Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Give the agent its own dedicated email inbox via AgentMail. Send, receive, and manage email autonomously using agent-owned email addresses (e.g. hermes-agent@agentmail.to).
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Use this skill when you need to:
This is NOT for reading the user's personal email (use himalaya or Gmail for that). AgentMail gives the agent its own identity and inbox.
am_)Add to ~/.hermes/config.yaml (paste your actual key — MCP env vars are not expanded from .env):
mcp_servers:
agentmail:
command: "npx"
args: ["-y", "agentmail-mcp"]
env:
AGENTMAIL_API_KEY: "am_your_key_here"
hermes
All 11 AgentMail tools are now available automatically.
| Tool | Description |
|---|---|
list_inboxes | List all agent inboxes |
get_inbox | Get details of a specific inbox |
create_inbox | Create a new inbox (gets a real email address) |
delete_inbox | Delete an inbox |
list_threads | List email threads in an inbox |
get_thread | Get a specific email thread |
send_message | Send a new email |
reply_to_message | Reply to an existing email |
forward_message | Forward an email |
update_message | Update message labels/status |
get_attachment | Download an email attachment |
create_inbox with a username (e.g. hermes-agent)hermes-agent@agentmail.tosend_message with inbox_id, to, subject, textlist_threads to see incoming conversationsget_thread to read a specific threadlist_inboxes to find your inbox IDlist_threads with the inbox ID to see conversationsget_thread to read a thread and its messagesget_threadreply_to_message with the message ID and your reply textSign up for a service:
1. create_inbox (username: "signup-bot")
2. Use the inbox address to register on the service
3. list_threads to check for verification email
4. get_thread to read the verification code
Agent-to-human outreach:
1. create_inbox (username: "hermes-outreach")
2. send_message (to: user@example.com, subject: "Hello", text: "...")
3. list_threads to check for replies
@agentmail.to domain on free tier (custom domains on paid plans)npx -y agentmail-mcp)mcp Python package must be installed: pip install mcplist_threads polling via cronjob instead for personal useAfter setup, test with:
hermes --toolsets mcp -q "Create an AgentMail inbox called test-agent and tell me its email address"
You should see the new inbox address returned.
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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