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 agentmods add skills/ken-technology/cold-email-skills/segmentationnpx skills add Ken-Technology/cold-email-skills --skill segmentationgit clone --depth 1 https://github.com/Ken-Technology/cold-email-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/ken-technology/cold-email-skills/segmentation)<a href="https://agentmods.dev/skills/ken-technology/cold-email-skills/segmentation"><img src="https://agentmods.dev/badge/skills/ken-technology/cold-email-skills/segmentation.svg" alt="Measured on agentmods" 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 | $0.00056 | $0.02332 |
| Opus 5 | $0.00028 | $0.01166 |
| Sonnet 5 | $0.00011 | $0.00466 |
| Haiku 4.5 | $0.00006 | $0.00233 |
Grade A, and why
segmentation 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 6d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Segmentation Skill
Write the audience description and segment-specific prompts for AI segmentation. These prompts feed any segmentation step that routes prospects into mutually exclusive audience segments.
Output Contract
segmentation.md must follow a fixed structure so downstream campaign tooling can parse it reliably. Hard rules this skill must honor:
## Default SegmentH2 (or legacy## General Audience) - its body is the general audience description for the Default / catch-all segment.## SegmentsH2 wraps all audience-segment H3s.- Each audience segment is
### Segment N: {Name}(or### {Name}). Authors may writeSegment N:prefix for organization; tooling strips it before slugifying. - The canonical name slug (
re.sub(r"[^a-z0-9]+", "-", name.lower()).strip("-")) MUST match the segment folder's slug. Mismatches warn and leave the segment with an empty description. - Always create the
0 - default/folder alongside the numbered segments. Audience segments are1 - {slug}/,2 - {slug}/, etc. - Optional
## Segmentation ModeH2 selectsai(default) orpercentage. Inpercentagemode each### Segment Ncarries a- **Weight**: Nline (int 1-100) instead of criteria, and so does## Default Segment(the Default is one of the test arms - use it as the baseline/control); weights of ALL arms including the Default must sum to exactly 100, and tooling records a percentage weight per arm. See the Segmentation Modes section below.
Required Context
- Read
plan.mdfrom the plan folder - Segment definitions and angles - Read
qualification.mdfrom the plan folder (if exists) - Audience description to reuse, criteria to NOT repeat - Read
search-strategy.mdfrom the plan folder - Search filters already applied. Never restate any of these dimensions in segment criteria. - Read
{workspace}/research.md- Client overview, ICP details ({workspace}= the client campaign workspace, default./cold-email/{slug}/under the current directory)
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.
- 6d ago First seen · 157 lines · 56 tokens per session scan A 329f6d70753d
segmentation is a skill published in the GitHub repository Ken-Technology/cold-email-skills (3 stars, last pushed 9d ago), licensed MIT. It adds 56 tokens to every session and 2,332 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 2026-08-31.
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