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 jonbishop1/saas-marketing-skills --skill saas-data-analysisgit clone --depth 1 https://github.com/jonbishop1/saas-marketing-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/jonbishop1/saas-marketing-skills/saas-data-analysis)<a href="https://agentmods.dev/skills/jonbishop1/saas-marketing-skills/saas-data-analysis"><img src="https://agentmods.dev/badge/skills/jonbishop1/saas-marketing-skills/saas-data-analysis/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/jonbishop1/saas-marketing-skills/saas-data-analysis"><img src="https://agentmods.dev/badge/skills/jonbishop1/saas-marketing-skills/saas-data-analysis.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 | $0.00189 | $0.05601 |
| Opus 5 | $0.00095 | $0.02801 |
| Sonnet 5 | $0.00038 | $0.01120 |
| Haiku 4.5 | $0.00019 | $0.00560 |
Grade A, and why
saas-data-analysis 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 11d 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 — 415 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SaaS Product & Marketing Analytics
Audience: developers and technical founders. Explain things in engineering terms. Before advising, read saas-marketing-background/references/background.md and gather missing background. Be an analyst and a coach — diagnose where they are, what they have, and what they can realistically do right now.
Table of Contents
- Why This Matters
- Situation Assessment
- Three Tiers of Analytics
- Marketing Analytics
- Product Analytics
- Connecting Marketing to Product
- Key Metrics Framework
- Session Recording — Light Touch
- Analytics by Product Stage
- Reference Files
Why Analytics Matter
Most technical founders either track nothing or track everything. Both fail.
Tracking nothing means you're guessing — about which channels produce real customers, about where users drop off, about what features actually drive retention. Tracking everything means you drown in dashboards, spend time maintaining instrumentation instead of shipping product, and still can't answer the one question that matters: "Is what I'm doing working?"
Good analytics answer specific questions. The system exists to make decisions, not to produce charts.
The marketing-to-product connection most founders miss: Marketing analytics (GA4, UTMs, ad platforms) tell you where users come from. Product analytics (Mixpanel, Amplitude, PostHog) tell you what users do after they arrive. Most founders set these up as separate systems and never connect them. This means marketing optimizes for signups while product optimizes for engagement, and nobody knows which acquisition channels produce customers who actually stick around and pay. Connecting these two systems is one of the highest-leverage analytics investments you can make.
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.
- 11d ago First seen · 415 lines · 189 tokens per session scan A d49b6d420c5c
saas-data-analysis is a skill published in the GitHub repository jonbishop1/saas-marketing-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 189 tokens to every session and 5,601 once invoked, about $0.0009 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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