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 whawkinsiv/solo-founder-skills --skill ai-featuresgit clone --depth 1 https://github.com/whawkinsiv/solo-founder-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/whawkinsiv/solo-founder-skills/ai-features)<a href="https://agentmods.dev/skills/whawkinsiv/solo-founder-skills/ai-features"><img src="https://agentmods.dev/badge/skills/whawkinsiv/solo-founder-skills/ai-features/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/whawkinsiv/solo-founder-skills/ai-features"><img src="https://agentmods.dev/badge/skills/whawkinsiv/solo-founder-skills/ai-features.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00070 | $0.02022 |
| Opus 5 | $0.00035 | $0.01011 |
| Sonnet 5 | $0.00014 | $0.00404 |
| Haiku 4.5 | $0.00007 | $0.00202 |
Grade A, and why
ai-features 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Features & LLM Integration
AI features should make your product 10x better at its core job, not be a marketing checkbox. This skill helps you choose the right AI pattern, manage costs, and ship AI features that users actually value.
Core Principles
- AI features should make your product 10x better at its core job, not be a marketing checkbox.
- Start with the API, not a custom model. You don't need to train anything.
- Cost per API call matters at scale. Design for it from day one.
- Prompt engineering is your product differentiator. The model is the same for everyone.
- Always have a fallback. AI features should degrade gracefully, not crash the app.
When to Add AI Features
Add AI When:
- Users do something repetitive that AI could automate (drafting, categorizing, summarizing)
- Users need help interpreting data (analysis, recommendations, insights)
- You can save users significant time on a task they do frequently
- AI makes your product dramatically easier for non-experts to use
- Competitors have AI features and users expect parity
Don't Add AI When:
- It's just a chatbot wrapper with no product context
- You're adding it for marketing ("AI-powered!") without clear user benefit
- The task requires 100% accuracy (legal, medical, financial decisions)
- A simple rule-based approach would work just as well
- You haven't validated that users want it
AI Feature Patterns for SaaS
Pattern 1: Smart Drafts / Generation
What: AI writes a first draft that users edit and refine.
Examples: Email drafts, report summaries, product descriptions, social posts.
Tell AI:
Add an AI draft feature to [describe where in the app].
When the user clicks "Generate draft," call the Claude API with:
- Context from [what data the AI should use]
- A system prompt that produces [describe the output format]
- User can edit the result before saving
Include: loading state, error handling, and a "regenerate" button.
Use the Claude API with the claude-sonnet-4-5-20250929 model.
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 · 252 lines · 70 tokens per session scan A 4a22d468360e
ai-features is a skill published in the GitHub repository whawkinsiv/solo-founder-skills (243 stars, last pushed 16d ago), licensed MIT. It adds 70 tokens to every session and 2,022 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 2026-08-30.
Other skills, from other repositories
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llm-app-patterns
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llm-security
Use for authorized security assessment of LLM applications and AI agents, including prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.
llm-ops
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ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.