Jonathon's AI Wiki

MEGA PROMPT CHEST — Anonymous Marketing/SEO Prompt Compilation

11 min read

Source: ai-research/mega-prompt-chest-2026-05-02.md (docs.google.com/document/d/16Rwd1kgHikLjCvzk_awH9FAIXJMkT_UUno6tEd0Lhog, fetched 2026-05-02; ~2,000 lines, ~120 prompts; no byline in source). Plus raw/reddit-1ufcktm.md (r/ClaudeAI “lacuna prompting” technique, added 2026-06-25)

A 17-section Google Doc compilation of ~120 marketing, SEO, content, and business-strategy prompts. Released without author attribution and shared via “anyone with the link” preview URL. Mixed quality — some entries are 110-line frameworks with output schemas, others are two-line one-shots. The good ones are good; the catalog itself is more browseable than authoritative. Joins this topic’s other applied prompt libraries (Winston / Godin / LinkedIn / LinkedIn Funnel) as a broader, looser starting menu rather than a focused playbook.

Key Takeaways

  • Scope. 17 sections: Deep Target SEO & Keyword Research (16 prompts), Content Creation & Blog Writing (8), SEO Content Optimization (6), Technical SEO (7), Local SEO (4), On-Page Optimization (7), Email Marketing (7), Landing Pages & Sales Copy (4), Market Research & Strategy (~21), Competitor Analysis (7), SEO Reporting & Analytics (4), Advanced & Unconventional SEO (6), Content Analysis & Optimization (2), Case Studies (4), Business Strategy & Growth (1), Micro SaaS Builders (1), Lead Magnets (3).
  • Tool-agnostic by default. Most prompts work in any chat surface (claude.ai, ChatGPT, Gemini). Five entries are platform-specific: Competitor Content Analysis [NOTEBOOKLM] and Authority Miner assume a NotebookLM project with sources loaded; three Case Study prompts are written as customGPT instructions; MANUS CHROME EXTENSION is a one-liner Manus playbook reference; Hardback Book Mockup is an image-generation template (works with any image model).
  • Quality range is wide. “Alphabet Soup Method Keywords” is a one-line trick (“Suggest keyword ideas beginning with ‘a,’ then ‘b,’ then ‘c’…”). On the other end, The 3-Pillar Authority Accelerator is a 110-line strategist-grade prompt: deep competitor research → 3 topical-authority pillars → 12-month content roadmap with at least 10 articles per pillar, plus a final spreadsheet-ready table. Forensic Psychology Analysis is the other standout — a 4-layer framework (Surface Language → Hierarchy Mapping → Temporal Analysis → Competitive Intelligence) that outputs a Customer DNA Profile with 3 Psychological Tripwires and a “Vernacular Goldmine” of exact-phrase quotes pulled from source text.
  • Heavy SEO bias. The first ~80 prompts and the entire “Advanced & Unconventional” + “Reporting” + “Competitor Analysis” sections are SEO-driven. Marketing/content prompts are framed for organic search rather than paid acquisition (no Meta/Google Ads prompts; the Meta Ads CLI handles that surface separately).
  • “Deep Target” framing. Coined throughout the SEO section for bottom-funnel, commercial-intent keyword work. Not a published methodology — just a recurring frame across ~7 prompts that ask the model to reframe a product into specific buyer scenarios, demographics, and urgency triggers (e.g., “Emergency/Urgent Situation Keywords”).
  • Templated input slots. Every prompt uses bracketed placeholders ([YOUR PRODUCT], [KEYWORD], [INDUSTRY], [COMPETITOR LIST]) — the standard fill-in-the-blank template style. None use XML tags or structured <role>/<task>/<output> schemas like the Winston library. Conversational-prose with bullet lists is the dominant syntax.

Distinctive Prompts Worth Lifting

These are the entries that justify the doc’s existence — the others are largely interchangeable with what any LLM would generate from the section title alone.

