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Personalized Email

OrganizationPopular
topoteretes
personalized-email

Draft a reply to your newest Gmail email that knows what you discussed in your Granola meetings, what you promised the sender, and how you write, using cognee memory. Use when someone asks to answer or reply to their latest email in their own tone. Runs locally; reads Gmail read-only; the draft is printed, never sent.

Overview

Publishertopoteretes
Repositorycognee
Skill namepersonalized-email
Stars
32K
Forks
3.3K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by topoteretes on GitHub. Read the source before you install it.

Installation

Install the Personalized Email AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/topoteretes/cognee/tree/main/.agents/skills/personalized-email
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/topoteretes/cognee.git /tmp/cognee
mkdir -p .claude/skills
cp -r /tmp/cognee/.agents/skills/personalized-email .claude/skills/personalized-email
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Personalized Email in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Personalized Email on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Personalized Email is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Personalized email

Runs the personalized email cookbook in examples/cookbooks/personalized_email/. It remembers your Granola meeting notes and your Gmail inbox and sent mail in the cognee dataset personalized_email, then drafts a reply to the newest inbox email. Setup, the scripts and what each one does are in the cookbook's README.md.

Run every command from the repo root.

Steps

With neither Gmail nor Granola set up, the commands below run on a sample mailbox (the [setup] line says so) and draft a reply to a fictional email; only LLM_API_KEY is needed. Tell the user the reply is to sample data. Add --sample to use the sample even when their accounts are set up.

  1. Run uv run python examples/cookbooks/personalized_email/personalized_email.py --check.
    • Exit 0: go to step 2.
    • Exit 2: each [setup] MISSING: line names one fix. Don't create credentials yourself. Tell the user exactly which line to fix, using the README's "What it needs" table, and stop. If only the Granola line is missing, ask whether to run with --no-granola instead.
  2. Run uv run python examples/cookbooks/personalized_email/personalized_email.py. Add --days N if the user named a time range for meetings, and --no-granola if they don't want Granola used.
  3. The draft is everything after the line [draft] Reply:. Give it to the user, and say which email it answers (the [draft] Answering: line) and which sources went in (the [ingest_granola] and [ingest_email] lines). The draft is never sent: hand it to the user to send.
  4. If a script fails, personalized_email.py exits 1 with a line naming what went wrong. Run that script from examples/cookbooks/personalized_email/scripts/ on its own to look closer.

Rules

  • Gmail access is read-only and nothing is sent.
  • Don't print the contents of credentials.json, token.json or .env.
  • A sample run forgets the cookbook's dataset personalized_email before it ingests (the [clear] line). On the user's own data, pass --clear only when they asked to start over: it forgets everything the dataset holds.
  • Ingesting reads the user's real meetings and mailbox, and every run uses LLM credits, so run it only when the user asked for it.

Clean up

bash
uv run cognee-cli forget --dataset personalized_email

Frequently asked questions

What does the Personalized Email AI skill do?

Draft a reply to your newest Gmail email that knows what you discussed in your Granola meetings, what you promised the sender, and how you write, using cognee memory. Use when someone asks to answer or reply to their latest email in their own tone. Runs locally; reads Gmail read-only; the draft is printed, never sent.

Why use Personalized Email on TypingMind?

Because you install it once and use it with any model. Personalized Email is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Personalized Email in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/topoteretes/cognee/tree/main/.agents/skills/personalized-email. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Personalized Email?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Personalized Email?

As many as you like. As long as a model supports skills, you can use Personalized Email with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Personalized Email AI skill free?

Yes. It is published on GitHub by topoteretes under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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