personalization-at-scale

personalization-at-scale is a skill for Claude Code, Codex from OneWave-AI/claude-skills. It costs 46 tokens per session (527 once invoked), scanned A, original, MIT.

A research and writing tool that creates tailored opening lines for large lists of sales prospects using company news, LinkedIn activity, shared contacts, and other available signals.

In plain words
What is it for?
Use it to prepare personalized first lines for email or social outreach campaigns from a CSV or pasted prospect list.
Why use it?
It reduces the manual research needed to make high-volume cold outreach specific to each recipient.

Skill for Claude CodeCodex

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

Good fit Use it to prepare personalized first lines for email or social outreach campaigns from a CSV or pasted prospect list.

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Install with agentmods
npx agentmods add skills/onewave-ai/claude-skills/personalization-at-scale
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 OneWave-AI/claude-skills --skill personalization-at-scale
Clone the repo
git clone --depth 1 https://github.com/OneWave-AI/claude-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 personalization-at-scale

README.md
[![agentmods](https://agentmods.dev/badge/skills/onewave-ai/claude-skills/personalization-at-scale/github.svg)](https://agentmods.dev/skills/onewave-ai/claude-skills/personalization-at-scale)
Your own site
<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/personalization-at-scale"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/personalization-at-scale/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 personalization-at-scale

Your own site · 80×15
<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/personalization-at-scale"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/personalization-at-scale.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 527 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00046 $0.00527
Opus 5 $0.00023 $0.00264
Sonnet 5 $0.00009 $0.00105
Haiku 4.5 $0.00005 $0.00053

Measured 9d ago against content hash 87b486b9be15, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

personalization-at-scale 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.

personalization-at-scale/SKILL.md · 38 lines

How it starts

The opening of the file, as written. The whole thing — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Personalization at Scale

Generate hundreds of unique, researched first lines in minutes instead of hours, making cold outreach feel warm.

Contents

  • references/research-sources.md - signal sources, personalization styles, quality standards
  • references/patterns-by-type.md - sample first lines and tables for each angle (congrats, observation, mutual connection, company news, hiring, tech stack, thought leadership, shared background)
  • references/fallbacks.md - role/stage/industry/competitor lines for prospects with no angle
  • references/output-template.md - full campaign deliverable structure
  • references/benchmarks.md - expected lift, A/B reference data, pro tips (do/don't)
  • references/example-campaigns.md - worked campaign examples by persona

Workflow

  1. Ingest the prospect list (CSV or pasted). Require First Name, Last Name, Title, Company; use LinkedIn URL, email, website, industry, size, and location when available.

  2. Confirm preferences: which personalization styles to prioritize (1-3), tone (professional, casual, direct, consultative), and any exclusions (recency cutoff, personal topics, sensitive subjects).

  3. Research each prospect across the sources in references/research-sources.md. Identify the strongest, most recent, verifiable angle per prospect.

  4. Match each prospect to its angle and draft from the matching pattern in references/patterns-by-type.md. For prospects with no angle, draft from references/fallbacks.md.

  5. Generate 2-3 first-line options per prospect, each with a confidence score (High/Medium/Low) and notes on alternative angles. Follow the structure in references/output-template.md.

  6. Quality-check the first 10 manually. Confirm each line is specific, recent, relevant, natural, and verifiable before scaling the batch.

  7. Export in the requested format: CSV with personalization columns, merge fields for the outreach tool (Outreach, Salesloft), individual drafts, or copy-paste blocks.

  8. Track response rates by personalization type and refresh personalizations every 30 days as activity changes.

Read the full file on GitHub · 38 lines

Files

What ships with it

6 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.

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 First seen · 38 lines · 46 tokens per session scan A 87b486b9be15

Subscribe to this mod's changes

personalization-at-scale is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 527 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 2026-09-03.

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