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 recovering-from-failuregit 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/recovering-from-failure)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/recovering-from-failure"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/recovering-from-failure/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/recovering-from-failure"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/recovering-from-failure.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 | $0.00034 | $0.01737 |
| Opus 5 | $0.00017 | $0.00869 |
| Sonnet 5 | $0.00007 | $0.00347 |
| Haiku 4.5 | $0.00003 | $0.00174 |
Grade A, and why
recovering-from-failure 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 9d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recovering From Failure
Turn product setbacks and stalled growth into strategic breakthroughs and pivots.
Help the user with recovering from failure using insights from 20 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Diagnose the failure - Use frameworks like the Four Ps or root cause analysis to identify exactly why the product or feature isn't gaining traction.
- Assess conviction - Determine if the team still believes in the problem and solution after facing real world challenges or if it is time to move on.
- Identify the pivot path - Look for organic pull within existing features or technical foundations to find a more viable direction.
- Execute with speed - Move decisively to abandon failing initiatives and reallocate resources toward high conviction opportunities.
Core Principles
Stay in the game to reinvent
Bret Taylor: "They gave me another shot to do a V2 of it and I got the impression it wasn't like my last shot, but I certainly was feeling a little dejected from going from a hot shot, new PM to a new thing. So, we spent a lot of time thinking about how can you make something that's just much more compelling and not just a digital version of the Yellow Pages and not just so similar to some of the other products out there."
Rebuilding a reputation after a high profile failure requires the resilience to stay active and the willingness to completely reinvent the product.
Pivot toward domain expertise
Dalton Caldwell: "Usually a successful pivot gets warmer instead of colder from what you're an expert at and somehow builds on what you learned on the prior idea. Right? And so in the case of Brex, it was they had worked on a FinTech company in Brazil when they were younger, and so I'm like, 'You need to work more on the thing all about and not the thing nothing about.'"
A successful pivot leverages lessons or internal tools developed during previous attempts and moves you closer to your existing expertise.
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.
- 9d ago First seen · 100 lines · 34 tokens per session scan A ead9731571cd
recovering-from-failure is a skill published in the GitHub repository RefoundAI/lenny-skills (1,321 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 1,737 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 2026-09-03.
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.