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 analytical-pmgit 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/analytical-pm)<a href="https://agentmods.dev/skills/aroyburman-codes/pm-skills/analytical-pm"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/analytical-pm/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/aroyburman-codes/pm-skills/analytical-pm"><img src="https://agentmods.dev/badge/skills/aroyburman-codes/pm-skills/analytical-pm.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 | $0.00034 | $0.01773 |
| Opus 5 | $0.00017 | $0.00886 |
| Sonnet 5 | $0.00007 | $0.00355 |
| Haiku 4.5 | $0.00003 | $0.00177 |
Grade A, and why
analytical-pm 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 10d 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytical PM Skill
Apply a structured framework to PM analytical, metrics, root-cause, and trade-off questions targeting AI product roles.
When to Use
- User asks "What metrics would you use for X"
- User asks "How would you measure success for X"
- User asks "Metric X dropped 20%, diagnose it"
- User asks about trade-offs between two product decisions
- User asks "Define a North Star metric for X"
- User says
/analytical-pmfollowed by a question - Any question about metrics, goals, root-cause analysis, A/B tests, or trade-offs
Context
- Tuned for: AI product roles at frontier AI companies
- What matters: Translating product intuition into measurable outcomes and debugging complex systems with data
- Common pitfall: Picking vanity metrics or being too qualitative. Be rigorous and quantitative.
Three Question Types
TYPE A: Metrics / Goal-Setting Questions
"Define success metrics for X" / "What would you measure for X" / "Set goals for X"
Framework: Analytical (6 Steps)
Step 1: Clarify the Product
- What is the product? Who uses it? What value does it deliver?
- What stage is it in? (launch, growth, mature, declining)
- What's the business model? (subscription, API usage, freemium, enterprise)
Step 2: Define the North Star Metric (NSM)
The NSM must capture the core value exchange between product and user.
- Formula: NSM = [engagement unit] per [user segment] per [time period]
- Example (ChatGPT): # of successful conversations per weekly active user
- Example (LLM API platform): # of API calls generating production value per monthly active developer
- Example (Claude): # of tasks completed per weekly active user
Decompose the NSM into a metric tree:
NSM = Factor A x Factor B x Factor C
Step 3: Supporting Metrics (3-5)
Leading indicators that the NSM will grow. Organized by AARRR:
- Acquisition: New users/developers, sign-up conversion
- Activation: First successful use, time-to-value
- Retention: D7/D30 retention, usage frequency
- Revenue: ARPU, conversion to paid, API spend
- Referral: Organic invites, word-of-mouth, virality coefficient
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.
- 10d ago First seen · 178 lines · 34 tokens per session scan A 1ad90f49cd94
analytical-pm is a skill published in the GitHub repository aroyburman-codes/pm-skills (25 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 1,773 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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