Borrowing it
Nothing to install: this file belongs to LeanOS-Technologies/strategy-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/LeanOS-Technologies/strategy-os/main/.claude/skills/stg-segmenting-customers/SKILL.mdgit clone --depth 1 https://github.com/LeanOS-Technologies/strategy-osWrote 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/leanos-technologies/strategy-os/stg-segmenting-customers)<a href="https://agentmods.dev/skills/leanos-technologies/strategy-os/stg-segmenting-customers"><img src="https://agentmods.dev/badge/skills/leanos-technologies/strategy-os/stg-segmenting-customers/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/leanos-technologies/strategy-os/stg-segmenting-customers"><img src="https://agentmods.dev/badge/skills/leanos-technologies/strategy-os/stg-segmenting-customers.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 | $0.00037 | $0.01834 |
| Opus 5 | $0.00018 | $0.00917 |
| Sonnet 5 | $0.00007 | $0.00367 |
| Haiku 4.5 | $0.00004 | $0.00183 |
Grade A, and why
stg-segmenting-customers 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 9d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Segmentation
Generate segment hypothesis using compression model: enumerate candidates, score, eliminate, carry alternatives. All segments defined by observable filters with tier-labeled evidence.
Procedure
Step 1: Load Context [S]
Read: market sizing notes (TAM/SAM/SOM), governor problem space description, research signals from BUILD phase 1.
Produce: segment generation parameters (industry focus, market scope, problem type).
Gate: context_loaded: bool -- market data and problem space available.
- Pass: Step 2.
- Fail: Report which inputs are missing. If market sizing is unavailable, proceed with governor input and research signals, noting reduced confidence.
Step 2: Enumerate Candidate Segments (Minimum 3) [K-grounded]
Grounded in: market data, problem space, research signals.
From market research, identify 3-5 potential customer groups. For each:
- Who they are (role, company type, industry)
- Why they might have the problem (structural reason)
- Rough size from TAM/SAM data
WebSearch for segment-specific signals if needed (job postings, forum activity, tool adoption patterns).
Produce: candidate segment list with initial evidence.
Gate: candidates_enumerated: bool -- at least 3 candidates identified, each with structural rationale for why they might have the problem.
- Pass: Step 3.
- Fail: If fewer than 3 candidates, broaden scope (adjacent industries, different company sizes, different roles). If still <3, document why the market may be narrower than expected.
Step 3: Define Observable Filters for Each Candidate [R]
For each segment, identify 2-4 searchable criteria.
| Valid Filters (Observable) | Invalid Filters (Reject) |
|---|---|
| Company size (employees, revenue) | "Innovative companies" |
| Industry (NAICS code, vertical) | "Growth-minded founders" |
| Technology used (specific tools, platforms) | "Tech-savvy teams" |
| Geography (region, country) | "Forward-thinking" |
| Behavioral signals (job postings, tool usage) | Any psychographic or subjective filter |
| Funding stage (from Crunchbase) | "Quality-focused" |
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.
- 9d ago First seen · 161 lines · 37 tokens per session scan A 8bfecdc76c04
stg-segmenting-customers is a skill published in the GitHub repository LeanOS-Technologies/strategy-os (37 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 1,834 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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