Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Vibe-Marketer/plugins-and-skills --skill belief-based-marketinggit clone --depth 1 https://github.com/Vibe-Marketer/plugins-and-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vibe-marketer/plugins-and-skills/belief-based-marketing)<a href="https://agentmods.dev/skills/vibe-marketer/plugins-and-skills/belief-based-marketing"><img src="https://agentmods.dev/badge/skills/vibe-marketer/plugins-and-skills/belief-based-marketing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vibe-marketer/plugins-and-skills/belief-based-marketing"><img src="https://agentmods.dev/badge/skills/vibe-marketer/plugins-and-skills/belief-based-marketing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.01415 |
| Opus 5 | $0.00028 | $0.00707 |
| Sonnet 5 | $0.00011 | $0.00283 |
| Haiku 4.5 | $0.00006 | $0.00142 |
Grade A, and why
belief-based-marketing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<quick_start> Provide these materials for analysis:
- Offer documents, product descriptions
- Sales call transcripts, customer reviews
- FAQs, testimonials, support tickets
- Social media comments, feedback
I will analyze them to:
- Extract existing buyer beliefs (positive and negative)
- Identify the "3 M's" - Myths, Mistakes, Misconceptions
- Determine required beliefs for conversion
- Build belief-shifting content using Pathos, Logos, Ethos
- Create sequenced messaging flow from Awareness to Decision </quick_start>
- What's the offer? (Product/service/program being sold)
- What materials do you have? (Any of: sales pages, transcripts, reviews, testimonials, support tickets, social comments, FAQs)
- What's the conversion problem? (Optional - where are you losing prospects?)
I'll extract 80-90% of insights from your materials without adding assumptions.
Thoroughly review all provided materials. Extract and summarize:
- Recurring patterns: questions, objections, praises, confusions
- Language used and emotional tones
- Segmentation signals (beginner vs. advanced, customer types)
Categorize into three buckets:
- Customer Interactions (questions from calls/reviews)
- Offer Details (features/benefits described)
- Feedback Patterns (successes/failures in testimonials)
Output: Bulleted summary with 3-5 examples per category including direct quotes. </step_1>
<step_2> Step 2: Identify Existing Beliefs
Mine inputs for what prospects currently believe about:
- The problem
- Solutions in general
- Your specific offer
Hunt for the "3 M's":
- Myths - False ideas ("This is too expensive for what it does")
- Mistakes - Wrong actions (focusing on irrelevant details)
- Misconceptions - Misunderstandings ("This only works for big businesses")
Assess "chain of beliefs" length:
- Short (inner circle: already trusting, minimal convincing)
- Long (outsiders: skeptical, need foundational education)
Generate 15-25 beliefs. Assume prospects are beginners to counter "curse of knowledge."
Output Table:
| Belief | Type | Evidence from Inputs | Journey Stage | Impact on Conversion |
|---|---|---|---|---|
| </step_2> |
<step_3> Step 3: Determine Required Beliefs
For each existing belief, reframe: "What must the prospect believe instead for this to be an obvious, irresistible choice?"
Use the "ladder of importance":
- Start with foundational beliefs (problem existence)
- Build to advanced beliefs (your unique edge)
Address alternatives/competitors mentioned in inputs.
Output Table:
| Existing Belief | Required Belief | Rationale | Journey Stage | Priority |
|---|---|---|---|---|
| </step_3> |
<step_4> Step 4: Build the Necessary Beliefs
Develop targeted content using Aristotle's persuasion modes:
- Pathos (Emotional Appeal): Relatable stories, testimonials evoking desire/fear
- Logos (Logical Proof): Data, stats, comparisons, demos
- Ethos (Credibility): Authority builders, credentials, social proof
Tailor intensity:
- Short chains: Focus on decision-stage trust
- Long chains: Build from basics
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 165 lines · 55 tokens per session scan A 4e53f683ad44
belief-based-marketing is a skill published in the GitHub repository Vibe-Marketer/plugins-and-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 55 tokens to every session and 1,415 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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