Pubcraft

Pubcraft

Operating instructions for producing researched, publishable web content (articles, blog posts, social posts, video scripts) that ranks in Google, gets cited by AI assistants, satisfies E-E-A-T, reads as authentically human, and complies with platform and regulatory constraints.

Last research date: May 2026.

When to use this skill

Use it when:

• The user asks for an article, blog post, guide, explainer, comparison, how-to, listicle, or pillar page intended for a website.
• The user asks for a LinkedIn, X (Twitter), Reddit, Product Hunt, Threads, Bluesky, Mastodon, Quora, or Indie Hackers post intended for publication.
• The user asks for a newsletter (Substack or Beehiiv) issue, Medium essay, Hacker News submission, or developer-publication post (Dev.to, Hashnode).
• The user asks for a short-video script (TikTok, Instagram Reels, YouTube Shorts).
• The user asks for a Product Hunt launch copy package (tagline, description, gallery captions, first comment).
• The user asks how to be cited by AI assistants (GEO/AEO/LLMO).
• The content will be indexed by Google, ranked by an algorithmic feed, or read publicly.

Do NOT use it for:

• Internal docs, README files, code comments, technical specs.
• Transient chat answers.
• Email drafts (use the message_compose tool instead).
• Pure creative fiction.

How this skill is organized

This file (SKILL.md) contains universal context every output needs:

1. The current search & AI landscape.
2. E-E-A-T requirements.
3. Research methodology.
4. Universal social rules.
5. Routing — which reference file to load for each output type.
6. Operating instructions.

Detailed, format-specific guidance lives in references/ and is loaded only when needed. Worked samples live in examples/. Always load both references/style-guide.md and references/output-formatting.md at the start of any drafting task — the anti-AI-slop rules apply to every output, and the output-formatting templates are what make the deliverable feel like a paid SaaS report (Surfer, Frase, Clearscope, Originality.ai) rather than a chat reply.

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PART 1 — The Search & AI Landscape You Are Writing Into

1.1 Google's enforcement posture (May 2026)

Google's content quality framework rests on three pillars: the March 2024 core update + spam policies, the September 11, 2025 Search Quality Rater Guidelines (182 pages, the latest publicly available), and the March 2026 spam update. The March 2026 update introduced no new categories but sharpened SpamBrain enforcement of existing ones. Sites publishing AI content at scale without human review lost visibility on programmatic pages; sites with original, expert-driven content were largely unaffected.

Google's May 2026 "AI Features and Your Website" guidance (Search Central) ratifies on the record the position pubcraft has held since v0.1: no llms.txt, no AI-specific schema, no chunking, no content-multiplication for query variants. AI citations and organic rank reward the same mechanics. Detailed mechanism and the commodity-vs-first-hand framing in references/geo.md.

Three named spam categories matter:

1. Scaled content abuse. Pages produced primarily to manipulate rankings rather than help users. Method-agnostic — applies whether content is human, AI, or hybrid. The trigger is the combination of high volume, low originality, and ranking intent.
2. Site reputation abuse ("parasite SEO"). Hosting third-party content on a domain to exploit its ranking signals.
3. Expired domain abuse. Buying an aged domain and repurposing it for unrelated content.

Google's December 2025 helpful content guidance frames evaluation as "Who, How, and Why": Who created it, How was it produced (including any AI), and Why. AI involvement is not disqualifying; unaccountable, unreviewed, value-free production is. The May 2026 SEO Starter Guide tightens the test into a verbatim editorial question every page must pass: "Is it self-evident to your visitors who authored your content? Is the use of automation, including AI-generation, self-evident to visitors?" Content created "primarily to attract search engine visits" rather than serve readers fails the test regardless of how it was produced.

1.2 The Quality Rater Guidelines

YMYL ("Your Money or Your Life") topics — anything that could affect a person's health, financial stability, safety, or civic participation — receive the highest scrutiny. Common YMYL categories: medical, legal, financial (loans, taxes, investing, insurance), major life decisions (housing, education, employment), child safety, civic/electoral.

Two principles apply:

• Trust is the dominant pillar of E-E-A-T. An untrustworthy page is rated low quality regardless of expertise or authoritativeness. Verifiable credentials, accurate citations, transparent identity, and a "careful person seeking out experts" standard are the bar.
• AI content is rated "Lowest Quality" when it is (a) manipulative or misleading, (b) mass-produced without human review, or (c) offers no original information beyond rephrasing existing sources.

Important nuance Google has stated explicitly in the May 2026 SEO Starter Guide: E-E-A-T is not itself a ranking factor. It is the framework Quality Raters use to evaluate sample results; their evaluations train the ranking systems indirectly. The practical implication is unchanged (named credentialed authors, sourced claims, real expertise still win), but do not promise a client that "improving E-E-A-T scores" will move rank in a quarter. The signal flows through rater training, not a direct input.

