/email-concierge

name: email-concierge
description: Local AI email assistant that automates unread-mail processing, offline-safe classification and reply drafting, local draft editing/approval queues, calendar conflict checks, voice transcription, and confirmed SMTP sending when connectivity returns. Use when the user wants to manage email, check inbox, draft or edit replies, queue messages offline, approve/send email, handle meeting invites, or mentions email assistant, IMAP, SMTP, voice memo, local AI email.

Email Concierge — Local AI Email Assistant

A privacy-first email agent that reads, understands, and drafts replies to your emails. Text inference runs locally via OpenVINO GenAI with device priority NPU > GPU > CPU — no data leaves your machine.

**Platforms:** Windows · macOS · Linux (x86-64 and arm64)

Architecture

IMAP Mailbox ──→ read_mailbox ──→ classify_summarize (OpenVINO GenAI, batched)
                                        │
iCal Files  ──→ check_calendar          │
                                        ▼
Microphone  ──→ start_voice_memo ──→ transcribe_memo (distil-whisper, optional)
                                        │
                                        ▼
                                 draft_reply (OpenVINO GenAI, 3 styles, single call)

**Models:** `OpenVINO/Qwen3-4B-int4-ov` (~2 GB, int4 OpenVINO format, auto-downloaded to `state/models/`) for classification and drafting; `distil-whisper-large-v3-int8-ov` (optional) for transcription.

Agent execution

Treat this as a cross-platform skill. Resolve `SKILL_DIR` from the loaded `SKILL.md`; never assume a Windows path or a particular shell. The bundled `setup_auto.py` detects OS, architecture, shell, package manager, Python, Intel NPU/OpenVINO devices, and local model availability, then installs only compatible components. `setup.bat` and `setup.sh` are thin wrappers.

Resolve this `SKILL.md` directory as `SKILL_DIR`; do not assume the current workspace contains the executable. On Windows/Qoder, prefer:

$skillDir = Join-Path $env:USERPROFILE ".qoder\skills\email-concierge"
& "$skillDir\venv\Scripts\python.exe" "$skillDir\email_concierge.py" --diagnose --json

Run `setup_auto.py --project-dir "$skillDir"` before automated inbox work, or when diagnostics report `setup_required=true`; setup installs missing components (including `openvino-genai`) and downloads the configured LLM model. Run `--diagnose --json` after setup. `local_ai_ready` defaults to `true` on supported Windows, macOS, and Linux systems. `local_ai_installed` reports whether OpenVINO, openvino-genai, and the model are all present. `llm_device` shows the device inference will use (highest available of NPU/GPU/CPU unless `device` pins one) and `llm_model_downloaded` whether the model files exist locally. NPU acceleration is ready only when `aipc_ready=true` and `openvino_devices` includes `NPU`.

The bootstrapper supports Windows PowerShell/CMD, macOS zsh/Bash, and Linux shells. It creates a local venv, installs core dependencies and OpenVINO GenAI via pip (no system elevation required), and downloads the LLM model directly from the ModelScope domestic source (`https://modelscope.cn`). Use `--no-openvino` or `--no-llm-model` only for constrained/offline environments. Setup also deletes the legacy `EmailConcierge-Ollama` logon task registered by older versions.

When executed without a TTY (e.g. by Qoder), `setup_auto.py` automatically enables `--yes` and skips system package manager installs on non-first installs. A marker file at `state/.setup_completed.json` records completion.

**Performance notes:** the first generation on a device compiles the model graph — on NPU this can take several minutes; the compiled cache in `state/ov_cache/<device>` makes subsequent runs start in seconds. Classification is one batched call for all emails and drafting is one merged call for all three styles (JSON-parsed), with keyword classification and template drafts as fallbacks so the workflow never blocks on model failure.

Automation policy

For inbox requests, run `--inbox` by default. It automatically reads unread mail without marking it read, falls back to cached mail while offline, classifies every message, checks calendar conflicts for meeting messages, creates local reply drafts, and persists them in `state/draft_queue.json`. If the local LLM is unavailable, create an editable deterministic draft and continue.

Only request user input for missing required IMAP/SMTP credentials, explicit reply instructions, draft edits, or final send confirmation. Never send automatically: require `--confirm-send` after the user has approved the queued draft. When offline, finish all local work and leave sending pending for a later `--send`.

---

Installation

Automated install for Qoder

A `qoder.json` manifest is included. Qoder can install the skill automatically with the platform-specific installer below. Existing `email_concierge_config.json` and `state/` are preserved across reinstalls.

**Windows**

.\install_qoder.ps1

# Copy files only, without package/model installation
.\install_qoder.ps1 -NoSetup

All dependencies (openvino-genai included) install into the local venv and the model downloads to `state/models/`; no administrator elevation is needed. Subsequent reinstalls are silent and preserve `email_concierge_config.json` and `state/`.

From CMD, run `install_qoder.bat`; it delegates to the same PowerShell flow and returns the installer exit code.

**macOS / Linux**

bash install_qoder.sh

Everything installs into the local venv without root. PortAudio (for voice recording) is the only optional system package; install it with your package manager when needed.

After installation, restart Qoder or open a new agent session so it reloads `SKILL.md`.

Manual install

Copy the skill directory into the target app's skill folder and run the bootstrapper:

python3 install_skill.py --target qod