Quietforge Dataset Quality Audit

x402

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

Price / call$0.03

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StatusUnknown

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.

Example use cases

Pre-training dataset validation

“Before I fine-tune this model, can you audit this CSV dataset for me — flag any duplicate rows, missing values, outliers, and tell me the overall quality score so I know if it's ready to use?”

PII scan before data sharing

“I need to share this customer JSON dataset with a third party — can you check it for any columns that might contain email addresses, phone numbers, or other PII patterns before I send it over?”

Data pipeline health check

“Here's a CSV export from our database — can you analyze it for type inconsistencies, high missing-value columns, and duplicate records, and give me a summary of the issues found?”

Technical details

Endpoint

POSThttps://qf-api.quietforge-studio.workers.dev/v1/dataset-audit?utm_source=zero.xyz

When to prefer

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.

Side effects

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

Input

Schema · Request body

Field	Type	Description
data	string	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	string	auto (default, sniffs '[' for JSON), csv, or json

Output

Schema · Response

{
  "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
    ]
  }
}

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