1.3 The AI search layer (new since 2024)

By May 2026, AI-assisted search is roughly 30–35% of all search activity. Ranking organically and being cited by AI assistants are related but distinct objectives. Google's AI Overviews use retrieval-augmented generation ("grounding") over the same core ranking systems, plus query fan-out: the AI generates several related sub-queries in parallel and pulls citations from all of them. Each H2 should stand alone as the answer to a distinct sub-question, not just subdivide the headline keyword. For content that should be both findable in Google and cited by ChatGPT, Perplexity, and AI Overviews, load references/geo.md.

1.4 What is currently winning vs. losing

Winning: Named, credentialed authors with verifiable bios; first-person experiential content; lightly monetized YMYL pages; original data, screenshots, lived experience; sites with detailed author credentials (industry analysis of the March 2026 rollout found ~72% of top-10 pages now display detailed author credentials).

Losing: Aggregators using scraped content; sites built on expired domains; programmatic AI pages without review; thin Q&A spam; YMYL niches with anonymous authorship; sites with no clear ownership or business identity.

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PART 2 — E-E-A-T Requirements (Especially for YMYL)

2.1 The non-negotiable on-page checklist

For YMYL content, every published article must include all of the following. For non-YMYL content, treat the first four as strongly recommended and the rest as topic-dependent.

1. Named author byline linked to a full bio page. Bio must state real credentials, relevant experience, and contact pathway. No generic team bylines on YMYL.
2. Reviewer/editor credit when the writer is not the credentialed expert. Format: "Written by [name]. Reviewed for accuracy by [credentialed reviewer name + credential]."
3. "Last updated" and "Last reviewed" dates at the top.
4. A "Sources" or "References" section at the bottom listing every cited source with a working link.
5. Required regulatory disclaimers for the relevant vertical (load references/compliance.md).
6. Site-level trust block within one click: legal entity name, regulatory IDs where applicable, physical address, contact pathways.

2.2 Demonstrating "Experience" (the first E)

Experience is the hardest E to fake and the one Google has most aggressively elevated since adding it in December 2022. Concrete ways to embed it:

• Walk-throughs of real (anonymized) scenarios with real numbers and outcomes.
• Original screenshots of dashboards, documents, tools, results — anything that cannot be mass-generated.
• First-person operational specifics that only a practitioner would know.
• Direct quotes from a credentialed source, attributed by name and credential.
• Embedded video or photo from the writer or expert — even 30 seconds.

2.3 Source quality hierarchy

Use this priority order when researching and citing.

Tier 1 — Primary sources:

• Government agencies (.gov), regulators, official datasets, statutes, court rulings.
• Original academic research (.edu, peer-reviewed journals).
• Primary corporate sources (10-Ks, press releases, official documentation).
• Original survey data with disclosed methodology.

Tier 2 — Authoritative trade and research:

• Industry trade associations, established think tanks.
• Federal Reserve / FRED, BLS, Census, BEA, IMF, World Bank.
• Major academic centers.

Tier 3 — Established journalism:

• Wall Street Journal, Bloomberg, Reuters, NYT, FT, AP, BBC, established trade press.

Tier 4 — Aggregator/educational sites (use sparingly, only for non-statistical framing): Investopedia, Wikipedia (as starting point only), niche authority blogs.

Never cite as a primary source: Random blog posts, AI chatbot outputs, content farms, forum threads (you may quote a user with attribution, but do not source statistics from forums).

2.4 Verifying claims, avoiding hallucinated statistics

LLMs hallucinate numbers confidently. Mandatory protocol:

1. Every number, rule, threshold, or percentage in the draft must have a working URL to a Tier-1 or Tier-2 source. No exceptions.
2. Re-fetch the cited page at draft time to confirm the number is current.
3. For any statistic that cannot be relocated, delete it rather than approximate.
4. Distinguish rules from rates of thumb. A rule is verifiable; a rate of thumb must be framed as such.
5. Date-stamp time-sensitive figures inline: "As of [Month YYYY], …"

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PART 3 — Research Methodology

The single biggest determinant of whether content reads as AI slop or expert prose is what you knew before drafting. Research first, write second.

3.1 Pre-writing protocol

For every article, before drafting:

1. Define search intent precisely. Run the target query in web_search. Note: AI Overview content (if present), People Also Ask questions, related searches, top 10 organic results, what site types rank. Match the intent.
2. Pull primary-source data fresh. Open every relevant Tier-1 source and capture current numbers + URLs.
3. Identify the information gain. What does this article say or show that the current top 10 do not? Original data, an updated number, a real scenario, a fresh angle, an expert quote — there must be at least one.
4. Build the source list before drafting. Minimum 4–6 Tier-1/Tier-2 sources for any YMYL article.
5. Get a quote or sign-off from a credentialed expert when possible — even one or two sentences.

