Skip to content
Agentmart
Submit a tool

C-Extractability

Audit a public webpage's machine-readability — whether important facts can be correctly extracted by AI and machine readers.

Skill by adobe-hackathon-research

IndexedMIT
Write a review

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/aax10-c-extractability

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Audit a public webpage's machine-readability — whether important facts can be correctly extracted by AI and machine readers. Tests render-dependent content (C-001), render-dependent structured data (C-002), and ambiguous fact-label associations (C-003). Use when checking if page content is correctly parseable.

Use cases: data, security, writing

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found
Runs as
Python package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/c-extractability

What's inside

Skill name
C-Extractability
Description
Audit a public webpage's machine-readability — whether important facts can be correctly extracted by AI and machine readers. Tests render-dependent content (C-001), render-dependent structured data (C-002), and ambiguous fact-label associations (C-003). Use when checking if page content is correctly parseable.
License
MIT
Metadata
author: adobe-hackathon-research · version: 1.0 · mechanism: read/extract · hypotheses: C-001, C-002, C-003
Instructions
53 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Agent Skills spec issues

  • name: only lowercase letters, digits and single hyphens allowed
  • files: referenced but missing: references/extractability-sources.md, references/severity-model.md

Files (10)

  • SKILL.md
  • C-001.md · hypotheses/
  • C-002.md · hypotheses/
  • C-003.md · hypotheses/
  • references.md
  • check_render_factual_content.py · scripts/
  • extractability_report.py · scripts/
  • fact_inventory.py · scripts/
  • semantic_association_audit.py · scripts/
  • structured_data_compare.py · scripts/

SKILL.md (first lines)

# Extractability Audit

## When to use

Use when you need to check whether a page's important facts are **correctly extractable** by machine readers. This skill tests three extraction failure modes:

- **Information Loss** (C-001): Are facts absent from initial HTML, requiring JS to appear?
- **Semantic Loss** (C-002): Is structured data (JSON-LD) only available after JS rendering?
- **Association Loss** (C-003): Are factual values unassociated with semantic labels?

## Inputs

| Input | Required | Description |
|-------|----------|-------------|
| `--url` | Yes* | URL of the page to audit |
| `--html` | Yes* | Path to a local HTML file to audit |
| `--raw` | For C-001/C-002 | Path to raw HTML file (before JS) |
| `--rendered` | For C-001/C-002 | Path to rendered HTML file (after JS) |
| `--output` | No | Output JSON file path |

*Provide either `--url` or `--html`.

## Procedure

1. **C-003 — Semantic association audit** (no rendering required):
   - Scan page text for factual values (prices, measurements, dates, dimensions)
   - For each value, check DOM for semantic label association (`<th>`, `<dt>`, `<label>`, ARIA)
   - Check if JSON-LD provides explicit attribute-value mapping
   - Flag values with no semantic association and no JSON-LD coverage

2. **C-001 — Render-dependent factual content** (requires Playwright headless browser):
   - Automatically fetch raw HTML via direct request
   - Automatically render the URL headlessly via Playwright
   - Extract structural text blocks (headings, paragraphs) from the rendered DOM
   - Verify if those exact text blocks exist anywhere in the raw HTML payload
   - Flag facts that are only visible to a JS execution engine

3. **C-002 — Structured data comparison** (requires raw + rendered HTML):
   - Extract JSON-LD blocks from raw and rendered HTML
   - Filter for relevant schema types (Product, FAQPage, etc.)
   - Identify render-only schemas
   - Check if schema field values exist in raw HTML text (semantic vs information loss)

## Output

Each check produces JSON with: `check_id`, `status`, `severity`, `evidence`, `reason`, `suggested_action`.

## References

- `references/extractability-sources.md` — Authoritative sources (WCAG, Schema.org, Google)
- `references/severity-model.md` — Severity framework

From AAX10/Adobe-Round-3, MIT.

More from AAX10/Adobe-Round-3

Signals

Stars
0
Forks
0
Open issues
0
Last commit (this folder)
Sep 11, 2026 (1 month ago)

From GitHub (fetched Oct 24, 2026).

Details

Version
1.0
License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from adobe-hackathon-research

Cite this page

C-Extractability skill. Agent Mart. https://agentmart.dev/skills/aax10-c-extractability (last updated 2026-10-24)