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 Pattyboi101/oats-autonomous-agents --skill first-revenuegit clone --depth 1 https://github.com/Pattyboi101/oats-autonomous-agentsWrote 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/pattyboi101/oats-autonomous-agents/first-revenue)<a href="https://agentmods.dev/skills/pattyboi101/oats-autonomous-agents/first-revenue"><img src="https://agentmods.dev/badge/skills/pattyboi101/oats-autonomous-agents/first-revenue/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/pattyboi101/oats-autonomous-agents/first-revenue"><img src="https://agentmods.dev/badge/skills/pattyboi101/oats-autonomous-agents/first-revenue.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 | $0.00040 | $0.01090 |
| Opus 5 | $0.00020 | $0.00545 |
| Sonnet 5 | $0.00008 | $0.00218 |
| Haiku 4.5 | $0.00004 | $0.00109 |
Grade A, and why
first-revenue 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 12d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
First Revenue — Your Project
You are advising on Your Project's path to first revenue. The product exists (3,100+ tools, MCP server, migration intelligence). The challenge is going from 0 to 1 paying customer.
Before Starting
Check current state:
- How many users? (Currently ~55)
- How many maker claims? (Check prod DB)
- What distribution is live? (Blog, registries, outreach emails)
- What responses have we gotten? (Email replies, claims, traffic)
Core Principle (from Sahil Lavingia)
Skip the launch. Focus on selling. Every seemingly overnight success is built on hard work. Sell one by one, learn from each interaction, build momentum.
For Your Project this means: don't build pricing pages and payment flows. Have ONE conversation with ONE maker about what they'd pay for. Then build that.
The Concentric Circles (adapted for Your Project)
Circle 1: Makers Who Already Claimed
These people engaged with us. They're warmest.
- What did they do after claiming? (Check dashboard usage)
- What data did they look at?
- What did they wish they could see?
- ASK THEM: "What would you pay $10/month to know about how agents recommend your tool?"
Circle 2: Makers Who Opened Our Email
They showed interest but didn't claim.
- Follow up with more data about their specific tool
- Show them something they can't get elsewhere (migration data, agent query logs)
Circle 3: Cold Outreach to Tool Makers
The 24 emails we sent. More batches if earlier ones show signal.
What We Could Sell (honest assessment)
Option A: Sponsored Placement ($29-99/mo)
"Your tool appears first when agents search for {category}"
- Pro: clear value, easy to understand
- Con: need agent volume to make it meaningful. Currently low.
Option B: Competitive Intelligence Report ($29 one-off)
"Here's what repos are migrating from your competitor to you (and vice versa)"
- Pro: unique data nobody else has, can sell TODAY
- Con: one-off, doesn't compound
Option C: Maker Dashboard Premium ($9-29/mo)
"See which agents recommend you, what queries surface you, daily trends"
- Pro: recurring, scales with more agent usage
- Con: data is thin for most tools right now
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.
- 12d ago First seen · 119 lines · 40 tokens per session scan A 56e71e406a7e
first-revenue is a skill published in the GitHub repository Pattyboi101/oats-autonomous-agents (6 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 1,090 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…