Borrowing it
Nothing to install: this file belongs to different-ai/agent-bank. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/different-ai/agent-bank/main/.opencode/skill/ai-email-pipeline/SKILL.mdgit clone --depth 1 https://github.com/different-ai/agent-bankWrote 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/different-ai/agent-bank/ai-email-pipeline)<a href="https://agentmods.dev/skills/different-ai/agent-bank/ai-email-pipeline"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/ai-email-pipeline/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/skills/different-ai/agent-bank/ai-email-pipeline"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/ai-email-pipeline.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 | $0.00000 | $0.02216 |
| Opus 5.5 | $0.00000 | $0.00886 |
| Sonnet 5.5 | $0.00000 | $0.00443 |
| Haiku 4.5 | $0.00000 | $0.00222 |
Grade A, and why
ai-email-pipeline 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.
const response = await fetch(att.blobUrl); How it starts
The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Email Pipeline
Skill Contract
Purpose
- Run the inbound/outbound AI email workflow for 0 Finance.
Inputs
- Inbound webhook payload (Resend or SES).
- Session state + attachments.
- Workspace mapping + AI email handle.
Outputs
- Updated session state, created/updated invoices/transfers, outbound replies.
Entities + CRUD Coverage
- Session: create/get/update via
packages/web/src/lib/ai-email/session.ts; delete not supported. - Message: create (append), read (history), update (follow-up), delete not supported.
- Attachment: create (persist), read (load), update not used, delete not supported.
- Invoice draft: create/update/send via tools.
Tools Used (Atomic)
extractInvoiceDetails,createInvoice,updateInvoice,requestConfirmation,sendReplyToUser.sendInvoiceToRecipient,getBalance,listSavedBankAccounts,createBankAccount,proposeTransfer.
Completion Signals
completewhen a reply is sent or a pending action is finalized.continuewhen awaiting user confirmation or clarification.erroron provider failures or invalid webhook signatures.
Credentials & Config
- Resend:
RESEND_API_KEY,RESEND_WEBHOOK_SECRET. - SES:
AWS_ACCESS_KEY_ID,AWS_SECRET_ACCESS_KEY,AWS_DEFAULT_REGION. EMAIL_PROVIDER=resendorses.AI_EMAIL_INBOUND_DOMAINdefaultzerofinance.ai.
Missing Credential Behavior
- If provider credentials are missing, ask the user and stop.
Canonical Inbox
Core Mental Model
The AI email system is a conversation, not a state machine.
Every email is a message in an ongoing conversation. The LLM reads the full context and decides what to do. There are no hardcoded shortcuts, no regex parsing for intent, no branching logic outside the AI.
Architecture Principle
Email arrives → Parse attachments → Build message history → Send to LLM → LLM calls tools → Done
That's it. The LLM handles:
- Understanding user intent ("yes", "no I mean...", "actually change it to...")
- Deciding which tools to call
- Generating responses
- Handling corrections and clarifications
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 · 300 lines · 0 tokens per session scan A aa9f1bebc609
ai-email-pipeline is a skill published in the GitHub repository different-ai/agent-bank (255 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,216 tokens. 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 2026-08-30.
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