Context Retention / Scent Trail Audit
When to Use
Use this skill during an engagement audit when a visitor is expected to arrive from an AI-generated citation with a specific fact in mind.
Inputs
html_content: The raw HTML document already fetched for the audit.url: Target webpage URL, used by the calling orchestrator for audit context.
Procedure
- Pass the already-fetched HTML to
scripts/check_anchors.pythrough standard input. - The script uses its own single-pass
ContextRetentionParser; it does not fetch the URL or build a second DOM tree. - Check for a substantive paragraph before the first
<h2>and for fragment targets or in-page fragment links. - Emit the script output unchanged.
Deterministic Grading Rubric
You must assign severities and generate findings strictly according to this matrix based on the JSON output from check_anchors.py.
| Condition from Script Output | Finding ID | Finding Title | Severity | Priority |
|---|---|---|---|---|
No 15-120 word paragraph appears before the first <h2> |
CTX-001 | Missing Bottom-Line-First Paragraph | medium | medium |
No in-page fragment links (href="#...") or section targets (id attributes on headings/sections) detected |
CTX-002 | Missing Fragment Navigation for Deep Linking | medium | medium |
Allowed Tools
bash(executing bundled scriptscripts/check_anchors.py)
Output
Emit only a valid JSON array conforming to the references/finding_schema.json format.