Cognee is an AI memory platform that stores information in a self-hosted knowledge graph so agents can retain context across sessions. It ingests data, connects related information, and helps agents retrieve it for reasoning and actions. The catalogue includes skills and instructions that extend agent workflows around Cognee.
Borrowing it
Nothing to install: this file belongs to topoteretes/cognee. 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/topoteretes/cognee/main/.agents/skills/personalized-email/SKILL.mdgit clone --depth 1 https://github.com/topoteretes/cogneeWrote 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/topoteretes/cognee/personalized-email)<a href="https://agentmods.dev/skills/topoteretes/cognee/personalized-email"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/personalized-email/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/topoteretes/cognee/personalized-email"><img src="https://agentmods.dev/badge/skills/topoteretes/cognee/personalized-email.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 | $0.00072 | $0.00693 |
| Opus 5.5 | $0.00029 | $0.00277 |
| Sonnet 5.5 | $0.00014 | $0.00139 |
| Haiku 4.5 | $0.00007 | $0.00069 |
Grade A, and why
personalized-email scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
- 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-granolainstead.
- Run
uv run python examples/cookbooks/personalized_email/personalized_email.py. Add--days Nif the user named a time range for meetings, and--no-granolaif they don't want Granola used. - 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. - If a script fails,
personalized_email.pyexits 1 with a line naming what went wrong. Run that script fromexamples/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.jsonor.env. - A sample run forgets the cookbook's dataset
personalized_emailbefore it ingests (the[clear]line). On the user's own data, pass--clearonly 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.
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 · 55 lines · 72 tokens per session scan A c407a9596cb6
personalized-email is a skill published in the GitHub repository topoteretes/cognee (33,094 stars, last pushed today), licensed Apache-2.0. It adds 72 tokens to every session and 693 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-08.
personalized-email compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| personalized-emailtopoteretes/cognee | Skill | 33k | today | Apache-2.0 | A | 72 | Claude Code, Codex |
| openmemoryCaviraOSS/OpenMemory | Skill | 4.6k | 19d ago | Apache-2.0 | A | 96 | Claude Code, Codex |
| memseek-remembermemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 24 | Claude Code |
| memseek-explainmemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 25 | Claude Code |
| memseek-searchmemseekai/memseek | Skill | 11 | yesterday | Apache-2.0 | A | 22 | Claude Code |
| surface-learningsterrizoaguimor/celiums-memory | Skill | 23 | 2mo ago | Apache-2.0 | A | 60 | Claude Code |
| context-recoveryterrizoaguimor/celiums-memory | Skill | 23 | 2mo ago | Apache-2.0 | A | 37 | Claude Code |