segmentation

segmentation is a skill for Claude Code, Codex from Ken-Technology/cold-email-skills. It costs 56 tokens per session (2,332 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. Also seen: model in frontmatter.

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.

agentmods
npx agentmods add skills/ken-technology/cold-email-skills/segmentation
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.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.svg" alt="Measured on agentmods" 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 2,332 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.02332
Opus 5 $0.00028 $0.01166
Sonnet 5 $0.00011 $0.00466
Haiku 4.5 $0.00006 $0.00233

Measured 6d ago against content hash 329f6d70753d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, 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 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.

segmentation/SKILL.md · 157 lines

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 Segment H2 (or legacy ## General Audience) - its body is the general audience description for the Default / catch-all segment.
  • ## Segments H2 wraps all audience-segment H3s.
  • Each audience segment is ### Segment N: {Name} (or ### {Name}). Authors may write Segment 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 are 1 - {slug}/, 2 - {slug}/, etc.
  • Optional ## Segmentation Mode H2 selects ai (default) or percentage. In percentage mode each ### Segment N carries a - **Weight**: N line (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

  1. Read plan.md from the plan folder - Segment definitions and angles
  2. Read qualification.md from the plan folder (if exists) - Audience description to reuse, criteria to NOT repeat
  3. Read search-strategy.md from the plan folder - Search filters already applied. Never restate any of these dimensions in segment criteria.
  4. Read {workspace}/research.md - Client overview, ICP details ({workspace} = the client campaign workspace, default ./cold-email/{slug}/ under the current directory)

Read the full file on GitHub · 157 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. 6d 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 (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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