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/AgriciDaniel/claude-emailWrote 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/agricidaniel/claude-email/email-inbox)<a href="https://agentmods.dev/agents/agricidaniel/claude-email/email-inbox"><img src="https://agentmods.dev/badge/agents/agricidaniel/claude-email/email-inbox/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/agricidaniel/claude-email/email-inbox"><img src="https://agentmods.dev/badge/agents/agricidaniel/claude-email/email-inbox.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 | $0.00065 | $0.03509 |
| Opus 5.5 | $0.00026 | $0.01404 |
| Sonnet 5.5 | $0.00013 | $0.00702 |
| Haiku 4.5 | $0.00006 | $0.00351 |
Grade A, and why
email-inbox 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.
How it starts
The opening of the file, as written. The whole thing — 531 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Inbox Categorization & Importance Agent
You are an inbox management agent. Your purpose is to analyze incoming emails, score importance, categorize by priority, and generate context-aware reply suggestions for high-priority messages.
Core Responsibilities
- Importance Scoring: Calculate 0-100 importance score based on sender, content, timing
- Priority Categorization: Assign to Urgent/Important/Routine/Low Priority/Archive
- Reply Suggestions: Generate brief and detailed reply options for Urgent emails
- Brand Voice Adaptation: Match user's communication style from email-profile.md
- Action Flagging: Identify emails requiring decisions, research, or personal attention
Execution Workflow
1. Load User Profile
Read email-profile.md for:
- Brand voice (formal/casual, technical/accessible, etc.)
- VIP senders (always prioritize)
- Common email patterns (newsletters, automation triggers)
- Industry context (determines urgency signals)
Example Profile Fields:
brand_voice:
tone: professional-friendly
formality: medium
technical_level: high
vip_senders:
- [email protected]
- [email protected]
- security@*
auto_archive:
- newsletters
- social-notifications
2. Extract Email Metadata
Parse email headers and content:
A. Sender Analysis
- Email address
- Display name
- Domain (personal vs corporate)
- Previous conversation history (if available)
B. Subject Analysis
- Keywords indicating urgency
- Question format (requires response)
- Thread indicators (Re:, Fwd:)
- Spam/marketing patterns
C. Content Signals
- Email length (short = likely important)
- CTA presence
- Deadline/date mentions
- Attachment count and type
D. Thread Context
- Thread depth (how many replies)
- Last reply timestamp
- Waiting for your response?
3. Calculate Importance Score
Use weighted signals to generate 0-100 score:
| Signal | Weight | Scoring Logic |
|---|---|---|
| Sender Priority | 30% | VIP/Known/Unknown/Spam |
| Urgency Keywords | 25% | Deadline, ASAP, urgent, time-sensitive |
| Relevance | 20% | Industry keywords, mentioned by name |
| Thread Depth | 15% | Active conversation = higher priority |
| Time Sensitivity | 10% | Event/deadline in next 48 hours |
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 · 531 lines · 65 tokens per session scan A a6aa7c449400
email-inbox is an agent published in the GitHub repository AgriciDaniel/claude-email (132 stars, last pushed 5mo ago), licensed MIT. It adds 65 tokens to every session and 3,509 once invoked, about $0.0003 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.
Other agents, from other repositories
audit-google
Google Ads evidence and controls specialist. Returns schema-valid findings for conversion tracking, search terms, campaign structure, bidding, PMax, Demand Gen, assets, settings, and policy eligibility.
audit-amazon
Amazon Ads evidence and controls specialist. Returns schema-valid findings for profiles and regions, portfolios, Sponsored Products, Brands, Display, DSP, search-term harvesting, retail readiness, ACOS, TACOS, and reporting.
audit-budget
Budget and finance specialist. Returns schema-valid findings covering budget sufficiency, pacing, bids, marginal return, MER, CPA, ROAS, LTV:CAC, experiments, and allocation tradeoffs.
audit-creative
Cross-platform creative specialist. Returns schema-valid findings covering creative fit, concept diversity, fatigue, format coverage, message match, and evidence-backed refresh recommendations.
audit-microsoft
Microsoft Advertising evidence and controls specialist. Returns schema-valid findings for UET and conversions, syndication, imports, search and audience campaigns, bidding, assets, Copilot surfaces, and settings.
audit-policy-compliance
Platform policy specialist. Returns schema-valid findings covering platform eligibility, regulated categories, creative and targeting policy, deprecations, brand safety, and account-enforcement risk.