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 kkanakas/AI-Prompts-for-Product-Management --skill ai-pm-promptsgit clone --depth 1 https://github.com/kkanakas/AI-Prompts-for-Product-ManagementWrote 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/kkanakas/ai-prompts-for-product-management/ai-pm-prompts)<a href="https://agentmods.dev/skills/kkanakas/ai-prompts-for-product-management/ai-pm-prompts"><img src="https://agentmods.dev/badge/skills/kkanakas/ai-prompts-for-product-management/ai-pm-prompts/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/kkanakas/ai-prompts-for-product-management/ai-pm-prompts"><img src="https://agentmods.dev/badge/skills/kkanakas/ai-prompts-for-product-management/ai-pm-prompts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00747 | $0.02475 |
| Opus 5 | $0.00374 | $0.01238 |
| Sonnet 5 | $0.00149 | $0.00495 |
| Haiku 4.5 | $0.00075 | $0.00248 |
Grade A, and why
ai-pm-prompts 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ai-pm-prompts — 95% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Prompts for Product Managers
A structured, version-controlled prompt library for Product Managers using GenAI across the full product lifecycle — from customer discovery and competitive analysis through to stakeholder alignment, strategy, and executive communication.
Each prompt follows the same structure: context, inputs, and a defined output format so you get consistent, usable results across Claude, ChatGPT, Gemini, GitHub Copilot, or any AI assistant.
How to Use
- Identify the phase you are in (discovery, research, ideation, evaluation, strategy, communication)
- Find the relevant prompt in
prompts/<category>/ - Fill in the
[PLACEHOLDERS]with your specific context - Run the prompt in your preferred AI tool, or use the MCP server to call it directly
Prompt Library
| Category | Directory | What's Inside |
|---|---|---|
| Architecture Diagrams | prompts/architecture-diagrams/ |
Sequence diagrams, UML class diagrams, Entity Relationship Diagrams, and flowcharts from code repositories |
| Communications | prompts/communications/ |
Weekly leadership updates, customer meeting transcript to Slack update and Jira tickets |
| Competitive Analysis | prompts/competitive-analysis/ |
Positioning, feature comparison, target customers, capability gap analysis |
| Customer Discovery | prompts/customer-discovery/ |
Interview guides, transcript analysis, JTBD, survey analysis, sentiment analysis of work items, pattern finding |
| Idea Evaluation | prompts/idea-evaluation/ |
RICE scoring, pre-mortem, assumption mapping, validation questions, Kano analysis, MoSCoW prioritization, Ulwick opportunity scoring (underserved vs. over-served) |
| Ideation | prompts/ideation/ |
Problem-to-solution generation, SCAMPER, VRIO, MECE analysis |
| Market Research | prompts/market-research/ |
Structured market analysis, cross-referencing sources, TAM/SAM/SOM sizing, competitive analysis with value chain mapping and SWOT |
| Metrics | prompts/metrics/ |
Feature success metrics, and AI product metrics frameworks covering latency, task completion, output quality, business value, and trust and safety |
| PRDs | prompts/prds/ |
Product Requirements Documents (Amazon 6-pager style, AI PRD, and EARS-notation PRD), use case documentation, features and user stories, and a Given/When/Then acceptance criteria generator with And chaining and But negative assertions |
| Prototyping | prompts/prototyping/ |
Lo-fi wireframe briefs, mid-fi flow specs, clickthrough demo scripts, Wizard of Oz protocols, prompt-powered UI generation, AI feature stubs, agent workflow simulations, API-connected LLM prototypes, narrative prototypes, and data dashboard prototypes |
| Release Notes | prompts/release-notes-generator/ |
Release notes from git commit history |
| Stakeholder Management | prompts/stakeholder-management/ |
RACI matrix, DACI decision framework, stakeholder influence and support analysis |
| Strategy | prompts/strategy/ |
Product strategy canvas, OKR generation, working backwards, Amazon PR/FAQ, dependency identification, private preview planning, value proposition design, Big Rock decomposition, launch gates |
| Synthetic Users | prompts/synthetic-users/ |
Creating and interviewing AI-generated personas |
| Trend Analysis | prompts/trend-analysis/ |
Feedback trend monitoring, industry trend identification |
| User Journey Maps | prompts/user-journey-maps/ |
End-to-end user journey mapping with emotions and opportunities |
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.
- 12d ago First seen · 128 lines · 747 tokens per session scan A 8cbabca535ad
ai-pm-prompts is a skill published in the GitHub repository kkanakas/AI-Prompts-for-Product-Management (3 stars, last pushed 3mo ago), licensed MIT. It adds 747 tokens to every session and 2,475 once invoked, about $0.0037 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 2026-08-31.
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