Spreadsheet Auditor

Audit existing Excel spreadsheets and financial models for correctness defects — formula errors, broken references, bad ranges, totals that don't reconcile, and data-quality risks.

spreadsheet-auditor is a portable Agent Skill for Claude and Codex (and a standalone command-line toolkit) that answers one question: "Can I trust this spreadsheet?"

Most spreadsheet tooling helps you build workbooks. This one audits a workbook you already have — often one you inherited — and returns a severity-ranked report of where it is likely wrong, fragile, or inconsistent. It is deterministic-first: bundled Python checks produce candidate findings with exact cell locations and evidence, so you are not eyeballing cells and guessing.

It is intentionally audit-only: it reports defects and optional annotations, but never silently rewrites, reformats, or "fixes" your source workbook.

[!TIP] See the auditor in action without installing anything: open examples/demo_audit_report.md or examples/demo_audit_report.html.

Demo (60 seconds)

pip install "spreadsheet-auditor[all]"
spreadsheet-auditor --demo --summary

Or against a workbook of your own:

spreadsheet-auditor path/to/workbook.xlsx \
    --out audit_report.md \
    --json findings.json \
    --annotated audit_annotated.xlsx

A complete demo lives in examples/ with a generator (examples/make_demo.py), the seeded workbook, and the resulting report/JSON/HTML/annotated outputs ready to inspect.

Why use it

• Pre-send model review — de-risk an LBO/DCF/budget model before it reaches an investment committee or counterparty.
• "Find the error" debugging — a number looks wrong and you need the cell, not a guess.
• Inherited-model trust assessment — you didn't build it; check whether it is trustworthy.
• CI / batch gating — fail a pipeline when a committed workbook has Critical defects (see examples/github-actions/).
• FP&A and month-end close — catch reconciliation and range mistakes in recurring workbooks.

What it detects

Each finding carries a detection mode (DET deterministic / HEUR heuristic), an error-confidence level (Defect / Likely defect / Review / Info), a severity, evidence, and a suggested fix. Full rule list: references/check_catalog.md.

The seeded-defect benchmark is published at benchmarks/seeded_defects_matrix.md; methodology at references/benchmark_methodology.md.

Install

pip install spreadsheet-auditor             # core + .xlsx/.xlsm/.csv audit
pip install "spreadsheet-auditor[all]"      # adds defusedxml, PyYAML

For local development:

git clone https://github.com/petehottelet/spreadsheet-auditor.git
cd spreadsheet-auditor
pip install -e ".[dev]"
spreadsheet-auditor --healthcheck
python -m pytest tests -q

Releases (signed source archive + Claude/Codex skill zips + SHA-256 checksums) are published from the Releases page.

Run

# Quick look in the terminal
spreadsheet-auditor model.xlsx

# Write artifacts in every supported format
spreadsheet-auditor model.xlsx \
    --out report.md \
    --json findings.json \
    --annotated annotated.xlsx
spreadsheet-auditor model.xlsx --format html --out report.html
spreadsheet-auditor model.xlsx --format sarif --out report.sarif

# CI-friendly: one-screen summary + non-zero exit on High-or-worse
spreadsheet-auditor model.xlsx --summary --fail-on High

# Bundled demo for a 60-second tour
spreadsheet-auditor --demo --summary

See spreadsheet-auditor --help for the full flag reference, including --strict, --show-suppressed, --quiet, --config, --ignore, --recalc-timeout, and --healthcheck --json.

Outputs

• Markdown report for human review, grouped by Confirmed/Likely/Review buckets so the worst items are easy to triage.
• HTML report for browser review or sharing with non-technical reviewers (self-contained, no network).
• JSON findings for reruns, CI, and downstream tooling. Validates against schemas/findings.schema.json. Each finding carries a stable fingerprint for diffing across runs and for fingerprint- based suppression.
• SARIF 2.1.0 for GitHub code scanning. See examples/github-actions/code-scanning.yml.
• Annotated workbook copy with comments at finding cells (--annotated). The source workbook is never modified. The copy is written by openpyxl, which does not carry over drawings, charts, images, or form controls; the audit warns when the source contains them, so keep the original as the master.

Exit codes

Benign limitations (no recalculation engine, missing optional packages) do not fail a normal run. Use --strict to surface them as exit code 2 in CI.

Agent Skill usage

Drop the Claude/Codex zip from the release into your Skills folder, or load it directly with Cursor Skills. Ask the agent something like:

"Audit Q3_Forecast.xlsx and tell me what's wrong with it. Write the report to audit_report.md and produce an annotated copy."

The skill maps the request onto the bundled CLI invocation and surfaces the findings inline.

Configuration

Optional config file (JSON or YAML) controls scope, materiality, suppression, performance limits, and which checks fire. Schema: schemas/config.schema.json.

Example:

scope:
  include_sheets: [Budget, Summary]
  headline_outputs: [Summary!C1, Summary!C2]
materiality:
  absolute: 1000.0
  relative: 0.001
finance:
  enabled: true        # turn on the finance HEUR pack
limits:
  max_formulas: 50000
  timeout_seconds: 120
  max_reported_findings: 200
checks:
  IFERROR_MASK: review   # warn instead of error
  LITERAL_CONSTANT: off
suppressions:
  - rule_id: BROKEN_REFERENCE
    range: Imports!A1:A100
    reason: External feed populated at runtime; cells start empty.

Safety

Spreadsheet files are treated as untrusted input. Macros are inventoried but never executed, external links are inventoried but never followed, recalculation runs headless in an isolated profile, and the original workbook is never overwritten. Details in SECURITY.md and references/limitations.md.

Limitations

Excel-specific behavior is approximated via LibreOffice. Value-dependent checks (TOTAL_MISMATCH, CROSS_FOOT_FAILURE) require either recalculation (LibreOffice/Calc) or cached values written by Excel. Dynamic arrays, data tables, Power Query/Data Model, and macros are inventoried but not executed. Full list: references/limitations.md.

Google Sheets: export to .xlsx and audit that. There is no native Sheets API integration. See references/google_sheets.md for a recipe.

FAQ

Does it fix my spreadsheet? No. It reports defect candidates with suggested fixes; applying changes is a separate, explicit step.

Is this an accounting or audit certification? No. It flags likely defects and review items; it does not certify business, accounting, tax, or legal correctness.

Does it work without Excel installed? Yes. It reads workbooks with openpyxl. LibreOffice is optional and only used to refresh cached values for value-dependent checks.

Does it support Google Sheets? Not directly. Export to .xlsx and audit that.

How are false positives handled? Suppress them by (rule_id, range, reason) or by fingerprint. The range may be a cell, a range, a whole column or row, or a bare sheet name; a finding is suppressed when its cell lies inside that target on the same sheet, never by text prefix (Imports!A1 does not hide Imports!A10). A reason is required; suppressions missing a reason are ignored and called out in the report's coverage limitations. Suppressed findings stay in the JSON payload (auditable) but are hidden from the report unless --show-suppressed is passed.

Contributing

We welcome new checks, additional test workbooks, and benchmark improvements. See CONTRIBUTING.md and references/custom_checks.md.

License

MIT. See LICENSE.

_{Topics: spreadsheet audit, excel auditor, xlsx, xlsm, financial model review, formula error checker, reconciliation, cross-foot, range check, circular reference detection, data quality, openpyxl, python, CLI, agent skill, static analysis, claude, codex, FP&A, financial modeling, fpna.}