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 agentmods add commands/jbalbu01/sales-enablement-plugin/rep-dashboardgit 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/commands/jbalbu01/sales-enablement-plugin/rep-dashboard)<a href="https://agentmods.dev/commands/jbalbu01/sales-enablement-plugin/rep-dashboard"><img src="https://agentmods.dev/badge/commands/jbalbu01/sales-enablement-plugin/rep-dashboard.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 | $0.00025 | $0.02155 |
| Opus 5 | $0.00013 | $0.01077 |
| Sonnet 5 | $0.00005 | $0.00431 |
| Haiku 4.5 | $0.00003 | $0.00215 |
Grade A, and why
rep-dashboard 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 5d 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/rep-dashboard
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Get a personalized performance dashboard for any rep or yourself. Combines pipeline data, skill assessments, deal outcomes, and coaching history into a single view with specific development recommendations.
Usage
/rep-dashboard
/rep-dashboard Sarah
/rep-dashboard my dashboard
Then provide the rep's recent data, or let me pull from connected tools.
How It Works
┌─────────────────────────────────────────────────────────────────┐
│ REP DASHBOARD │
├─────────────────────────────────────────────────────────────────┤
│ STANDALONE (always works) │
│ ✓ Performance metrics summary (pipeline, win rate, velocity) │
│ ✓ Skill assessment across core selling competencies │
│ ✓ Deal pattern analysis (what works, what doesn't) │
│ ✓ Coaching priorities ranked by impact │
│ ✓ Personalized development plan (30/60/90) │
│ ✓ Peer benchmarking (anonymous team comparison) │
├─────────────────────────────────────────────────────────────────┤
│ SUPERCHARGED (when you connect your tools) │
│ + CRM: Pull pipeline, activity, and deal history │
│ + Transcription: Analyze call quality and technique │
│ + Calendar: Meeting patterns and prospect engagement │
│ + Chat: Communication patterns and collaboration │
├─────────────────────────────────────────────────────────────────┤
│ PERSONALIZATION (adapts to the rep) │
│ → Reads rep-profile for skill scores and learning style │
│ → Adapts recommendations based on experience level │
│ → Tracks improvement over time against previous dashboards │
│ → New reps get onboarding-focused view │
│ → Experienced reps get mastery-focused view │
│ → Managers get team rollup with individual drill-downs │
└─────────────────────────────────────────────────────────────────┘
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.
- 5d ago First seen · 238 lines · 25 tokens per session scan A 86119a0f84cb
rep-dashboard is a command published in the GitHub repository jbalbu01/sales-enablement-plugin (14 stars, last pushed 6mo ago), licensed MIT. It adds 25 tokens to every session and 2,155 once invoked, about $0.0001 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 commands, from other repositories
churn-prevention
Win/loss interview + churn analysis + retention playbook. Focuses on retention (not acquisition) — where 80% of the current plugin is focused. For B2B SaaS with churn > 5%/month, consulting with renewal rate < 70%, or a recurring business on a plateau.
audit
Offer diagnostic via the Value Equation — scores 1-10 on each vector (Dream Outcome, Probability, Time Delay, Effort), pinpoints the critical bottleneck, proposes the top 3 concrete levers, and rewrites the offer in 1 paragraph. Recommended prerequisite before LP and script.
client-onboarding
The client's first 30 days after the sale. A structured touch cadence, early quick wins to validate the purchase decision, visible value milestones. Cuts early churn (< 30 days) by a typical 40-60%. For consulting, agencies, cohort programs, mid-market B2B SaaS.
Email sequence (cold, warm, nurture, re-engagement). Generates 5-7 sequential emails with hook + agitation + proof + CTA, timing between touches, and context per touch. Tuned for B2B cold outreach — direct voice, no superlatives, specific proof.
help
Interactive decision matrix. Answer 3 questions and get the right command recommended. Useful for onboarding a new client or when you don't know where to start a session. Covers all 17 plugin commands.
lp
Creates or refines a long-form sales landing page (2000-3500 words). Uses Grand Slam Offer, Value Equation, bonus stacking, scarcity/urgency, guarantees. Humanizer full mode mandatory. Soft warning if there's no recent audit.