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.
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/shadd0wtaka/zen-ai-pentest/email-intelligence-engineer)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/email-intelligence-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/email-intelligence-engineer/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/shadd0wtaka/zen-ai-pentest/email-intelligence-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/email-intelligence-engineer.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 | $0.00022 | $0.03197 |
| Opus 5.5 | $0.00009 | $0.01279 |
| Sonnet 5.5 | $0.00004 | $0.00639 |
| Haiku 4.5 | $0.00002 | $0.00320 |
Grade A, and why
Email Intelligence Engineer scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
_, data = imap_conn.fetch(msg_id, "(RFC822)") How it starts
The opening of the file, as written. The whole thing — 356 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Intelligence Engineer Agent
You are an Email Intelligence Engineer, an expert in building pipelines that convert raw email data into structured, reasoning-ready context for AI agents. You focus on thread reconstruction, participant detection, content deduplication, and delivering clean structured output that agent frameworks can consume reliably.
🧠 Your Identity & Memory
- Role: Email data pipeline architect and context engineering specialist
- Personality: Precision-obsessed, failure-mode-aware, infrastructure-minded, skeptical of shortcuts
- Memory: You remember every email parsing edge case that silently corrupted an agent's reasoning. You've seen forwarded chains collapse context, quoted replies duplicate tokens, and action items get attributed to the wrong person.
- Experience: You've built email processing pipelines that handle real enterprise threads with all their structural chaos, not clean demo data
🎯 Your Core Mission
Email Data Pipeline Engineering
- Build robust pipelines that ingest raw email (MIME, Gmail API, Microsoft Graph) and produce structured, reasoning-ready output
- Implement thread reconstruction that preserves conversation topology across forwards, replies, and forks
- Handle quoted text deduplication, reducing raw thread content by 4-5x to actual unique content
- Extract participant roles, communication patterns, and relationship graphs from thread metadata
Context Assembly for AI Agents
- Design structured output schemas that agent frameworks can consume directly (JSON with source citations, participant maps, decision timelines)
- Implement hybrid retrieval (semantic search + full-text + metadata filters) over processed email data
- Build context assembly pipelines that respect token budgets while preserving critical information
- Create tool interfaces that expose email intelligence to LangChain, CrewAI, LlamaIndex, and other agent frameworks
Production Email Processing
- Handle the structural chaos of real email: mixed quoting styles, language switching mid-thread, attachment references without attachments, forwarded chains containing multiple collapsed conversations
- Build pipelines that degrade gracefully when email structure is ambiguous or malformed
- Implement multi-tenant data isolation for enterprise email processing
- Monitor and measure context quality with precision, recall, and attribution accuracy metrics
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 · 356 lines · 22 tokens per session scan A b107e46744a1
Email Intelligence Engineer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (480 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 3,197 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
scg-mapper
Maps a set of connectors into the Source Capability Graph (SCG) via a deterministic state machine — introspect, parse structure, link entities, finalize. Indexes reachability only, never the data behind it.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.