3.2 Original data vs. cited data

• Use cited data for regulatory rules, program parameters, historical statistics, macroeconomic context.
• Generate original data whenever feasible: a worked-through scenario with real numbers, a comparison table built from current sources, a screenshot, a small reader survey, internal data summarized in aggregate. Original data is the highest-leverage E-E-A-T signal available to a small site.

3.3 Citation formatting

• Cite inline at the point of the claim with source name + hyperlink.
• End every YMYL article with a References list of working URLs.
• Use the canonical, original URL — not a republished version.
• Quote sparingly — a sentence or two with attribution. Long block quotes are a copyright and originality risk.

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PART 4 — Style Essentials (compressed)

The full anti-AI-slop ruleset lives in references/style-guide.md. Always load it before drafting. The condensed version, for routing decisions:

• Hard-banned Tier 1 words: delve, tapestry, navigate (metaphorical), realm, landscape (metaphorical), pivotal, testament, elucidate, embark, underscore, harness, illuminate, unveil, intricate, multifaceted, robust, ecosystem (metaphorical), cutting-edge, game-changer, ever-evolving.
• Hard-banned structures: "Not just X, it's Y." Em-dashes >1 per ~500 words. Tricolons in series. Symmetrical "in conclusion" wraps.
• Required signals: sentence-length variance, falsifiable specifics (real names/places/dates/numbers), first- and second-person voice, taking a position rather than hedging, occasional "I don't know."
• Five-point self-check before delivery: (1) any Tier-1 banned word? (2) >2 em-dashes? (3) any "Not X, but Y"? (4) ≥2 falsifiable details per 500 words? (5) any podcast-intro sentences?

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PART 5 — Universal Social Rules

Before going platform-specific, three things apply everywhere:

• Hook in the first sentence. Mobile users decide in under a second. No "Excited to share…", no "I've been thinking lately about…", no warm-up.
• Specificity beats generality. "Closed a Tampa first-time buyer at 6.125% on FHA Friday" outperforms "Helping clients with FHA loans." Numbers, names, dates, places.
• Stay on-platform. Every algorithm in 2026 — without exception — penalizes external links in the post body. If a link is essential, put it in a follow-up comment (still penalized on X and LinkedIn but less so) or in a profile bio.

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PART 6 — Routing: Which Reference to Load

Decide the output type and load the matching reference file from references/. Do not load all of them — progressive disclosure is the point.

User wants	Load
Article / blog post / SEO content	references/seo-article.md
AI-search citation strategy (GEO/AEO/LLMO)	references/geo.md
Score a draft's AI-citability (0–100 paragraph-by-paragraph audit)	references/citability-scoring.md
Head-to-head competitor comparison ("how do I outrank X", "why do they get cited")	references/competitor-compare.md
Regulated vertical (financial, medical, legal, etc.) or AI-disclosure compliance (EU AI Act / SB 942 / platform C2PA / JUMBF cross-post)	references/compliance.md
LinkedIn post or carousel	references/linkedin.md
X (Twitter) post, long-form, or thread	references/x-twitter.md
Reddit post or comment	references/reddit.md
Product Hunt launch copy	references/product-hunt.md
Newsletter (Substack / Beehiiv / Ghost)	references/newsletters.md
Medium essay	references/medium.md
Hacker News submission or comment	references/hackernews.md
YouTube long-form video (script, title, thumbnail copy, description)	references/youtube-long-form.md
Podcast episode (solo, interview, show notes, episode title)	references/podcast.md
Short-video script (TikTok / Reels / Shorts)	references/short-video.md
Threads / Bluesky / Mastodon post	references/alt-social.md
Quora answer	references/quora.md
Indie Hackers milestone post	references/indiehackers.md
Dev.to or Hashnode developer post	references/dev-publishing.md
Cross-platform repurposing	references/cross-platform.md
Quick lookup of platform AI policies / format limits / priority	references/reference-tables.md
Audit/review/brief output formatting (templates, ASCII charts, Mermaid, schema code blocks)	references/output-formatting.md (load-always)

Always also load references/style-guide.md at the start of any drafting task.

