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 skills add swan-gtm/gtm-skills --skill personalization-6-bucketsgit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/personalization-6-buckets)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/personalization-6-buckets"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/personalization-6-buckets/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/swan-gtm/gtm-skills/personalization-6-buckets"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/personalization-6-buckets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01013 |
| Opus 5.5 | $0.00022 | $0.00405 |
| Sonnet 5.5 | $0.00011 | $0.00203 |
| Haiku 4.5 | $0.00006 | $0.00101 |
Grade A, and why
personalization-6-buckets 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- personalization-6-buckets — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
6 Buckets of Personalization
Bucket 1: Self-Authored Content (Highest Value)
Content the prospect has created themselves:
- Speaking Engagements - Conferences, podcasts
- Webinars - Hosted or participated
- Articles - Blog posts, publications
- Posts - LinkedIn, Twitter content
Usage: Highest value personalization. Reference their thought leadership directly.
Bucket 2: Engaged Content
Content the prospect has interacted with:
- Commented On - Their comments on posts
- Shared - Content they've shared
- Liked Comments - Comments they've liked
- Liked Posts - Posts they've liked
Usage: Shows what topics interest them. Reference shared interests.
Bucket 3: Self-Identified Traits
How the prospect describes themselves:
- Profile Line ("About me" Section) - Bio content
- Company Line (Role, Specialization & Achievements) - Role description
- Headline (Below Profile Picture) - LinkedIn headline
Usage: Use their own words to frame relevance.
Bucket 4: Junk Drawer
Personal details from their profile:
- Personal Interests - Hobbies, activities
- Volunteer Experience: Personal (Charity) - Causes they support
- Languages Spoken - Multilingual abilities
- Schools Attended - Education background
- Interested In/Following - Topics they follow
Usage: Build rapport but don't overdo it. Use sparingly.
Bucket 5: Background Centric
Professional history and credentials:
- Tenure at Company - How long they've been there
- Professional Trajectory - Career movement
- Recommendations Given - Who they recommend
- Recommendations Received - Social proof
- Boards They're On - Board positions
- Volunteer Experience: Professional (Mentorship) - Industry involvement
- Awards Received - Recognition
- Certifications - Professional credentials
- Mutual Connections - Shared network
- Skill Endorsements - Endorsed abilities
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.
- 1mo ago First seen · 144 lines · 56 tokens per session scan A 821efc15a2d7
personalization-6-buckets is a skill published in the GitHub repository swan-gtm/gtm-skills (220 stars, last pushed 7d ago), licensed MIT. It adds 56 tokens to every session and 1,013 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 2026-08-30.
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