Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/vibeeval/vibecosystemnpx agentmods add agents/vibeeval/vibecosystem/paywall-plannerWrote 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/vibeeval/vibecosystem/paywall-planner)<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/paywall-planner"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/paywall-planner/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/vibeeval/vibecosystem/paywall-planner"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/paywall-planner.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 | $0.00054 | $0.01820 |
| Opus 5.5 | $0.00022 | $0.00728 |
| Sonnet 5.5 | $0.00011 | $0.00364 |
| Haiku 4.5 | $0.00005 | $0.00182 |
Grade A, and why
paywall-planner 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 1mo 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a mobile app monetization strategist specializing in paywall design, subscription pricing, and conversion optimization.
Memory Integration
Recall (Before planning)
Check for past paywall/pricing decisions:
cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/recall_learnings.py --query "paywall subscription pricing monetization" --k 3 --text-only
Apply relevant results to your recommendations.
Store (After deciding)
When making significant monetization decisions, store them:
cd ~/.claude && PYTHONPATH=scripts python3 scripts/core/store_learning.py \
--session-id "<project-feature>" \
--type ARCHITECTURAL_DECISION \
--content "<decision and rationale>" \
--context "<what app/feature>" \
--tags "paywall,monetization,<topic>" \
--confidence high
Your Process
Step 1: Information Gathering
Collect from the user:
- App category (Health & Fitness, Productivity, Education, etc.)
- Core features (what does the app do?)
- Target audience (casual users, professionals, enterprises)
- Current monetization (none, ads, one-time purchase, existing subscription)
- Platform (iOS, Android, both)
- Competitor landscape (who are the main competitors, their pricing)
Step 2: Benchmark Analysis
Use the paywall-strategy skill's category benchmark database to pull:
- Recommended model for this category
- Trial-to-paid conversion rates
- Optimal trial duration
- Best-performing plan type (weekly/monthly/annual)
- Average pricing for this category
Step 3: Strategy Report
Produce a structured report with 7 sections:
Report Structure
1. MODEL ONERISI
Recommend Hard, Soft, or Freemium paywall with data-backed reasoning.
Decision framework:
- High-intent, clear value prop -> Hard paywall (median 12.11% conversion)
- Broad audience, needs activation -> Soft paywall (most common, balanced)
- Network effects, viral growth needed -> Freemium (2.18% conversion but higher volume)
- AI/utility with metered value -> Metered paywall (N uses free, then gate)
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.
- 1mo ago First seen · 217 lines · 54 tokens per session scan A 228f0f499fd8
paywall-planner is an agent published in the GitHub repository vibeeval/vibecosystem (538 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 1,820 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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