/portaljs-check-data-quality.md — the single source of truth. Read and follow it when executing. Summary:

  1. Gather input — the file path or URL to audit. If missing, ask for it; never dead-end.
  2. Resolve the source: if it's an http/https URL, download it to a temp file first; otherwise use the local path as given.
  3. Validate the extension is .csv or .tsv. If not, or the file is missing, or the header row is empty, stop and surface the error JSON as-is — do not guess a fix.
  4. Profile every column: null/blank counts, distinct values, sample values, inferred per-value type (boolean/integer/float/date/string), numeric min/max, and year range for columns whose name looks year-like.
  5. Derive findings from the profiles — duplicate rows, missing-value ratios, invalid year values, mixed types, suspect negative values, duplicate identifier values, and ambiguous overlapping year columns — each tagged critical, warning, or info.
  6. Assemble the JSON report (status, file metadata, findings, recommendations, column_profiles), print it, and clean up the temp file if one was created.
  7. Relay the report to the user as-is; do not modify the source file, datasets.json, or any other project file based on the findings — that's a separate, explicit step.
Show full SKILL.md (275 more words)Show less

Output

A single JSON object printed to stdout:

  • status — ok, warning, or critical.
  • file, file_name, source_type (local or url), row_count, column_count.
  • findings — structured issues, most severe first.
  • recommendations — de-duplicated suggested next steps.
  • column_profiles — per-column summary (nulls, blanks, distinct count, sample values, inferred types, numeric/year ranges).

No files are created or modified. A remote URL's temp download is removed on exit, success or failure alike.

Error Handling

SymptomCauseFix
"File ... is not available."Local path is wrong, or the URL download failedVerify the path or URL is reachable and retry.
"Only CSV and TSV files are supported right now."File extension isn't .csv/.tsvConvert the file, or point to its tabular source instead.
"... does not contain tabular headers."File is empty or the header row is malformedOpen the file and confirm it has a valid, non-empty header line.
Command hangs on a URLRemote host is slow or blocks non-browser requestsDownload the file manually and audit the local copy instead.
python3: command not foundPython 3 isn't installed or not on PATHInstall Python 3, or run the audit where it's available.
Report looks truncated in the terminalLarge report wrapped/paginated by the shellRedirect to a file (> report.json) and open it separately.

Examples

Example 1 — Audit a local CSV before publishing
/portaljs-check-data-quality ./public/data/trash.csv
Example 2 — Audit a remote CSV over HTTPS
/portaljs-check-data-quality https://example.com/trash.csv
Example 3 — Audit a TSV and save the report for review
bash
bash scripts/check-data-quality.sh ./data/emissions.tsv > /tmp/emissions-quality.json
Example 4 — Read a critical status report
json
{
  "status": "critical",
  "findings": [
    { "severity": "critical", "check": "duplicate_rows", "message": "42 duplicate rows found." }
  ],
  "recommendations": ["Review and deduplicate repeated rows if they are not intentional."]
}

Fix the flagged rows/columns, then re-run the audit before publishing.

Resources

© datopian, 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 1 other file (references) in skills/portaljs-check-data-quality of datopian/portaljs.

  • SKILL.md
  • references/reference.md

Open the folder on GitHubat commit d5c096a

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 datopian/portaljs, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Portaljs Check Data Quality 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.

Portaljs Check Data Quality compared with similar skills
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Portaljs Check Data Quality this skilldatopian/portaljs2.4k1 repos~1.5kAutomated safety check: PassMIT
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Education Cloud Course Catalog Migrateforcedotcom/sf-skills1.1k—~5.4kAutomated safety check: PassApache-2.0
CSV Processingbenchflow-ai/skillsbench1.8k—~455Automated safety check: PassApache-2.0
Visual Skillsnpc-live/clawfirm156—~7.4kAutomated safety check: PassNone
XLSX Spreadsheet ToolkitXiaomiMiMo/MiMo-Code14k—~2.9kAutomated safety check: PassApache-2.0

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Questions about Portaljs Check Data Quality

What does Portaljs Check Data Quality do?

Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates. Portaljs Check Data Quality is an agent skill from datopian/portaljs. Audit a local or remote tabular file (CSV/TSV) for common data quality issues — schema, nulls, types, duplicates.

When should I use Portaljs Check Data Quality?

Portaljs Check Data Quality fits situations like: A dataset needs a quality check before publishing; A showcase renders wrong (blank cells; garbled numbers; an unsortable date column) and the cause needs isolating.

How do I install Portaljs Check Data Quality in Claude Code?

Run `npx skills add datopian/portaljs --skill portaljs-check-data-quality -a claude-code`. Or copy the skill folder (skills/portaljs-check-data-quality in datopian/portaljs) into .claude/skills/portaljs-check-data-quality in your project. Claude Code loads it when a task matches its description.

How do I install Portaljs Check Data Quality in Codex?

Run `npx skills add datopian/portaljs --skill portaljs-check-data-quality -a codex`. Or copy the skill folder (skills/portaljs-check-data-quality in datopian/portaljs) into .agents/skills/portaljs-check-data-quality in your project. Codex loads it when a task matches its description.

Can I use Portaljs Check Data Quality 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 datopian/portaljs --skill portaljs-check-data-quality -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/portaljs-check-data-quality, .gemini/skills/portaljs-check-data-quality, .github/skills/portaljs-check-data-quality and .opencode/skills/portaljs-check-data-quality in your project.

What does Portaljs Check Data Quality need to run?

Going by SKILL.md and its folder, Portaljs Check Data Quality needs the command-line tools its instructions call (bash). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash(curl:*), Bash(awk:*), Bash(sort:*), Bash(head:*), Bash(wc:*). Compatibility (from SKILL.md): Claude Code with PortalJS portals (Next.js 14, React 18, Node 18+). Runs from any project via the plugin, a personal ~/.claude/commands install, or a portaljs clone..

Does Portaljs Check Data Quality access the network?

SKILL.md names 1 domain. As links in the text: docs.python.org. This is read from the text; nothing was executed.

Is Portaljs Check Data Quality 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 Portaljs Check Data Quality use?

Portaljs Check Data Quality 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 Portaljs Check Data Quality use?

About 1.5k tokens (SKILL.md is roughly 5.9k 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 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Portaljs Check Data Quality?

Skills that share tags, products or a category with Portaljs Check Data Quality: Dataset Quality Audit (zebbern/claude-code-guide, 4.7k stars), Education Cloud Course Catalog Migrate (forcedotcom/sf-skills, 1.1k stars), CSV Processing (benchflow-ai/skillsbench, 1.8k stars) and Visual Skills (npc-live/clawfirm, 156 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Portaljs Check Data Quality?

datopian (a GitHub organization) maintains it in datopian/portaljs, which has 2,358 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 8, 2026.

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