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 aroyburman-codes/pm-skills --skill pm-case-studygit clone --depth 1 https://github.com/aroyburman-codes/pm-skillsWrote 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/aroyburman-codes/pm-skills/pm-case-study)<a href="https://agentmods.dev/skills/aroyburman-codes/pm-skills/pm-case-study"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/pm-case-study.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.01174 |
| Opus 5 | $0.00023 | $0.00587 |
| Sonnet 5 | $0.00009 | $0.00235 |
| Haiku 4.5 | $0.00005 | $0.00117 |
Grade A, and why
pm-case-study 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 8d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PM Case Study Skill
Generate a detailed PM case study from a real AI product launch, pivot, or strategic decision — reconstructing the PM thinking behind it.
When to Use
- User asks "Write a case study on [AI product launch/decision]"
- User wants to understand PM decisions behind a real product
- User says
/pm-case-studyfollowed by a topic - Great for: ChatGPT launch, Claude's Constitutional AI, Gemini's multimodal strategy, GitHub Copilot pricing, Perplexity's search bet, Midjourney's Discord-first strategy, etc.
Framework: PM Case Study (8 Sections)
Section 1: Executive Summary
- What happened: One paragraph summary of the product decision/launch
- When: Timeline of key events
- Who: Key people and teams involved
- Outcome: How it played out (success, failure, mixed)
Section 2: Context & Background
- Company situation: Where was the company at this point? Stage, funding, competitive position.
- Market context: What was happening in the broader market?
- Technical context: What capabilities existed? What was newly possible?
- User context: What were users doing before this product? What pain existed?
Section 3: The Decision
- What was decided: Specific product/strategy decision
- Alternatives considered: What other paths were likely on the table?
- Key trade-offs: What did they give up by choosing this path?
- Stakeholder dynamics: Who likely championed this? Who likely opposed it?
Section 4: Execution Analysis
- Go-to-market strategy: How was it launched? Distribution channel?
- Phasing: Was it a big bang launch or phased rollout?
- Pricing: How was it priced? Why that model?
- Technical execution: What was the technical approach? Shortcuts taken?
Section 5: What Went Right
- Identify 3-5 specific decisions that contributed to success
- For each: What was the decision, why it mattered, what would have happened otherwise
- Be specific — reference actual features, timelines, or metrics where available
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.
- 8d ago First seen · 109 lines · 45 tokens per session scan A e16c86d0e8ba
pm-case-study is a skill published in the GitHub repository aroyburman-codes/pm-skills (25 stars, last pushed 6mo ago), licensed MIT. It adds 45 tokens to every session and 1,174 once invoked, about $0.0002 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 2026-08-30.
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