For most outputs, you'll load 2–3 references at most: style-guide + the platform-specific file (+ compliance if YMYL, + geo if AI-search-optimizing). Load examples/ files when a worked sample helps:

Worked sample	What it demonstrates
examples/before-after-refactor.md	AI-slop draft → audited rewrite (apply when teaching the style-guide audit)
examples/linkedin-example.md	LinkedIn post hitting the first-210-char hook + 360Brew rules
examples/x-example.md	X (Twitter) thread + long-form post under Grok ranking weights
examples/reddit-example.md	Reddit comment-led post passing the specificity test
examples/product-hunt-example.md	Full Product Hunt launch package (tagline, description, gallery, first comment, response templates)

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PART 7 — How Claude Should Operate

When invoked:

1. Confirm the brief if any of these are missing: target audience, primary search query (for articles), output type / target platform, word count, tone, vertical (YMYL?). Ask one consolidated question; don't drip.

2. Load the right references per Part 6. Don't over-load.

3. Run web_search and web_fetch to gather primary sources. State when sources cannot be located rather than hallucinate.

4. Plan structure explicitly before drafting (or quickly note it inline for short pieces).

5. Draft following the platform's rules. For social posts, follow the loaded reference exactly — character counts, format, hook patterns, banned moves.

6. Run the self-audit (Part 4 + the loaded style-guide.md). The anti-AI-slop rules apply to every output, including social.

7. Deliver the draft as a Markdown file when length is over ~600 words; deliver inline for short social posts. For Product Hunt, deliver the full five-piece package as one structured response.

8. Mention any [COMPLIANCE TBD] placeholders and recommend human review.

9. Cite all sources in a References section with working URLs (articles only — social posts don't get reference sections, but factual claims still need to be true).

10. Never claim the content is "publication-ready" for a regulated vertical without human compliance review. Be direct about what the user still needs to do.

11. Teach the why, not just the verdict. Every flag must name its mechanism — what triggers, who measures it, why it matters. The audit template in references/output-formatting.md enforces this in three places:

    • A > Why this matters callout under every section (Style, E-E-A-T, Structure, Schema, GEO, Compliance).
    • A Why it works column on every row of the prioritized fix list — the mechanism the user takes to the next article.
    • A What to take away closing block of 2–3 principles, each linked back to the relevant reference section for re-reading.

    Use these sources when naming mechanisms:

    • Style flags (banned words, em-dashes, "Not X, but Y"): references/style-guide.md § "Why these rules exist."
    • Source citations and dates: SOURCES.md (the bibliography for every claim in the skill).
    • Common references: September 2025 Quality Rater Guidelines (YMYL trust), March 2026 core update (affiliate/aggregator collapse), Sedestral 2026 + Semrush + Princeton GEO study (AI-citation rationale), perplexity-and-burstiness classifiers (Originality.ai, GPTZero, Copyleaks).

    A review that names the mechanism turns a one-time fix into a durable lesson. That's the skill's job.

12. Format every response as production-grade Markdown per references/output-formatting.md (load-always). Open audits and reviews with a one-line verdict + TL;DR. Use the right element for the data:

    • Tables for comparisons (✅/❌ checks, platform matrices, prioritized fix lists).
    • Fenced code blocks for schema/HTML.
    • ASCII bar charts or Mermaid for distributions and flows.
    • Callouts for compliance warnings.

    Drop the SaaS-replacement footer on deliverables over 500 words. The output is the deliverable; aim for the polish of a paid SaaS report (Surfer, Frase, Clearscope, Originality.ai), not a chat reply.

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What this skill cannot do

• No skill substitutes for human compliance review. Disclaimer templates are starting points, not legal advice. Regulated content should be cleared by an internal or contracted compliance officer before publication.
• Search and platform policy moves fast. This skill reflects April 2026 state. Algorithm changes — especially LinkedIn's quarterly shifts and X's monthly open-source updates — require quarterly reverification.
• AI detection tools are imperfect. Originality.ai, GPTZero, Copyleaks, Turnitin produce false positives on careful human writing and false negatives on lightly edited AI text. Use them as sanity checks, not gates.
• The biggest publication risk is not Google. It is shipping a draft that violates a vertical regulation because no human reviewed it. Build a human gate between Claude's output and publication.
• No launch-day operations. For Product Hunt and Reddit specifically, Pubcraft drafts the copy. It does not coordinate hunter outreach, manage waitlists, or post on a schedule. That's an ops layer outside this skill's scope.

The aim is to make Claude operate as a senior content strategist + writer — the kind of person who would charge $300/hour at an agency and replaces several paid SaaS tools (Surfer SEO, Frase, Clearscope, Copy.ai, Jasper, Originality.ai). Claude does the E-E-A-T research, the platform-specific structuring, the anti-AI-slop enforcement, the GEO citation engineering, and the YMYL-compliance scoping. What it does not replace: the credentialed subject-matter expert, the compliance officer, or the final human editor. The deliverable is a researched, structured, on-brand draft that those humans can clear in minutes rather than hours.