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End-to-end pipeline: trending topic → keyword research → brief → UGC enrichment → human-style draft → visual & link enrichment → SEO + GEO optimization → scored QA report.

Pipeline

[0]   X TREND MONITOR   → auto-run on session start, propose hot keywords
[1]   KEYWORD RESEARCH  → scored, data-backed selection
[2]   CONTENT BRIEF     → outline + keyword map + UGC insertion points
[3]   UGC ENRICHMENT    → mine comments → ready-to-paste blog sections
[4]   DRAFT             → human-style, data-backed, zero AI patterns
[4.5] VISUAL & LINKS    → data charts (matplotlib) + inline hyperlinks on every citation
[5]   SEO + GEO         → on-page checklist + AI-bot-friendly formatting
[6]   QA REPORT         → rubric score + publish verdict

Enter at any stage. Run all stages from scratch. Start at Stage 5 for existing drafts.

Quick commands:

• run full pipeline → Stage 0 → Stage 6
• start from stage [N] → enter at any stage
• score my draft → Stage 5 → Stage 6
• enrich my draft → Stage 4.5 only (charts + links on existing draft)

Reference files (read when detail is needed):

• references/scoring-rubrics.md — all scoring tables (trend, keyword, UGC, SEO, GEO)
• references/ai-patterns-blacklist.md — full list of AI phrases to never write
• references/output-templates.md — exact output format for each stage
• references/example-run.md — real example: keyword "what is liquid staking"

Inputs

Required: project_spec (.md) + keyword_or_topic Optional: ugc_urls (skip Stage 3 if absent), blog_draft, language (default: English), word_count (default: 1500–2000), tone (default: auto-detect from spec)

If project_spec is missing: stop and ask for it before starting.

Stage 0 — X Trend Monitor

Run automatically at the start of every new session, before the user says anything.

1. Parse project_spec.md → extract: domain/niche, audience, product themes, competitor names
2. Run web searches (fill in values from spec):
   • site:x.com [domain keyword] -filter:replies
   • [domain keyword] trending twitter 2025
   • [domain keyword] discussion OR debate twitter
   • [competitor name] twitter sentiment 2025
   • crypto twitter trending today
   • [domain keyword] CT crypto twitter discussion
3. Score each topic → see references/scoring-rubrics.md Stage 0
4. Keep top 3. Output Trend Alert before any user message → see references/output-templates.md Stage 0

Fallback if web search off: skip, say "Stage 0 unavailable. Provide a keyword to start Stage 1." Fallback if no trends found: list 3 evergreen topics from spec context.

Stage 1 — Keyword Research

1. Generate 8–12 seed keywords: head terms, mid-tail, long-tail question variants
2. For each seed, run:
   • google trends [keyword] 2025 → direction
   • site:x.com [keyword] + [keyword] trending twitter 2025 → buzz + debate angle
   • [keyword] search volume 2025 + ubersuggest [keyword] → volume estimate
   • Search [keyword] → extract People Also Ask + Related Searches
3. Score using rubric → see references/scoring-rubrics.md Stage 1
4. Select top 1 = primary, next 5–8 = secondary/LSI. Label volume as [estimated].
5. Ask once: "Do you have an Ahrefs or SEMrush API key?" Use if provided.
6. Output → see references/output-templates.md Stage 1

Edge cases: keyword too broad → suggest 3–5 long-tail alternatives. Doesn't match spec → warn + confirm.

Stage 2 — Content Brief & Outline

1. Pick flow pattern:
   • Problem → Solution | What → Why → How | Comparison | Journey | Argument
2. Map every long-tail keyword to H2, H3, or FAQ entry. No section without a keyword anchor.
3. Flag UGC insertion points: ← UGC: community counterpoints / ← UGC: case studies / ← UGC: FAQ
4. Output brief + outline → see references/output-templates.md Stage 2
5. Ask: "Does this outline look good?" Wait for confirmation before Stage 3.

FAQ section: minimum 5 entries. Mandatory for GEO.

Stage 3 — UGC Enrichment

Skip if no ugc_urls. Note "UGC: Missing" in QA report.

Why it matters: Reddit ~21% of Google AI Overview citations. Real UGC = 2–3x AI search visibility. E-E-A-T "Experience" signal = cannot be faked by AI content.

For each URL:

1. web_fetch → extract post body + all comments with engagement metrics
   • Fallback if login wall: "Paste comment text directly"
2. Quality-score each comment → see references/scoring-rubrics.md Stage 3
3. Classify into 3 categories:
   • Category A — Counter-Arguments: challenges original post, edge cases, "Yeah but..." → 150–300 word prose paragraph
   • Category B — Real-World FAQs: direct questions from comments, especially upvoted or repeated → 3–6 Q&A pairs
   • Category C — Personal Experiences: first-person accounts with specific numbers/outcomes → 2–4 mini case studies, 50–100 words each, paraphrased but never fabricated
4. Viral pattern analysis for comments scoring ≥7: identify top 3 trigger types, output 2 writing tips
5. Map each UGC block to its insertion point from Stage 2 outline
6. Output → see references/output-templates.md Stage 3

Hard rules: never fabricate, never include usernames, never include spam/memes, max 6 FAQs + 4 case studies.

Edge cases: <10 comments → warn "limited data". All score <3 → "No high-value comments, try different URL."

Stage 4 — Draft Writing

Write full article: follow Stage 2 outline exactly, insert Stage 3 UGC blocks at flagged points.

Before writing: write Meta Title (≤60 chars, primary keyword near front) + Meta Description (≤160 chars, primary keyword + hook)

Intro: specific fact or bold claim opener. Primary keyword within first 100 words. Write featured snippet candidate block (40–60 words, direct definition or numbered steps) — place within first H2.

Body: answer-first each H2 (1–2 sentence direct answer before elaboration). Min 1 data point per major section. Find real stats first: search [topic] statistics 2025. If unavailable: [DATA NEEDED: search "[query]"]. Source format: stat — Source, Year. UGC blocks at exact insertion points.

Data minimum: 3 data points total per article. Never invent statistics.

Paragraphs: 3–4 lines max. Define technical terms inline on first use.

AI patterns: never write any phrase from references/ai-patterns-blacklist.md. If caught: stop, rewrite with fact or direct claim.

Conclusion: synthesize 2–3 takeaways. Do not restate all H2s as bullet points. Specific CTA.

Internal links: suggest 2–3, anchor text uses secondary keywords.

Output format → see references/output-templates.md Stage 4

Stage 4.5 — Visual & Link Enrichment

Runs automatically after Stage 4 is complete. Cannot be skipped — required for publish-ready output.

Why This Stage Exists

• Data charts are 3× more likely to be cited by AI search engines than the same data in plain text
• Inline hyperlinks on every source citation signal editorial credibility to Google and AI crawlers
• Every unlinked — Source, Year is a missed E-E-A-T trust signal

Step 1 — Identify Chart Opportunities

Scan the draft for data points that meet at least one of these criteria:

Chart types by data shape:

Chart design standards (apply to every chart):

• Background: #F7F7F7 (light gray), no heavy gridlines
• Primary color: #E8650A (orange, default for crypto/Web3 — adjust to project brand if spec defines one)
• Remove top + right spine
• Annotate key data points directly on chart (peak, trough, threshold)
• Source line at bottom-left: gray italic — Source: [Name] | [Notes]
• Font: DejaVu Sans (matplotlib default)
• Resolution: 150 DPI minimum
• Always generate alt text for every chart

Maximum 6 charts per article. More than 6 slows page load. Excess data points → stat callout boxes instead.

If no qualifying data points found: output a styled stat callout box for that statistic instead of a chart.