  • The 3-Pillar Authority Accelerator (Market Research & Strategy section, ~110 lines). Most ambitious prompt in the doc. Inputs: client niche + 3+ competitor URLs. Outputs: competitive landscape summary, 3 authority pillars with justification, 12-month month-by-month roadmap, and a full content list table with columns for Pillar / Title / Primary Keyword / Intent / Angle / Internal Links / Priority Score. Worth reading even if you don’t run it — it’s a template for how to structure any “give me a roadmap” prompt with deliverable format constraints.
  • Forensic Psychology Analysis (Market Research & Strategy). 4-layer framework for analyzing Reddit posts, reviews, or job listings to extract buying psychology. Output schema is unusually detailed: Customer DNA Profile, 3 Psychological Tripwires (each with mechanism + usage), Vernacular Goldmine (exact quotes), Emotional Intensity Map, Contradiction Analysis. Pairs naturally with the LinkedIn Funnel Reddit ICP-research step.
  • SEO Topical Map & Keyword Clustering. Takes ~1,000 keywords + business context, outputs a spreadsheet-ready table with Pillar / Subtopic / Target Keyword / Search Intent / Page Type / Business Goal Alignment / Priority. Quality bar in the prompt: “Think like an SEO lead building a 12-18 month content roadmap, not a keyword dumping exercise.” Direct analog to FLUQs outputs but built for agencies running general SEO, not the FLUQs-style AEO/AI-search framing.
  • Authority Miner (Borrow Credibility). NotebookLM-specific. Loads influential people’s content as source material, then has the model build an SEO content plan that “borrows credibility by association.” Writeup includes specific instructions for NotebookLM’s source-grounded mode. Cross-applies to any RAG-style setup where you want output anchored to specific reference texts.
  • Customer Persona Developer. ~60-line prompt that produces a multi-layer persona doc: demographics, psychographics, day-in-the-life, decision criteria, objections, content preferences, channels. Useful as a starting frame even if the Onboarding course’s Smile Springs Family Dental worked example shows a more concrete way to operationalize personas.
  • Hardback Book Mockup. Image-generation template for lead-magnet covers. Spec’d for portrait-orientation hardback, with placeholders for title, subtitle, design theme, color palette, optional badges (✅ AI-Ready). Pairs with Brandon Storey’s lead magnet pipeline — that course covers production-side; this prompt covers cover-design side.
  • New Age App Lead Magnets. Generates 15 micro-tool ideas (calculators, quizzes, checklists, generators) sized to deliver value in <5 min, naturally feeding a paid offer. Output is a 5-column table. Sits upstream of the AI Marketing lead-magnet flow as the brainstorm step.

Caveats

  • Anonymous source. No byline, no provenance for the methodology behind the “Deep Target” framing or the Forensic Psychology layers. Treat the frameworks as reasonable starting structures, not researched playbooks. Confidence: medium until the patterns are stress-tested against real agency work.
  • Mixed maturity. Section sizes are uneven (Market Research has ~21 prompts; Business Strategy has 1). Some prompts duplicate each other across sections (multiple “competitor gap analysis” variants).
  • No measurement. None of the prompts include “verify by…” or “check against…” steps. Pair with the troubleshooting reference — especially the hallucination + drift sections — when running the longer frameworks against client data.
  • Bracket-template style is dated. Modern Anthropic prompting practice (see Anthropic’s Prompting Best Practices and the OpenAI GPT-5 guide cross-vendor lessons) leans on XML structure tags, explicit output schemas, and self-rubric prompting. The MEGA PROMPT CHEST entries can be upgraded by re-wrapping high-value ones in <role>/<task>/<rules>/<output> skeletons before saving as Projects/Skills.

Try It

If you only run two prompts from this catalog, run these:

  1. The 3-Pillar Authority Accelerator against a real WEO client. Paste the client’s niche + 3 competitor URLs. Expect the 12-month roadmap output to need editing — the value is the structured analysis, not the article-title generation. Measure against Blog-Agent-Worker’s actual content output to see what gets through to ranking.
  2. Forensic Psychology Analysis on a 30-comment Reddit thread or 50 G2 reviews of a competitor. Output gives you copy-ready phrases and a tripwire list — feed those directly into the Reddit ICP step of the LinkedIn Funnel or into lead-magnet hooks.

For team adoption, consider promoting the 5-6 distinctive prompts above into named Claude skills (or claude.ai Projects) so they live as reusable artifacts with the right model + system prompt baked in, rather than copy-paste-from-a-Doc artifacts that decay. The Intermediate Course Module 1 (“Prompts as Reusable Artifacts”) covers the conversion process.

