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 jbalbu01/sales-enablement-plugin --skill rep-profilegit clone --depth 1 https://github.com/jbalbu01/sales-enablement-pluginWrote 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/jbalbu01/sales-enablement-plugin/rep-profile)<a href="https://agentmods.dev/skills/jbalbu01/sales-enablement-plugin/rep-profile"><img src="https://agentmods.dev/badge/skills/jbalbu01/sales-enablement-plugin/rep-profile.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 | $0.00121 | $0.02760 |
| Opus 5 | $0.00060 | $0.01380 |
| Sonnet 5 | $0.00024 | $0.00552 |
| Haiku 4.5 | $0.00012 | $0.00276 |
Grade A, and why
rep-profile 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 8d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rep Profile
Makes every interaction feel like it was designed specifically for this rep. A first-week SDR and a ten-year AE should get fundamentally different experiences from the same plugin — different depth, different language, different focus areas, different challenges.
Why This Matters
"Hyper-personalized learning" isn't about adding a name to a template. It means:
- A rep who crushes discovery but struggles with closing gets coaching focused on negotiation
- A rep who just joined gets scaffolded frameworks; a veteran gets contextual nudges
- A rep who learns by doing gets role-play practice; one who learns by studying gets frameworks and examples
- Content complexity scales with the rep's experience and comfort level
How It Works
┌─────────────────────────────────────────────────────────────────┐
│ REP PROFILE │
├─────────────────────────────────────────────────────────────────┤
│ PROFILE COMPONENTS │
│ • Skill assessment (scored competencies) │
│ • Experience level (tenure, deals closed, ramp stage) │
│ • Deal patterns (what they win, what they lose, why) │
│ • Learning style (doing, studying, observing, discussing) │
│ • Development plan (current focus areas and progress) │
│ • Interaction history (what help they've asked for before) │
├─────────────────────────────────────────────────────────────────┤
│ ADAPTATION RULES │
│ New rep → More structure, more scaffolding, explicit frameworks │
│ Mid-level → Balanced guidance, focus on weak spots │
│ Senior rep → Brief nudges, advanced scenarios, edge cases │
│ Manager → Coaching lens, team patterns, data-driven insights │
├─────────────────────────────────────────────────────────────────┤
│ SUPERCHARGED (when you connect your tools) │
│ + ~~CRM: Deal history, win rates, cycle lengths, quota data │
│ + ~~CRM: Stage-specific patterns and performance vs team avg │
│ + ~~conversation intelligence (Gong): Talk-to-listen ratios │
│ + ~~conversation intelligence (Gong): Questions per call │
│ + ~~conversation intelligence (Gong): Competitor handling skill │
│ + ~~conversation intelligence (Gong): Next steps discipline │
│ + ~~data enrichment (LinkedIn): Career history and expertise │
│ + ~~data enrichment (ZoomInfo): Industry vertical experience │
│ + ~~chat: Coaching conversations and peer feedback │
└─────────────────────────────────────────────────────────────────┘
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.
- 8d ago First seen · 258 lines · 121 tokens per session scan A 79bec06600bc
rep-profile is a skill published in the GitHub repository jbalbu01/sales-enablement-plugin (14 stars, last pushed 6mo ago), licensed MIT. It adds 121 tokens to every session and 2,760 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
learning-and-development
Builds capability — skills gaps, career frameworks, training that transfers to the job, and internal mobility. Use this to design a career ladder, close a capability gap, decide whether to build or hire a skill, structure onboarding into a role, or work out why training keeps failing to change anything.
diagnose
A short question-based assessment of a pilot's current skill level. It produces a profile and saves it in the connected browser or database.
deepline-quickstart
Run a quick Deepline demo recipe to show the user how Deepline works.
lesson-close
A workflow for finishing the day's lesson file, recording its status and duration, then saving it to the personal-guide GitHub repository.
personal-guide-render
Instructions for understanding the status of a personal guide that the platform builds automatically from stored user data. The platform creates daily lessons and rebuilds the full guide weekly on its servers.
review
Use when user asks about their learning progress or wants study guidance. Triggers on "how am I doing", "my progress", "what should I study next", "show my scores", "what are my weak areas", "review my learning", "how well do I know X", or any request to see quiz results, track improvement, or decide what to focus on…