How do I install the Lenny Research skill?

Run npx skillmds@latest add mnttnm/lenny-research in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Lenny Research safe to use?

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Which AI agents work with Lenny Research?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Lenny Research free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Lenny Research?

mnttnm (@mnttnm) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Lenny Research by mnttnm — Agent Skill

Name: Lenny Research
Author: mnttnm

Lenny Research

Research Lenny Rachitsky's archive of 349 newsletter posts and 289 podcast interviews for comprehensive, well-cited answers. Auto-trigger when the user asks practical questions about startups, product management, growth, B2B SaaS, pricing, leadership, career development, hiring, workplace soft skills, AI product work, go-to-market, analytics, org design, or user research. Handles topic deep-dives, guest-specific queries, actionable advice requests, career transition questions, cross-topic comparisons, and multi-faceted questions spanning several domains. Use the lennys-data MCP tools for search and retrieval.

mnttnm e12952c 3 files · 11.3 KB Updated Sep 17, 2026

File contents

Lenny's Archive Research

Research Lenny's archive using multi-probe retrieval for comprehensive, well-cited answers.

Workflow

1. Decompose the Query

Identify query type and search dimensions:

Topic deep-dive: "What has Lenny covered about pricing?" → single topic, broad search
Guest-specific: "What did Shreyas Doshi say about PM craft?" → guest + topic
Actionable advice: "How should I run my first 1:1?" → practical frameworks
Comparison: "Compare advice on PMF from different guests" → multiple perspectives
Multi-faceted: "Moving from engineering to product in the AI era" → 2-3 topic dimensions

Read references/topic-map.md to expand query terms into pipe-delimited search probes. Most real questions span 2-3 topic clusters — create a separate search probe for each dimension.

2. Parallel Multi-Probe Search

Fire 2-3 search_content calls in parallel with different term expansions. Use content_type filter only when query clearly targets one type.

Example for "Moving from engineering to product in the AI era":

Probe 1: search_content("career|transition|role|engineering to product|switch", limit=15)
Probe 2: search_content("AI|LLM|product manager|new world|changing", limit=10)

If Probe 1 returns <3 results, skip further probes — topic is sparse, note this in response.

3. Merge, Rank, Extract

Deduplicate by filename. Rank: multi-probe overlap > title match > tag relevance > recency.

For top 5-10 results, extract in parallel:

Newsletters (typically <5K words): read_content for full text
Podcasts (typically >10K words): read_excerpt with radius=800, iterate match_index 0-2

See references/retrieval-guide.md for detailed extraction strategy per query type.

4. Synthesize

Adaptive depth based on query type:

Query type	Sources	Style
Topic deep-dive	5-10	Group by sub-theme, multiple perspectives
Guest-specific	3-5 episodes	Organize by topics discussed
Actionable advice	3-5	Lead with clearest framework, then alternatives
Comparison	3-5 per side	Table or side-by-side format
Quick factual	1-3	Direct answer with citation

Citation Rules

Cite every claim: [Post Title](filename) — include guest name for podcasts
Include 2-3 direct quotes per response when pithy/memorable quotes exist
Group findings by sub-theme, not by source file
Flag sparse coverage: "I found limited coverage of this in Lenny's archive"
Note: the archive excludes the last 3 months of newsletter posts

Speed Rules

All search probes execute in parallel (single tool call batch)
All excerpt/content reads execute in parallel
Never full-read a podcast transcript unless specifically needed
Skip secondary probes when primary search returns <3 results

---name: lenny-researchdescription: Research Lenny Rachitsky's archive of 349 newsletter posts and 289 podcast interviews for comprehensive, well-cited answers. Auto-trigger when the user asks practical questions about startups, product management, growth, B2B SaaS, pricing, leadership, career development, hiring, workplace soft skills, AI product work, go-to-market, analytics, org design, or user research. Handles topic deep-dives, guest-specific queries, actionable advice requests, career transition questions, cross-topic comparisons, and multi-faceted questions spanning several domains. Use the lennys-data MCP tools for search and retrieval.---# Lenny's Archive ResearchResearch Lenny's archive using multi-probe retrieval for comprehensive, well-cited answers.## Workflow### 1. Decompose the QueryIdentify query type and search dimensions:- **Topic deep-dive**: "What has Lenny covered about pricing?" → single topic, broad search- **Guest-specific**: "What did Shreyas Doshi say about PM craft?" → guest + topic- **Actionable advice**: "How should I run my first 1:1?" → practical frameworks- **Comparison**: "Compare advice on PMF from different guests" → multiple perspectives- **Multi-faceted**: "Moving from engineering to product in the AI era" → 2-3 topic dimensionsRead [references/topic-map.md](references/topic-map.md) to expand query terms into pipe-delimited search probes. Most real questions span 2-3 topic clusters — create a separate search probe for each dimension.### 2. Parallel Multi-Probe SearchFire 2-3 `search_content` calls **in parallel** with different term expansions. Use `content_type` filter only when query clearly targets one type.Example for "Moving from engineering to product in the AI era":```Probe 1: search_content("career|transition|role|engineering to product|switch", limit=15)Probe 2: search_content("AI|LLM|product manager|new world|changing", limit=10)```If Probe 1 returns <3 results, skip further probes — topic is sparse, note this in response.### 3. Merge, Rank, ExtractDeduplicate by filename. Rank: multi-probe overlap > title match > tag relevance > recency.For top 5-10 results, extract in parallel:- **Newsletters** (typically <5K words): `read_content` for full text- **Podcasts** (typically >10K words): `read_excerpt` with `radius=800`, iterate `match_index` 0-2See [references/retrieval-guide.md](references/retrieval-guide.md) for detailed extraction strategy per query type.### 4. Synthesize**Adaptive depth** based on query type:| Query type | Sources | Style ||------------|---------|-------|| Topic deep-dive | 5-10 | Group by sub-theme, multiple perspectives || Guest-specific | 3-5 episodes | Organize by topics discussed || Actionable advice | 3-5 | Lead with clearest framework, then alternatives || Comparison | 3-5 per side | Table or side-by-side format || Quick factual | 1-3 | Direct answer with citation |## Citation Rules- Cite every claim: `[Post Title](filename)` — include guest name for podcasts- Include 2-3 direct quotes per response when pithy/memorable quotes exist- Group findings by sub-theme, not by source file- Flag sparse coverage: "I found limited coverage of this in Lenny's archive"- Note: the archive excludes the last 3 months of newsletter posts## Speed Rules- All search probes execute in parallel (single tool call batch)- All excerpt/content reads execute in parallel- Never full-read a podcast transcript unless specifically needed- Skip secondary probes when primary search returns <3 results

Frequently asked questions