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/audit-orchestrator

@170b582
by Tejas Shuklatejasshukla2007/brand-ai-readiness-audit1 stars

Audit a website for AI-discoverability and on-site-engagement problems — crawlability, JS-render gaps, missing/invalid structured data, facts locked in non-text, stale or uncorroborated facts, entity ambiguity, weak on-site orientation / no context retention — and produce a report of findings plus prioritized, actionable fixes. Use when diagnosing why a brand is missing or misrepresented in AI assistants, or why visitors who arrive don't engage.

  • 3 files
  • 5.9 KB
  • Updated 3 weeks ago
  • GitHub

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Use this Skill: https://skilld.dev/gh/tejasshukla2007/brand-ai-readiness-audit/audit-orchestrator

This session only. Nothing lands on disk.

SKILL.md

≈118 tokens always: the name and description. ≈439 when used: this file. ≈669 more on demand in 1 file.

Audit Orchestrator

When to use

  • Use this skill when you need a complete audit of a brand's visibility in AI assistants.
  • Invoke this to get a consolidated report covering both AI discoverability (off-site citations) and on-site engagement.
  • This skill orchestrates the entire audit workflow. It performs core checks directly and can also invoke subordinate skills if they exist in the marketplace.

Inputs

  • url: The target website URL or domain string to evaluate.

Procedure

  1. Parse the input url to ensure it is a valid, reachable target for the audit.
<!--Update the sub-skill list below as new skills are added to the marketplace.-->
  1. Invoke each available sub-skill on the url and collect their findings arrays. Registered sub-skills: freshness-corroboration, semantic-structure-audit, a11y-extractor-audit, context-retention-audit, content-quality-audit.
  2. Aggregate all findings from the executed sub-skills into a single, unified list. Deduplicate any overlapping issues.
  3. Rank the aggregated findings by severity (critical, high, medium, low). Ensure every finding explicitly states the evidence and includes a proactive, mechanism-sound suggested_action.
  4. Execute python scripts/compile_report.py <url> to generate the base JSON structure and automatically calculate the summary metrics (total_findings, critical, high, medium, low) based on the aggregated list.
  5. Inject the ranked findings array into the base JSON structure.

Output

Emit a single JSON report object strictly conforming to the schema defined in references/output_schema.json

The final output must be recommend-only. Do not attempt to make any changes to the site.

Source: SKILL.md on GitHub

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Provenance

Signed by skilld at 170b582. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub last week.

Activeupdated 3 weeks ago

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