Lenny research
Custom Claude Code skills spanning UI/UX, productivity, and developer tooling
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What its author says it does
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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.
SKILL.md
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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_contentfor full text - Podcasts (typically >10K words):
read_excerptwithradius=800, iteratematch_index0-2
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