agentmods.dev

segmentation

segmentation is a skill for Claude Code, Codex from Ken-Technology/cold-email-skills. It costs 56 tokens per session (1,703 once invoked), scanned A, original, MIT.

A campaign-planning skill that writes AI instructions for dividing prospects into separate campaign groups. It creates a general audience description and criteria for each segment.

In plain words
What is it for?
Use it when setting up AI segmentation for a cold-email campaign. It helps define mutually exclusive audience groups, their criteria, and a default catch-all group.
Why use it?
It removes the need to manually decide how prospects should be routed and document the rules for each group. It also gives campaign tooling a consistent structure for reading the segments.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 4repo changed 9d ago A scan 56 tokens original MIT

Good fit Use it when setting up AI segmentation for a cold-email campaign. It helps define mutually exclusive audience groups, their criteria, and a default catch-all group.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ken-technology/cold-email-skills/segmentation
View source ↗ Ken-Technology/cold-email-skills ken.so
Install

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.

Any agent
npx skills add Ken-Technology/cold-email-skills --skill segmentation
Clone the repo
git clone --depth 1 https://github.com/Ken-Technology/cold-email-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for segmentation

README.md
[![agentmods](https://agentmods.dev/badge/skills/ken-technology/cold-email-skills/segmentation/github.svg)](https://agentmods.dev/skills/ken-technology/cold-email-skills/segmentation)
Your own site
<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/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.

agentmods 80×15 button for segmentation

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,703 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.01703
Opus 5.5 $0.00022 $0.00681
Sonnet 5.5 $0.00011 $0.00341
Haiku 4.5 $0.00006 $0.00170

Measured 9d ago against content hash 5a8f370988ad, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

segmentation/SKILL.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Segmentation Skill

Split a campaign's qualified leads into the plan's segments so each group gets the right messaging. You write the segment descriptions the assigner reads, plus the Default segment's description.

You write them to segmentation.md in the campaign workspace (./cold-email/{slug}/) and create one folder per segment. The user runs the AI segments' descriptions as prompts in any AI tool (a chat, an AI column in Clay or a spreadsheet, one lead at a time) and the manual segments as hard filters in their list tool. Nothing is written to any platform.

Inputs

The plan's segments (name, sub-ICP, angle), the qualification audience description, and the applied search filters and qualification criteria (so you repeat neither). Read them from the campaign workspace:

  1. plan.md - the segments, their angles and the segmentation mode.
  2. qualification.md, when it exists - the audience description to reuse, and the criteria not to repeat.
  3. search-strategy.md - the filters already applied. Never restate any of them in a segment.
  4. research.md - the client and the ICP.

When the skill runs on its own, without a workspace, take the same inputs from the user.

Pick the Mode

Mode In segmentation.md Use when
Manual manual The split is a hard filter: industry, seniority, function, geo, company type, email validity. The user's list tool applies it for free. Prefer it.
AI ai (the default) The split needs judgment from profile or company text (e.g. product company vs services hybrid). One AI call per lead.
A/B arms percentage A sequence test: random split by weight, no criteria.

Never mix A/B arms with criteria segments in one campaign.

The Default Segment

  • Every campaign has a Default segment, and its folder is always 0 - default/ (literal, no variation). Never rename it.
  • Default catches every qualified lead that fits no other segment, plus any lead the assigner is not confident about.
  • Its description is the short general audience description: persona + org type only (e.g. "Financial advisors at independent advisory practices"). No criteria, filters, headcount, geography, value prop, or "doesn't fit the above" wording.
  • Default gets its own sequence like any other segment. Leads that fall through still receive email.

Read the full file on GitHub · 104 lines

Changes

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.

  1. 9d ago Changed · -53 lines 5a8f370988ad
  2. 2mo ago First seen · 157 lines · 56 tokens per session scan A 329f6d70753d

Subscribe to this mod's changes

segmentation is a skill published in the GitHub repository Ken-Technology/cold-email-skills (4 stars, last pushed 6d ago), licensed MIT. It adds 56 tokens to every session and 1,703 once invoked, about $0.0002 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 2026-08-31.

Claude CodeMarketing and SEOagent-skillsai-agentscodexcold-emailoutbound
Related

segmentation compared with skills from other repositories

segmentation next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
segmentationKen-Technology/cold-email-skillsSkill46d agoMITA56Claude Code, Codex
cold-emailFrontal-so/outbound-skillsSkill91mo agoMITA177Claude Code, Codex
list-buildingFrontal-so/outbound-skillsSkill91mo agoMITA190Claude Code, Codex
claygentFrontal-so/outbound-skillsSkill91mo agoMITA131Claude Code
conditional-logicFrontal-so/outbound-skillsSkill91mo agoMITA128Claude Code
people-enrichmentFrontal-so/outbound-skillsSkill91mo agoMITA110Claude Code
scoringFrontal-so/outbound-skillsSkill91mo agoMITA113Claude Code