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 continuous-discoverygit 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/continuous-discovery)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/continuous-discovery"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/continuous-discovery/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/continuous-discovery"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/continuous-discovery.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 | $0.00029 | $0.01543 |
| Opus 5 | $0.00015 | $0.00772 |
| Sonnet 5 | $0.00006 | $0.00309 |
| Haiku 4.5 | $0.00003 | $0.00154 |
Grade A, and why
continuous-discovery 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 10d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuous Product Discovery
Turn customer feedback from a periodic chore into a high-frequency engine for product decisions.
Help the user with continuous product discovery using insights from 23 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Audit current proximity - Evaluate the frequency and quality of direct team-to-user interactions and identify existing gatekeepers.
- Define discovery rituals - Help set up recurring cadences for interviews, demo days, and support shifts that involve the entire product trio.
- Structure the opportunity space - Guide the translation of raw feedback into a visual map of unmet needs, pain points, and desires.
- Accelerate evidence gathering - Recommend lightweight methods for testing assumptions through prototypes and behavior mapping before committing to full builds.
Core Principles
Distinguish needs from solutions
Teresa Torres: "I can tell you that opportunity is an unmet need pain point or desire, and that's great. But I can tell you that 98% of people that write opportunities write them as solutions. So we tend to just really struggle with this distinction between the problem space and the solution space."
True discovery requires defining every opportunity strictly as an unmet customer need rather than a pre-conceived feature idea.
Remove layers between builders and users
Brian Tolkin: "Talking to customers every single day like one-on-one onboarding drivers responding to support tickets, there's no centralized support team, there was no closer to the customer, right? And so I think that foundation actually for really understanding what moves the business and being super close to the customer actually is a pretty good foundation for them going on to say, okay, what do we actually want to build in a more scalable technology way?"
Deep empathy is built by eliminating centralized filters and having product teams engage directly in onboarding and support.
Integrate discovery into execution
Itamar Gilad: "Google, was what I call an evidence guided company. So essentially it put a high premium on focusing on customers, coming up with a lot of ideas on looking at the data, looking at how these ideas actually worked out. They weren't shy about launching betas and things that were very rough and incomplete and learning from that and then they expected people to take action based on the results."
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.
- 10d ago First seen · 95 lines · 29 tokens per session scan A f5cccd8109a4
continuous-discovery is a skill published in the GitHub repository RefoundAI/lenny-skills (1,315 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 1,543 once invoked, about $0.0001 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
llama-cpp
Run LLM inference with llama.cpp on CPU, Apple Silicon, AMD/Intel GPUs, or NVIDIA — plus GGUF model conversion and quantization (2–8 bit with K-quants and imatrix). Covers CLI, Python bindings, OpenAI-compatible server, and Ollama/LM Studio integration. Use for edge deployment, M1/M2/M3/M4 Macs, CUDA-less…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.
github-auth
Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.
ideation
Generate project ideas through creative constraints. Use when the user says 'I want to build something', 'give me a project idea', 'I'm bored', 'what should I make', 'inspire me', or any variant of 'I have tools but no direction'. Works for code, art, hardware, writing, tools, and anything that can be made.
notion
Notion API for creating and managing pages, databases, and blocks via curl. Search, create, update, and query Notion workspaces directly from the terminal.
ocr-and-documents
Extract text from PDFs and scanned documents. Use webextract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.