# Quietforge Dataset Quality Audit

> Quietforge Dataset Quality Audit is a paid API for AI agents from qf-api.quietforge-studio.workers.dev, paid per call via x402, $0.03/call, status unknown (last checked 2026-10-11).

Audits a CSV or JSON dataset for duplicates, missing values, type inconsistencies, class imbalance, numeric outliers, and PII patterns, returning a structured quality report

## Facts

- Endpoint: POST https://qf-api.quietforge-studio.workers.dev/v1/dataset-audit?utm_source=zero.xyz
- Price: $0.03/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-11
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/quietforge-dataset-quality-audit-4d689025
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ytvThAlKiK5Kvnbnhzy0T

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability quietforge-dataset-quality-audit-4d689025 -d '<json body>'
```

Example prompt: Can you run a data quality audit on this CSV I'm about to paste — I need to know about duplicate rows, missing values, outliers, and whether any column looks like it contains emails or other PII?

## When to prefer this

Choose this endpoint when you need a fast, pay-per-call dataset quality audit with no signup — especially useful for one-off checks before ML training, data sharing, or pipeline validation. It covers a broad set of quality dimensions (duplicates, missing values, type consistency, outliers, PII) in a single call, returning a machine-readable JSON report with a summary quality score. Prefer it over manual pandas profiling when you need a quick automated check from an agent workflow without standing up local infrastructure.

## Known failure modes

- Dataset exceeds 5 MB, 50,000 rows, 200 columns, or 2,000,000 cells — returns an error
- Malformed CSV or non-flat JSON array — parsing fails with an error response
- Payment not included or insufficient — returns HTTP 402 requiring x402 USDC payment on Base
- Empty dataset — may return minimal output with no meaningful issues
- Auto-detection misidentifies format — caller should specify 'csv' or 'json' explicitly

## How this service works

Pay-per-call document conversion, PDF text extraction, headless-browser rendering of a URL or HTML to PNG / JPEG / PDF, a health-probed directory of x402 pay-per-call endpoints, a sudoku generator (solver-verified, technique-graded puzzles as JSON or a print-ready A4/Letter book), a book typesetter (prose manuscript to a KDP-ready interior PDF + editable docx), a crossword generator (verified freeform grid from your clue/answer pairs as JSON or print-ready PDFs), a family tree chart layout engine (descendant / ancestor / hourglass charts from people+families JSON or a GEDCOM file, as verified layout JSON or print-ready poster and tiled PDFs), and a dataset quality audit tool (duplicates, missing values, type consistency, class imbalance, numeric outliers and PII-pattern counts for a CSV/JSON dataset, as JSON or a one-page PDF summary). Payment: x402 (USDC on Base). No signup, no API key.

## Output

Returns a JSON object with: a 'quality_score' (0-100), an 'issues' array of human-readable problem descriptions, a 'columns' array with per-column dtype, missing count/percentage, outlier count, and mixed-type flag, plus 'duplicate_row_count', 'duplicate_row_indexes', 'pii_flags' listing any columns matching PII patterns (email, phone, etc.), 'row_count', 'col_count', and 'elapsed_ms'. Optionally available as a one-page PDF summary.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "data": {
   "type": "string",
   "description": "the dataset itself: CSV text, or a JSON array of flat objects. Max 5242880 bytes, 50000 rows, 200 columns, and 2000000 cells (rows x columns) — the cell cap binds first on wide datasets."
  },
  "format": {
   "enum": [
    "auto",
    "csv",
    "json"
   ],
   "type": "string",
   "description": "auto (default, sniffs '[' for JSON), csv, or json"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "issues": [
   "1 duplicate row(s) (25.0% of rows)",
   "Column 'age' is 25.0% missing",
   "Column 'email' looks like it may contain email-pattern data (2 match(es))"
  ],
  "columns": [
   {
    "name": "id",
    "dtype": "int",
    "mixed_type": false,
    "missing_pct": 0,
    "missing_count": 0
   },
   {
    "name": "name",
    "dtype": "string",
    "mixed_type": false,
    "missing_pct": 0,
    "missing_count": 0
   },
   {
    "name": "email",
    "dtype": "string",
    "mixed_type": false,
    "missing_pct": 0,
    "missing_count": 0
   },
   {
    "name": "age",
    "dtype": "int",
    "mixed_type": false,
    "missing_pct": 25,
    "missing_count": 1,
    "outlier_count": 0
   }
  ],
  "col_count": 4,
  "pii_flags": [
   {
    "column": "email",
    "pattern": "email",
    "match_count": 2
   }
  ],
  "row_count": 4,
  "elapsed_ms": 3,
  "quality_score": 79,
  "duplicate_row_count": 1,
  "duplicate_row_indexes": [
   2
  ]
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/quietforge-dataset-quality-audit-4d689025/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from qf-api.quietforge-studio.workers.dev](https://www.zero.xyz/host/qf-api.quietforge-studio.workers.dev/llms.txt)