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 nimrodfisher/data-analytics-skills --skill segmentation-analysisgit clone --depth 1 https://github.com/nimrodfisher/data-analytics-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/nimrodfisher/data-analytics-skills/segmentation-analysis)<a href="https://agentmods.dev/skills/nimrodfisher/data-analytics-skills/segmentation-analysis"><img src="https://agentmods.dev/badge/skills/nimrodfisher/data-analytics-skills/segmentation-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/nimrodfisher/data-analytics-skills/segmentation-analysis"><img src="https://agentmods.dev/badge/skills/nimrodfisher/data-analytics-skills/segmentation-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 | $0.00033 | $0.00569 |
| Opus 5.5 | $0.00013 | $0.00228 |
| Sonnet 5.5 | $0.00007 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
segmentation-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 1mo 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 — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Segmentation Analysis
When to use
- The team needs to understand who the best customers are and what distinguishes them
- Marketing wants distinct groups to target with different messages or offers
- Product needs to prioritise features based on high-value user behaviour patterns
- Churn is high and the team needs to identify at-risk users before they leave
- An existing segmentation feels arbitrary and needs data validation or improvement
Process
- Define the segmentation goal — clarify what decisions the segments will inform (product roadmap, marketing campaigns, retention programs). The goal determines which variables matter and how many segments are useful (typically 3–7). See
references/segmentation_approaches.md. - Select and prepare variables — choose 3–7 attributes or behaviours that vary across users and relate to the business outcome. Handle missing values and scale continuous variables. Remove outliers only if they would distort cluster centroids.
- Run the segmentation — for data-driven segmentation, use k-means clustering via
scripts/segmentation_runner.py. For rule-based segmentation, apply the business logic rules and validate that segments are distinct and non-overlapping. - Profile each segment — compute the mean and median for each variable by segment, expressed as % above/below the overall average. Identify the 2–3 defining characteristics of each segment and assign a descriptive name.
- Validate and interpret — confirm segments are meaningfully different (silhouette score > 0.3 for clustering) and make business sense. Sanity-check by asking whether you would actually treat each segment differently.
- Map to strategy and report — assign each segment to a recommended strategy (Retain & Expand, Monetise, Activate, Win-Back, Sunset). Produce
assets/segment_profile_template.mdwith the profiles and strategic priorities.
Inputs the skill needs
- User-level data with attributes (demographics, plan type) and behavioural metrics (sessions, revenue, feature usage, recency)
- Business goal the segmentation will serve
- Any existing segmentation to validate or replace
- Minimum of ~100 users per expected segment for clustering to be meaningful
What ships with it
3 files 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.
- 1mo ago First seen · 33 lines · 33 tokens per session scan A 00432265021c
segmentation-analysis is a skill published in the GitHub repository nimrodfisher/data-analytics-skills (485 stars, last pushed 25d ago), licensed MIT. It adds 33 tokens to every session and 569 once invoked, about $0.0001 per session on Opus 5.5. 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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