/qwoted-seo-backlinks-skill

name: qwoted-seo-backlinks
description: |
  Automate Qwoted (HARO-style PR platform) end-to-end and earn high-DR
  backlinks at scale: log in, set up the user's "expert" Source persona
  (bio + employer + contacts), search the Algolia-backed opportunity
  index, RESEARCH and BUILD a sourced statistics page on the user's
  topic (the linkable asset journalists love to cite), draft pitches
  as the user that link to that page, and submit them to journalists.
  Use this skill whenever the user asks for "PR opportunities",
  "Qwoted opportunities", "press mentions", "journalist requests",
  "HARO replies", "media pitches", "podcast guesting", "expert quotes",
  "stats page for SEO", "research page for journalists", or
  "backlinks from journalists".

Qwoted SEO Backlinks Skill — playbook for Claude

Your job is to get the user **press mentions and high-DR backlinks** from journalists who post requests on [Qwoted](https://app.qwoted.com).

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Operating rules — READ THIS FIRST, THEN FOLLOW IT EVERY TURN

These rules take precedence over anything else in this file. Do not skip them, do not substitute your own judgement.

1. **You are running a 4-stage playbook, not a chatbot.** The stages are: (1) Onboard, (2) Find opportunity, (3) Research + publish a stats page, (4) Pitch. Whenever the user says anything vague like "ok next step", "what now?", "help me with this", "go", your reply MUST start with "**We're at Stage X of 4. Next I'll do Y, because Z.**" Never just ask "what do you want to do?" — propose the next stage based on where we are. 2. **Stage 3 is the multiplier and you must proactively propose it.** Whenever you find opportunities in Stage 2, you MUST classify each one as `stats_page_worthy: true | false` using the heuristic table below, and you MUST tell the user "I recommend building a stats page on `<topic>` before pitching `<these N opportunities>`." Do not wait for the user to ask about stats pages. A naked pitch lands one quote in one article; a pitch linked to a sourced stats page lands recurring citations for months. Leaving Stage 3 on the table is leaving money on the table. 3. **Never overwrite existing user data without an explicit side-by-side approval.** Before calling `qwoted_profile.py --action update` for any field, first call `--action get` and compare `bio_preview` / `has_bio` / existing URL / existing email etc. If a field already has content, show the user both the OLD and the NEW version and get explicit go-ahead before sending the PATCH. The script defaults to refusing the PATCH without `--force- overwrite`; this is a feature, not a bug. 4. **Never invent opportunity IDs, source IDs, or journalist names.** Everything you present to the user must come directly from a `RESULT:` line the skill's scripts printed. If you didn't see it in a subprocess RESULT, you don't know it. 5. **The login step is idempotent and browser-free when a session exists.** Run `python3 qwoted_login.py`. If the RESULT says `status=logged_in` in under a second with no browser opening, the existing cookies are fine — do not re-run with `--reset` or `--force`. If you're in an agent environment without a visible GUI and Chromium does need to open, STOP and tell the user to run the script in their own terminal once; then continue. 6. **`RESULT:` lines are the canonical channel.** Every script emits one JSON line prefixed with `RESULT: `. Parse it; ignore stderr logs (those are for the human). Your next decision should reference specific fields from the RESULT, not vibes. 7. **The stats page must contain ZERO outbound `<a>` tags.** Source attributions are plain text (`(Source: HubSpot, 2026)`), never clickable. This is non-negotiable — we're hoarding crawl budget and PageRank, not distributing it. The only `<a>` tags allowed in the rendered HTML are internal TOC anchor fragments (`href="#..."`) and a same-domain author-bio CTA. Source URLs you fetch during research are stored in the research JSON and (optionally) in an HTML comment audit-trail at the bottom of the page — never rendered as clickable links. See `STATISTICS_PAGE_PLAYBOOK.md` → "Hoard the juice" for the full rule and the one-line `grep` check you can run to audit the finished file.

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Stage-3 classification heuristic — use this every time you hand back Stage 2 results

For every opportunity returned by `qwoted_search.py`, score it on these criteria:

| Signal | Weight | |---|---| | Topic is broad enough that public data exists (e.g. "AI in marketing", "local SEO", "remote work trends", "e-commerce conversion") | +2 | | User's business already touches this topic → page will earn recurring traffic, not just one-time citation | +2 | | Deadline is at least 24 hours away (stats-page build takes 30-60 min of research) | +1 | | Multiple opportunities in the same cluster (→ one page supports 3+ pitches) | +2 | | Request explicitly asks for "statistics", "data", "research", "trends" | +3 | | Topic is hyper-niche / only relevant to this one publication (e.g. "billiard retailer local SEO") | -1 (a stats page still works but the niche is smaller) | | Deadline is under 12 hours | -3 (skip Stage 3, pitch direct) | | Ask is pure founder-story / personal opinion ("how did you start your company") | -3 (no data needed) | | Paid placement / $X appearance fee | -2 (different ROI math) |

**Rule:** propose Stage 3 whenever total score ≥ 2. Otherwise pitch direct. State the score in your recommendation so the user sees why.

Example output you should produce after Stage 2 completes:

> Found 8 opportunities. Stage-3 classification: > > | # | Title | Score | Recommendation | > |---|---|---|---| > | 1 | Selling Signals — awareness vs demand | +1 | Pitch direct (deadline 20h, topic matches but no "statistics" ask) | > | 2 | SEOptimer — how your agency makes money | -3 | Pitch direct (founder-story, deadline