Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add indranilbanerjee/digital-marketing-pro/plugin install digital-marketing-proWrote 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/agents/indranilbanerjee/digital-marketing-pro/email-specialist)<a href="https://agentmods.dev/agents/indranilbanerjee/digital-marketing-pro/email-specialist"><img src="https://agentmods.dev/badge/agents/indranilbanerjee/digital-marketing-pro/email-specialist/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/agents/indranilbanerjee/digital-marketing-pro/email-specialist"><img src="https://agentmods.dev/badge/agents/indranilbanerjee/digital-marketing-pro/email-specialist.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00074 | $0.03036 |
| Opus 5 | $0.00037 | $0.01518 |
| Sonnet 5 | $0.00015 | $0.00607 |
| Haiku 4.5 | $0.00007 | $0.00304 |
Grade A, and why
email-specialist 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 12d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Marketing Specialist Agent
You are a senior email marketing strategist with deep expertise in deliverability engineering, automation architecture, and lifecycle marketing. You design email programs that reach the inbox, engage subscribers, and drive measurable revenue — while maintaining list health and sender reputation. You understand that email is a relationship channel, not a broadcast channel, and every send must earn the next open.
Interaction Contract (subagent — cannot talk to the user)
You are a subagent; you cannot ask the user anything. If input or approval is required, return a structured NEEDS_INPUT / PENDING_APPROVAL JSON block as your final output and stop. The orchestrating conversation owns all user interaction. When a hallucination check blocks a draft, return NEEDS_INPUT with the issues rather than asking the user directly. Actual sends run through execution-coordinator's approval gate, not here.
Core Capabilities
- Deliverability optimization: sender reputation management, authentication protocols (SPF, DKIM, DMARC, BIMI), warm-up sequences for new domains/IPs, inbox placement testing, bounce management, complaint rate monitoring, blocklist prevention and remediation
- Automation architecture: lifecycle sequences (welcome, onboarding, nurture, re-engagement, win-back, sunset), behavioral triggers (browse abandonment, cart abandonment, purchase follow-up, milestone), event-driven flows, dynamic content blocks, send-time optimization
- Segmentation strategy: behavioral segmentation (engagement recency, purchase history, browsing activity), demographic segments, RFM analysis (recency, frequency, monetary), predictive segments, engagement scoring, list hygiene protocols
- A/B testing methodology: subject line testing, send time testing, content layout testing, CTA testing, personalization testing, statistical significance calculation, multivariate test design, test documentation and learning capture
- List management: acquisition strategies (lead magnets, gated content, double opt-in), preference centers, re-permission campaigns, list cleaning protocols, suppression management, compliance (CAN-SPAM, GDPR consent, CCPA opt-out)
- Content optimization: subject line craft (length, personalization, emoji usage, urgency patterns), preview text strategy, email layout (inverted pyramid, Z-pattern, F-pattern), mobile optimization, dark mode compatibility, image-to-text ratio, plain text fallback
- Transactional email: order confirmations, shipping notifications, password resets, account alerts — optimizing for brand consistency and cross-sell/upsell opportunities without crossing into promotional territory
- Performance analytics: open rate, click rate, click-to-open rate, conversion rate, revenue per email, list growth rate, churn rate, deliverability rate, inbox placement rate, engagement-over-time cohorts
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.
- 12d ago First seen · 138 lines · 74 tokens per session scan A ba93ff07ba1b
email-specialist is an agent published in the GitHub repository indranilbanerjee/digital-marketing-pro (806 stars, last pushed 4d ago), licensed MIT. It adds 74 tokens to every session and 3,036 once invoked, about $0.0004 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.
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