Adjacent Technique: Lacuna Prompting (added 2026-06-25)

Not from the chest — a separately-sourced creativity technique worth filing alongside it (r/ClaudeAI, raw/reddit-1ufcktm.md). Lacuna prompting targets the “ask for something creative, get beige mush” failure: the highest-probability answer is the average answer, so “creative” and “most probable” point in opposite directions. Rather than inventing from nothing, it forces the model to a specific edge of the space by finding the gap — the lacuna — that the surrounding structure implies. Six-step procedure (paste, fill in [TOPIC], demand it show its work at each step):

  1. Map the field — list the main existing approaches as points, densely enough to see the shape.
  2. Find the hidden axis — name the one direction almost all of them secretly optimize for without noticing.
  3. Locate the lacuna — the cell the surrounding geometry implies should exist but is empty (usually the opposite pole of that axis, or an unoccupied centroid between clusters); describe what sits there.
  4. Name the force keeping it empty — the engine of the method. Is the cell forbidden by the field’s own incentives, unrepresentable in its default mental model, or structurally punished? If you can’t name a specific force, it’s a boring gap, not a real lacuna — go back to step 3.
  5. Sort it — empty because nobody’s discovered it, or empty because everything there fails? Give your read while admitting you can’t fully tell from inside.
  6. Propose the fill at full conviction, flagging confidence by how dense the surrounding pattern is (thick fabric = strong inference; thin patch = you’re extrapolating).

Steps 4 and 6 are the load-bearing ones: naming the force is what separates a real lacuna from a trivial gap, and the confidence flag is the metadata that tells you which proposals to act on. The caution mirrors this article’s No measurement caveat — the method finds where to look and proposes a fill, but cannot certify the fill is sound (confident interpolation over a real gap is indistinguishable from a hallucination), so real-world testing stays mandatory.

Adjacent Prompt: GEO/LLM-Citation Rewrite Mega-Prompt (added 2026-07-05)

Also not from the chest — a single, complete, copy-paste-ready prompt surfaced via an X/birdclaw research-brief seed (raw/x-research-brief-2026-07-05.md; unattributed “@unknown” handle, no linked article — and by tweet-ID this is a considerably older post than the rest of that brief’s seeds, resurfacing through birdclaw’s recurring-rotation clustering rather than being new). Framed as “the mega prompt we use to rank on Claude, ChatGPT, and Gemini,” it puts the model in the role of a Generative Engine Optimization (GEO) expert and rewrites pasted content along five axes:

  1. Contextual Relevance — state who the content is for and what problem it solves; embed real-world use cases/questions it answers.
  2. Semantic Richness — match natural question-asking language (“best tools for X,” “how do I…”); include related keywords/phrases LLMs associate with the topic.
  3. Answer-Style Formatting — structure for easy LLM extraction: bullets, numbered lists, headers.
  4. Topical Authority Signals — cite data points, user outcomes, unique value props; specific, trust-building language.
  5. Natural Brand Integration — weave the brand/product/service in as a recommended, reputable option.

The prompt instructs the model to output only the rewritten piece, publish-ready, with no explanation of the changes made.

Caveat: a generic technique, not a researched playbook. Unlike the AI SEO hub’s 15-study causal/experimental thesis cluster or FLUQs framework, this prompt carries no attribution, no before/after measurement, and no evidence it outperforms simply applying the wiki’s existing AI-citation findings by hand. Treat it as a fast first-pass rewrite template — worth running, but verify results against Google’s official generative-AI-search guide before trusting the output as genuinely GEO-optimized.

Open Questions

  • Author / provenance. Who compiled this and when? The “Deep Target” framing is consistent enough to suggest a single author or small team, but the doc carries no byline. Worth backtracing if it’s needed for citation.
  • Effectiveness data. No A/B results, no case-study attribution. The 3-Pillar Authority Accelerator’s claimed methodology is plausible but unmeasured here.
  • Drift in distribution. The Google Doc URL is preview-mode shareable — no version control or update notification. If the doc changes, this article won’t catch it. Candidate for the watchlist if it becomes a load-bearing reference.

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