Lenny Skills is a collection of product-management and engineering workflows for Claude Code and other AI agents, covering areas such as strategy, research, planning, shipping, growth, and hiring. Each skill gives an agent specialized guidance, frameworks, checklists, or templates for product work, and the catalogue contains many of these skills.
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 RefoundAI/lenny-skills --skill ai-evalsgit clone --depth 1 https://github.com/RefoundAI/lenny-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/refoundai/lenny-skills/ai-evals)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/ai-evals"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/ai-evals/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/refoundai/lenny-skills/ai-evals"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/ai-evals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00036 | $0.01321 |
| Opus 5 | $0.00018 | $0.00660 |
| Sonnet 5 | $0.00007 | $0.00264 |
| Haiku 4.5 | $0.00004 | $0.00132 |
Grade A, and why
ai-evals 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Evaluation Strategy
Move beyond vibe checks to systematic, empirical measurement of AI product quality and reliability.
Help the user with ai evaluation strategy using insights from 11 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Identify Failure Modes - Help the user conduct error analysis on real traces to find where the system specifically breaks.
- Select Eval Methods - Recommend the right mix of human, code, and LLM judges based on the specific technical use case.
- Build Gold Sets - Assist in curating a reference dataset of high-quality examples to act as the ground truth for your application.
- Operationalize - Guide the user in integrating these evaluations into a CI/CD pipeline for continuous quality improvement.
Core Principles
Automate the Value Chain
Brendan Foody: "I think that for enterprises especially, the core way to think about it is how can they build a test or systematic way to measure how well AI automates their core value chain? So if it's an architecture firm that's producing these architecture diagrams of what they provide to their end customer, how can they effectively measure that? And each company has its own value chain or maybe a handful of them if it's a multi-product company."
Identify the core deliverables unique to your business and develop systematic tests to measure how accurately AI can replicate those specific tasks.
Prioritize Subjective Excellence
Edwin Chen: "We are looking for a Nobel Prize-winning poetry. Is this poetry unique? Is it full of subtle imagery? Does it surprise you and target your heart? Does it teach you something about the nature of moonlight?"
True data quality is defined by deep, subjective human excellence, such as emotional resonance and uniqueness, rather than superficial binary checks.
Eliminate Vibe Checks
Hamel Husain & Shreya Shankar: "Evals help you create metrics that you can use to measure how your application is doing and kind of give you a way to improve your application with confidence. That you have a feedback signal in which to iterate against."
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 79 lines · 36 tokens per session scan A 20b3cec6fac9
ai-evals is a skill published in the GitHub repository RefoundAI/lenny-skills (1,321 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 1,321 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 2026-08-30.
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