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.
git 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/rules/kkanakas/ai-prompts-for-product-management/pm-prompts)<a href="https://agentmods.dev/rules/kkanakas/ai-prompts-for-product-management/pm-prompts"><img src="https://agentmods.dev/badge/rules/kkanakas/ai-prompts-for-product-management/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/rules/kkanakas/ai-prompts-for-product-management/pm-prompts"><img src="https://agentmods.dev/badge/rules/kkanakas/ai-prompts-for-product-management/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 | $0.00000 | $0.00652 |
| Opus 5 | $0.00000 | $0.00326 |
| Sonnet 5 | $0.00000 | $0.00130 |
| Haiku 4.5 | $0.00000 | $0.00065 |
Grade A, and why
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.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Prompts for Product Managers
This repo is a structured prompt library for PMs. Every prompt lives in prompts/<category>/ and follows the same format: Context → Inputs → Outputs.
How to use a prompt
- Identify the right prompt from the catalog below
- Read the prompt file to get the full template
- Fill in
[PLACEHOLDERS]with the user's specific context - Return the filled prompt ready to run
Prompt Catalog
| Category | Directory | When to use |
|---|---|---|
| Architecture Diagrams | prompts/architecture-diagrams/ |
Sequence diagrams from code |
| Communications | prompts/communications/ |
Weekly leadership updates for Teams/Slack |
| Competitive Analysis | prompts/competitive-analysis/ |
Positioning, feature comparison, capability gaps |
| Customer Discovery | prompts/customer-discovery/ |
Interview guides, transcripts, JTBD, surveys, sentiment |
| Idea Evaluation | prompts/idea-evaluation/ |
RICE, pre-mortem, Kano, MoSCoW |
| Ideation | prompts/ideation/ |
Problem-to-solution, SCAMPER, VRIO, MECE |
| Market Research | prompts/market-research/ |
Market analysis, evidence checks, landscape reports |
| Metrics | prompts/metrics/ |
Feature success metrics |
| PRDs | prompts/prds/ |
Product requirements documents |
| Prototyping | prompts/prototyping/ |
UI prototype specs |
| Release Notes | prompts/release-notes-generator/ |
Release notes from git history |
| Stakeholder Management | prompts/stakeholder-management/ |
RACI, DACI, stakeholder mapping |
| Strategy | prompts/strategy/ |
Product strategy canvas, team OKRs |
| Synthetic Users | prompts/synthetic-users/ |
AI-generated personas and interviews |
| Trend Analysis | prompts/trend-analysis/ |
Feedback and industry trend monitoring |
| User Journey Maps | prompts/user-journey-maps/ |
End-to-end journey mapping |
When the user asks for PM help
- Match their request to the right category and prompt file
- Read the prompt file with the Read tool
- Ask for any missing placeholder values you cannot infer
- Return the completed prompt with all placeholders filled
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 · 52 lines · 0 tokens per session scan A 8fd69cd4fcb6
pm-prompts is a cursor rule published in the GitHub repository kkanakas/AI-Prompts-for-Product-Management (3 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 652 tokens. 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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