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 daffy0208/ai-dev-standards --skill product-analystgit clone --depth 1 https://github.com/daffy0208/ai-dev-standardsWrote 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/daffy0208/ai-dev-standards/product-analyst)<a href="https://agentmods.dev/skills/daffy0208/ai-dev-standards/product-analyst"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/product-analyst/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/daffy0208/ai-dev-standards/product-analyst"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/product-analyst.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 | $0.00052 | $0.03307 |
| Opus 5 | $0.00026 | $0.01654 |
| Sonnet 5 | $0.00010 | $0.00661 |
| Haiku 4.5 | $0.00005 | $0.00331 |
Grade A, and why
product-analyst 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 — 511 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Analyst
Measure user behavior and product health to inform data-driven decisions.
Core Principle
What gets measured gets improved. Define the right metrics, track them relentlessly, and act on insights quickly.
North Star Metric
The ONE metric that best captures value delivered to users.
Your North Star should:
- ✅ Represent real customer value
- ✅ Correlate with revenue
- ✅ Be measurable frequently (daily/weekly)
- ✅ Rally the entire team around one goal
Examples by Product Type:
Communication:
Slack: Messages Sent (weekly active)
Zoom: Weekly Meeting Minutes
Discord: Active Servers
Marketplace:
Airbnb: Nights Booked
Uber: Completed Rides
Etsy: Gross Merchandise Value (GMV)
Media/Content:
Spotify: Time Listening
Netflix: Hours Watched
Medium: Total Time Reading
SaaS/B2B:
Asana: Weekly Active Teams
Notion: Collaborative Documents
Salesforce: Deals Closed (CRM value)
Social:
Facebook: Daily Active Users (DAU)
Instagram: Posts Shared
Twitter: Tweets per User
How to choose your North Star:
- What action represents core value?
- If users do this more, do they get more value?
- Does this predict revenue?
- Can the entire team influence it?
Key Metrics by Category
Acquisition Metrics
Goal: Get users into the product
Traffic Sources:
- Organic Search: SEO traffic
- Paid Ads: Google Ads, Facebook Ads
- Referral: Word of mouth, links
- Direct: Typed URL, bookmarked
- Social: Twitter, LinkedIn posts
Key Metrics:
- Unique Visitors: Total website visitors
- Sign-ups: Users who created account
- Conversion Rate: Visitors → Sign-ups
- Cost Per Acquisition (CPA): Ad spend / sign-ups
- Source Quality: Which sources convert best?
Targets:
- Visitor → Sign-up: 2-5% (good), 5-10% (excellent)
- CPA: < $50 (B2C), < $200 (B2B), depends on LTV
Activation Metrics
Goal: Get users to "aha moment"
Activation Definition:
- User completes onboarding
- User takes first core action
- User experiences product value
Examples:
Slack: Sent 2,000 messages (team is active)
Dropbox: Added file to folder
Twitter: Followed 30 accounts
Airbnb: Completed first booking
Key Metrics:
- Activation Rate: Sign-ups → Activated
- Time to Activation: How long to aha moment?
- Onboarding Completion: % who finish setup
Targets:
- Activation Rate: >40% (good), >60% (excellent)
- Time to Activation: <24 hours (ideal)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 511 lines · 52 tokens per session scan A f6844d32665f
product-analyst is a skill published in the GitHub repository daffy0208/ai-dev-standards (36 stars, last pushed 8mo ago), licensed MIT. It adds 52 tokens to every session and 3,307 once invoked, about $0.0003 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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