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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: \x3C10 comments → warn "limited data". All score \x3C3 → "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.

Step 2 — Generate Charts

Use Python + matplotlib. Execute in the computing environment.

For each chart, produce:

1. PNG image embedded in the output
2. Caption (1–2 sentences): Chart [N]: [What it shows]. [Time period]. Sources: [Name].
3. Alt text string for CMS upload: [Chart type] showing [metric] from [start] to [end]. [Key finding in one sentence].

Place each chart immediately after the paragraph that first introduces its data. Never stack two charts back-to-back without body text between them.

Step 3 — Convert All Source Citations to Hyperlinks

Scan the entire draft for every inline source citation. Patterns to find:

For each citation:

1. Run web search: [publication] [topic] [approximate date]
2. Verify URL resolves
3. Replace plain-text source with inline hyperlink — anchor text = source name
4. If exact article not found: link to publication homepage + flag [VERIFY URL]
5. If citation is vague ("analysts say"): flag [SOURCE NEEDED] — never invent a source

Link rules:

• Prefer original publisher over aggregators
• No paywalled links if a free version exists
• All external links: open in new tab in HTML output
• Do not add links to unattributed claims

Step 4 — Output Summary

Append this block to the enriched draft:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
STAGE 4.5 — VISUAL & LINK ENRICHMENT SUMMARY
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Charts generated:      [N] / [max 6]
  - Chart 1: [title] → placed after [section name]
  - Chart 2: [title] → placed after [section name]

Source links resolved: [N] / [total citations found]
  - [Source name] → [URL] ✅
  - [Source name] → [URL] ⚠️ [VERIFY URL — linked to homepage]

Unlinked citations flagged: [N]
  - "[claim]" → [SOURCE NEEDED]

Alt text strings:
  Chart 1: "[alt text]"
  Chart 2: "[alt text]"
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Stage 5 — SEO + GEO Optimization

SEO Checklist

• Meta title: 50–60 chars, primary keyword near front
• Meta description: 150–160 chars, primary keyword, hook
• H1 contains primary keyword (only one H1)
• H2s use secondary keywords or question variants
• Primary keyword in first 100 words and in conclusion
• Keyword density: 1–1.5%
• Paragraphs ≤4 lines
• 2+ internal link suggestions (anchor text = secondary keywords)
• 1–2 external authoritative sources noted
• Image alt text recommendations (min 2)
• FAQ present → FAQPage schema | How-to steps → HowTo schema

GEO Rules (apply all 7)

GEO = Generative Engine Optimization. Goal: easy for Perplexity, Google SGE, ChatGPT Search to parse and cite.

1. Answer-first every H2 — 1–2 sentence direct answer before elaboration
2. Explicit entity labeling — full context on first mention, units on all numbers ("3.2% APY"), explicit dates ("as of Q1 2025")
3. FAQ mandatory (min 5) — each answer self-contained, 50–150 words, direct answer first
4. Featured snippet block — 40–60 words, placed within first H2, paragraph OR numbered list (not mixed)
5. Citation-friendly — each factual claim on its own sentence, data formatted number unit — Source, Year, no pronoun ambiguity
6. Secondary keywords in internal link anchor text
7. UGC sections present — confirms E-E-A-T "Experience" signal

GEO output block → see references/output-templates.md Stage 5 Scoring rubric → see references/scoring-rubrics.md Stage 5

Stage 6 — QA Scoring Report

Score using 100-point rubric → see references/scoring-rubrics.md Stage 6

Thresholds: 85–100 = Publish-ready | 70–84 = Minor fixes | Below 70 = Needs revision

Output format → see references/output-templates.md Stage 6

Edge Cases

Limitations

• Stage 0: simulated X monitoring via web search, not live X API
• Keyword volume: estimated unless user provides API key
• No CMS auto-publishing, no image generation (editorial)
• Stage 4.5 generates data charts from in-draft data via matplotlib — does NOT generate decorative or editorial images
• [VERIFY], [DATA NEEDED], [SOURCE NEEDED], and [VERIFY URL] flags require human review before publishing
• SEO/GEO scores: internal rubric, not third-party tool scores
• UGC mining: public pages only