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 fixing-underperforming-teamsgit 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/fixing-underperforming-teams)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/fixing-underperforming-teams"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/fixing-underperforming-teams/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/fixing-underperforming-teams"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/fixing-underperforming-teams.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 | $0.00036 | $0.01405 |
| Opus 5 | $0.00018 | $0.00702 |
| Sonnet 5 | $0.00007 | $0.00281 |
| Haiku 4.5 | $0.00004 | $0.00140 |
Grade A, and why
fixing-underperforming-teams 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 11d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fixing Underperforming Teams
Diagnose the root cause of dysfunction and take decisive action to restore high performance.
Help the user with fixing underperforming teams using insights from 12 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Diagnose the source - Help the user distinguish between leadership failure, skill gaps, or structural strategy issues.
- Identify complicity - Guide the leader in reflecting on how their own patterns are contributing to the team struggles.
- Draft an action plan - Structure a sequence of interventions ranging from process changes to talent decisions.
- Prepare for hard conversations - Provide scripts and frameworks for delivering direct feedback or handling terminations.
Core Principles
Apply the Enthusiastic Rehire Test
Uri Levine: "Every time that you hire someone new, mark your calendars for 30 days down the road and ask yourself one question, knowing what I know today, would I hire this person? If the answer is no, fire them immediately."
Regularly ask if you would still hire an individual today knowing what you know now. Admitting a hiring mistake early prevents cultural decay.
Distinguish Skill from Will
Anuj Rathi: "There are only three reasons why things do not happen the way you want them to happen as a leader. You can look at a person, and you would say either that person can't do, which is a capability issue, or they won't do, which is a motivation or an alignment issue, or they were not set up to do, which is really your problem that you didn't set up the ways of working now design properly."
Systematically determine if underperformance is a lack of ability (can't do) or a lack of motivation (won't do). This determines whether to coach or move the person.
Align the Five Elements of Change
Bangaly Kaba: "I found this framework travels with me. It's got these five components to it, vision, skills, incentives, resources, action plan, and you need all of those to have change. And then within those buckets you've got to figure out what are the right levers that you need to pull? What are the things that are missing?"
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.
- 11d ago First seen · 85 lines · 36 tokens per session scan A 2f507915d59b
fixing-underperforming-teams is a skill published in the GitHub repository RefoundAI/lenny-skills (1,318 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 1,405 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.
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.