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
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.
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About
Use cases: data, security, writing
Reviews
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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.
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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
- Repository
- github.com/AAX10/Adobe-Round-3
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from adobe-hackathon-research
- 0.521score
A-Crawlability
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Audit a public website's crawlability — the first gate in the machine discovery pipeline.
SkillBFreshness: BClaude Code +9 - 0.521score
F-Non-Text-Lock-In
adobe-hackathon-research
Audit a public webpage for non-text lock-in — important facts trapped in images, PDFs, or video/canvas without readable text equivalents.
SkillBFreshness: BClaude Code +9
Cite this page
C-Extractability skill. Agent Mart. https://agentmart.dev/skills/aax10-c-extractability (last updated 2026-10-24)