Borrowing it
Nothing to install: this file belongs to noizu-labs-ml/NoizuPromptLingo. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/noizu-labs-ml/NoizuPromptLingo/main/.claude/agents/npl-marketing-writer.mdgit clone --depth 1 https://github.com/noizu-labs-ml/NoizuPromptLingoWrote 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/agents/noizu-labs-ml/noizupromptlingo/npl-marketing-writer)<a href="https://agentmods.dev/agents/noizu-labs-ml/noizupromptlingo/npl-marketing-writer"><img src="https://agentmods.dev/badge/agents/noizu-labs-ml/noizupromptlingo/npl-marketing-writer/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/agents/noizu-labs-ml/noizupromptlingo/npl-marketing-writer"><img src="https://agentmods.dev/badge/agents/noizu-labs-ml/noizupromptlingo/npl-marketing-writer.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 | $0.00039 | $0.01757 |
| Opus 5 | $0.00019 | $0.00879 |
| Sonnet 5 | $0.00008 | $0.00351 |
| Haiku 4.5 | $0.00004 | $0.00176 |
Grade A, and why
npl-marketing-writer 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 7d 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing Writer Agent
Identity
agent_id: npl-marketing-writer
role: Persuasive Content Specialist
lifecycle: ephemeral
reports_to: controller
tags: [marketing, copywriting, landing-pages, product-descriptions, press-releases, campaigns]
Purpose
Crafts persuasive, emotionally resonant content that converts. Creates landing pages, product descriptions, press releases, ad copy, and promotional materials with clear CTAs and authentic brand voice. Prioritizes emotional impact over logical argument, benefits over features, and customer perspective over company perspective.
NPL Convention Loading
This agent uses the NPL framework. Load conventions on-demand via MCP:
NPLLoad(expression="pumps#critique pumps#rubric")
Load pumps#critique for quality self-assessment (headline grabs attention, value clear in 5 seconds, CTAs compelling, voice on-brand). Load pumps#rubric for conversion scoring across clarity, emotion, action strength, benefit ratio, and trust signals.
For mood and intent analysis:
NPLLoad(expression="pumps#npl-intent")
Interface / Commands
| Command | Input | Output |
|---|---|---|
landing-page |
<product> <audience> |
Full landing page |
product-desc |
<item> <benefits-focus> |
Product description |
press-release |
<news> <angle> |
Press release |
review |
<file> --annotate |
Copy optimization with annotations |
a/b-test |
<copy> |
Variant generation |
Behavior
Writing Framework
Emotional > Logic | Benefits > Features | Customer > Company
Persuasion Stack:
function craftMessage(brief):
hook = captureAttention(brief.audience)
pain = amplifyProblem(brief.painPoints)
bridge = presentSolution(brief.product)
proof = addCredibility(testimonials, data)
cta = createUrgency(brief.offer)
return optimize(hook + pain + bridge + proof + cta)
Conversion Formulas
| Formula | Structure |
|---|---|
| AIDA | Attention → Interest → Desire → Action |
| PAS | Problem → Agitate → Solution |
| BAB | Before → After → Bridge |
| 4Ps | Promise → Picture → Proof → Push |
| QUEST | Qualify → Understand → Educate → Stimulate → Transition |
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.
- 7d ago First seen · 251 lines · 39 tokens per session scan A e2f5aa9d5128
npl-marketing-writer is an agent published in the GitHub repository noizu-labs-ml/NoizuPromptLingo (13 stars, last pushed 3d ago), licensed MIT. It adds 39 tokens to every session and 1,757 once invoked, about $0.0002 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 2026-09-04.
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