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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CLI to manage emails via IMAP/SMTP. Use himalaya to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Himalaya is a CLI email client that lets you manage emails from the terminal using IMAP, SMTP, Notmuch, or Sendmail backends.
references/configuration.md (config file setup + IMAP/SMTP authentication)references/message-composition.md (MML syntax for composing emails)himalaya --version to verify)~/.config/himalaya/config.toml# Pre-built binary (Linux/macOS — recommended)
curl -sSL https://raw.githubusercontent.com/pimalaya/himalaya/master/install.sh | PREFIX=~/.local sh
# macOS via Homebrew
brew install himalaya
# Or via cargo (any platform with Rust)
cargo install himalaya --locked
Run the interactive wizard to set up an account:
himalaya account configure
Or create ~/.config/himalaya/config.toml manually:
[accounts.personal]
email = "you@example.com"
display-name = "Your Name"
default = true
backend.type = "imap"
backend.host = "imap.example.com"
backend.port = 993
backend.encryption.type = "tls"
backend.login = "you@example.com"
backend.auth.type = "password"
backend.auth.cmd = "pass show email/imap" # or use keyring
message.send.backend.type = "smtp"
message.send.backend.host = "smtp.example.com"
message.send.backend.port = 587
message.send.backend.encryption.type = "start-tls"
message.send.backend.login = "you@example.com"
message.send.backend.auth.type = "password"
message.send.backend.auth.cmd = "pass show email/smtp"
cat << EOF | himalaya template send) is recommended for reliability. Interactive $EDITOR mode works with pty=true + background + process tool, but requires knowing the editor and its commands--output json for structured output that's easier to parse programmaticallyhimalaya account configure wizard requires interactive input — use PTY mode: terminal(command="himalaya account configure", pty=true)himalaya folder list
List emails in INBOX (default):
himalaya envelope list
List emails in a specific folder:
himalaya envelope list --folder "Sent"
List with pagination:
himalaya envelope list --page 1 --page-size 20
himalaya envelope list from john@example.com subject meeting
Read email by ID (shows plain text):
himalaya message read 42
Export raw MIME:
himalaya message export 42 --full
To reply non-interactively from Hermes, read the original message, compose a reply, and pipe it:
# Get the reply template, edit it, and send
himalaya template reply 42 | sed 's/^$/\nYour reply text here\n/' | himalaya template send
Or build the reply manually:
cat << 'EOF' | himalaya template send
From: you@example.com
To: sender@example.com
Subject: Re: Original Subject
In-Reply-To: <original-message-id>
Your reply here.
EOF
Reply-all (interactive — needs $EDITOR, use template approach above instead):
himalaya message reply 42 --all
# Get forward template and pipe with modifications
himalaya template forward 42 | sed 's/^To:.*/To: newrecipient@example.com/' | himalaya template send
Non-interactive (use this from Hermes) — pipe the message via stdin:
cat << 'EOF' | himalaya template send
From: you@example.com
To: recipient@example.com
Subject: Test Message
Hello from Himalaya!
EOF
Or with headers flag:
himalaya message write -H "To:recipient@example.com" -H "Subject:Test" "Message body here"
Note: himalaya message write without piped input opens $EDITOR. This works with pty=true + background mode, but piping is simpler and more reliable.
Move to folder:
himalaya message move 42 "Archive"
Copy to folder:
himalaya message copy 42 "Important"
himalaya message delete 42
Add flag:
himalaya flag add 42 --flag seen
Remove flag:
himalaya flag remove 42 --flag seen
List accounts:
himalaya account list
Use a specific account:
himalaya --account work envelope list
Save attachments from a message:
himalaya attachment download 42
Save to specific directory:
himalaya attachment download 42 --dir ~/Downloads
Most commands support --output for structured output:
himalaya envelope list --output json
himalaya envelope list --output plain
Enable debug logging:
RUST_LOG=debug himalaya envelope list
Full trace with backtrace:
RUST_LOG=trace RUST_BACKTRACE=1 himalaya envelope list
himalaya --help or himalaya <command> --help for detailed usage.references/message-composition.md).pass, system keyring, or a command that outputs the password.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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