botaudit 1.2.0

CLI tool that grades how accessible a website is to AI clients

botaudit

CLI tool that grades how accessible a website is to AI clients.

botaudit fetches a webpage, analyzes its HTML structure, and scores it across six categories that affect how well AI crawlers and language models can discover and consume its content. The output is a letter-graded report with per-category scores and actionable recommendations.

Installation

Requires Python 3.11+.

pip install botaudit

Usage

botaudit https://example.com

Options

Flag	Description
--timeout SECONDS	HTTP request timeout (default: 10)
--format {text,json,csv}	Output format (default: text)
--no-recommendations	Suppress improvement recommendations
--skip-llm-discovery	Skip LLM discoverability analysis (no robots.txt/llms.txt fetches)
--fail-under GRADE	Exit with code 1 if grade is below GRADE (A, B, C, D, or F)

CI usage

# Fail the build if the site scores below a B
botaudit https://staging.myapp.com --fail-under B --format json

Example output

==================================================
  BotAudit Report
  https://example.com
==================================================

  Overall Grade: B (82/100)

--------------------------------------------------
  Content Availability (27%)              90/100
--------------------------------------------------
    - 342 words of visible text.
    - <noscript> fallback present.

  Semantic HTML (23%)                     68/100
--------------------------------------------------
    - 15 semantic vs 7 generic elements (ratio: 68%)
    - Semantic tags: nav (3), article (2), section (4), header (2), ...

    Recommendations:
    [MEDIUM] Wrap supplementary content (sidebars, promos) with <aside>.
    ...

Categories

Each category is scored 0-100 and weighted toward the overall grade:

Category	Weight	What it measures
Content Availability	27%	Visible text in initial HTML, <noscript> fallback
Semantic HTML	23%	Ratio of semantic elements (<article>, <nav>, ...) to generic containers (<div>, <span>)
Link Discoverability	18%	Navigable <a href> links vs javascript:, #, or empty hrefs
Structured Data	13%	JSON-LD, Open Graph tags, meta description
Metadata & Discoverability	9%	<title>, canonical URL, robots meta, sitemap reference
LLM Discoverability	10%	robots.txt AI crawler policies, llms.txt, llms-full.txt

Grades map to the overall weighted score: A (90+), B (80-89), C (70-79), D (60-69), F (<60).

Categories scoring below 90 receive actionable recommendations at HIGH, MEDIUM, or LOW severity.

Development

python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows
pip install -e .

Running tests

python -m unittest discover tests

Project structure

src/botaudit/
  cli.py                 CLI entry point and argument parsing
  fetcher.py             HTTP fetching with error handling
  analysis.py            HTML analysis (5 categories)
  robots_analysis.py     robots.txt parsing for AI crawler access
  llm_discoverability.py LLM discovery file fetching and analysis
  grading.py             Per-category scoring and overall grading
  recommendations.py     Per-category recommendation generation
  report.py              Report formatting (text, JSON, CSV)
  models.py              Shared data structures and constants