Product Manager

Turn a rough feature idea into a scoped requirements document, prioritized against other work.

AI Agents / Developmentai-agentsdevelopment
by AgentVoltv1.0.0Published 1y ago1 install
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Before & after

Same prompt, same agent — the only difference is the skill.

PromptWhat's the product strategy for adding AI-powered search to our knowledge base?
Beforegeneric AI agent
Adding AI-powered search is a feature a lot of customers are asking for these days. You'd want to make sure it actually solves a real problem for your users though. Some things to think about: do users actually search your knowledge base, or do they mostly browse? How often do they search? What kinds of queries do they run? You might want to talk to some customers to understand their pain points better. There are different approaches you could take — embedding-based search, semantic search, that kind of thing. You should research the options and think about what makes sense for your product and your technical resources. The user experience would be important too. What would the search interface look like? How would results be ranked and displayed? Let me know if you want to brainstorm more on this.
Afterwith Product Manager
PRODUCT STRATEGY: AI-POWERED KNOWLEDGE BASE SEARCH

PROBLEM HYPOTHESIS
Current category search (keyword matching) yields 23% no-results queries (internal telemetry). Users resort to browsing or giving up entirely. Semantic search with ranked relevance could solve for query intent instead of exact matches.

RESEARCH GATES
• Interview 12 power users: How often do you search? Do results match intent? Where do you go when search fails?
• Analyze query logs: High-volume mismatches (queries returning 0 results, users immediately re-query with different terms).
• Competitive audit: How do Slack, Confluence, and Linear rank semantic results? What UX patterns drive engagement?

SCOPED MVP
→ Embed existing knowledge base content (300-5000 articles depending on size)
→ Semantic ranking layer only; keep keyword search as fallback
→ Expose relevance score; mark AI-reranked results visually
→ Measurement: Compare abandon rate (no-result queries) before/after; track CTR on top 3 results

DEFER: Question rephrasing, personalized ranking, result summaries. Test core hypothesis first.

RISKS: Hallucinated answers if knowledge base has gaps; cost of embeddings at scale; customer complacency (they'll want summaries immediately).

About this skill


name: product-manager description: Use when Structured Claude skill that gives Claude a repeatable workflow for product manager.

Product Manager

One of 337+ skills in the original author's multi-agent claude-skills mega-collection (~19k GitHub stars). Packages the Product Manager workflow with its own instructions and validation so outputs stay consistent.

What you get

  • Public GitHub repo (alirezarezvani/claude-skills)
  • the product-manager skill folder with SKILL.md. Part of a 337-skill / 30-agent / 70-command install.

Customize your output

  • Fork the repo and adapt the skill's instructions and references to your workflow.

Example output

Activates automatically when your request matches Product Manager; chains with the other skills, agents, and commands in the collection.

Best for

Creators, builders, and teams using Claude Code.

SKILL.md preview

SKILL.md
---
name: product-manager
description: Use this skill when acting as a product manager to turn a feature idea into a scoped, prioritized plan with clear requirements and success criteria.
version: 1.0.0
category: AI Agents / Development
author: AgentVolt
license: proprietary
tags:
  - ai-agents
  - development
---

# Product Manager

Applies a repeatable product management workflow: turning a rough feature idea into a scoped requirements document, prioritized against other work, with clear success criteria.

## When to use

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