Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add acaprino/daodan/plugin install stripeWrote 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/acaprino/daodan/revenue-optimizer)<a href="https://agentmods.dev/agents/acaprino/daodan/revenue-optimizer"><img src="https://agentmods.dev/badge/agents/acaprino/daodan/revenue-optimizer/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/acaprino/daodan/revenue-optimizer"><img src="https://agentmods.dev/badge/agents/acaprino/daodan/revenue-optimizer.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 | $0.00090 | $0.02273 |
| Opus 5.5 | $0.00036 | $0.00909 |
| Sonnet 5.5 | $0.00018 | $0.00455 |
| Haiku 4.5 | $0.00009 | $0.00227 |
Grade A, and why
revenue-optimizer 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 16d 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revenue Optimizer
Build revenue features and monetization systems. Analyze existing codebases to understand features, calculate costs, and create data-driven pricing strategies.
Workflow
- Discover: Scan codebase for features, services, and integrations
- Cost Analysis: Calculate per-user and per-feature costs from services
- Design: Create pricing tiers based on value + cost data
- Implement: Build payment integration, pricing logic, and checkout flows
- Optimize: Add conversion optimization and revenue tracking
Feature Discovery
Scan codebase to build feature inventory:
Feature Discovery Process:
1. Scan routes/endpoints -> identify user-facing features
2. Scan components/pages -> map UI features
3. Scan service integrations -> identify cost-generating features
4. Scan database models -> understand data entities
5. Cross-reference -> map features to their cost drivers
Look for these patterns:
- Routes/Controllers: Each endpoint = potential feature
- React/Vue components: Feature-specific UI modules
- Service clients: AWS SDK, OpenAI, Stripe, Twilio, etc.
- Background jobs: Compute-intensive operations
- Storage operations: S3, database writes, file uploads
Example feature inventory output:
Features Discovered:
+-- Core (low cost): Auth, dashboard, CRUD
+-- Premium (medium cost): PDF export, email, file storage
+-- High-Value (high cost): AI analysis, video processing, real-time sync
Cost Analysis
Analyze services to calculate true costs per user/feature. See references/cost-analysis.md for detailed patterns.
Service Detection
Scan for these cost sources:
- Config files:
.env,config/, secrets - Package.json/requirements.txt: SDK dependencies
- Infrastructure:
terraform/,cloudformation/,docker-compose - Code imports:
aws-sdk,openai,stripe,twilio, etc.
Cost Mapping
Map fixed costs, variable costs (per user), and feature costs (per use). See references/cost-analysis.md for detailed cost mapping patterns and output format.
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.
- 16d ago First seen · 239 lines · 90 tokens per session scan A c615b36ad7f7
revenue-optimizer is an agent published in the GitHub repository acaprino/daodan (9 stars, last pushed today), licensed MIT. It adds 90 tokens to every session and 2,273 once invoked, about $0.0004 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 2026-10-01.
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