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Hoo import audit

Skill shalintripathi/organic-os/plugin/skills/hoo-import-audit

Run an always-on SEO/AEO growth loop on your own site with Claude: pulls GA4 and Search Console signals, proposes fixes with the reasoning attached, applies approved changes to WordPress, verifies they landed, and learns from outcomes. Every write is human-approved. MIT, no telemetry, no scraping.

Install
npx -y skills add shalintripathi/organic-os --skill hoo-import-audit

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What its author says it does

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Use to import an external claude-seo audit report or action plan as gated proposals - "import this audit", "turn this audit report into proposals", /organic-os:import-audit. They audit, we operate.

SKILL.md

3.1 KB, as published. Nobody here has run it

Import an external audit (claude-seo shape)

Turns a point-in-time audit report into organic-os proposals without re-deriving the audit. The import trusts but attributes: findings keep their original evidence labels ([Measured]/[Inference]/[Unverified]), the original finding text rides in each proposal verbatim, and anything our own audit dimensions would dispute is flagged for the human, never silently rewritten.

  1. Input: a file path from the user (a FULL-AUDIT-REPORT.md, an ACTION-PLAN.md, or similar markdown) or report text pasted in chat. Pasted text: write it to a temp file first. Read site-profile.yaml for the approval channel and the site URL.
  2. Parse and rank with the import module - stdlib only, defensive by design (the format is theirs and may vary between versions; whatever matches no item pattern is preserved in parsed["unparsed"], never dropped): PYTHONPATH="$CLAUDE_PLUGIN_ROOT/lib" python3 -c snippet importing hoo.audit_import - parsed = parse_report(text), then props = to_proposals(parsed, max_items=5).
  3. Create each proposal via create_item(kind=p["kind"], slug=p["slug"], title=p["title"], body=p["body"], target=<the finding's page URL if one appears in it, else parsed["source_meta"]["page_url"], else the profile site URL>, source="import-audit"). create_item is the ONLY birth path - items are born proposed with an empty approvals list and are NEVER auto-approved; approval is the human's act, on the normal gate. Each body already carries the verbatim finding (quoted), its evidence label, the attribution line, and the dimension-mapping note: overlap names the onsite-audit dimension (skills/onsite-audit steps 3-4) via the DIMENSION_HINTS map in plugin/lib/hoo/audit_import.py; an llms.txt finding gets the deliberate-skip note (our position in plugin/docs/evidence.md); no overlap is marked external-only and imported as-is on the source audit's evidence.
  4. Present a summary table in-session:
    • imported items: title, source severity, dimension mapping (dimension name | deliberate-skip | external-only)
    • skipped items: title + reason (ranked below the max_items cut; re-run with a higher max_items to import them)
    • unparsed excerpt count, with a pointer that the excerpts were preserved, not dropped
  5. Rebuild the queue and route the approval notification through the configured channel exactly like any propose run (skills/onsite-propose step 4, including the is_notified/mark_notified discipline so re-runs never re-notify).
  6. Append ONE signal recording the import: source audit date (parsed["source_meta"]["audit_date"]), items parsed, proposals created, skipped count, unparsed excerpt count.

Pattern credit: claude-seo (https://github.com/AgriciDaniel/claude-seo), credited in README.md since v0.1.0. They audit, we operate.

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