Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NousResearch/hermes-agent --skill memento-flashcardsgit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/memento-flashcards)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/memento-flashcards"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/memento-flashcards/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/nousresearch/hermes-agent/memento-flashcards"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/memento-flashcards.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 67 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Excessive Agency · line 79 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00021 | $0.03263 |
| Opus 5.5 | $0.00008 | $0.01305 |
| Sonnet 5.5 | $0.00004 | $0.00653 |
| Haiku 4.5 | $0.00002 | $0.00326 |
Grade A, and why
memento-flashcards 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- memento-flashcards — 100% identical, 19 lines differ
- memento-flashcards — 100% identical, 19 lines differ
- memento-flashcards — 95% identical, 25 lines differ
- memento-flashcards — 95% identical, 25 lines differ
- memento-flashcards — 95% identical, 60 lines differ
- memento-flashcards — 95% identical, 25 lines differ
- memento-flashcards — 95% identical, 0 lines differ
- memento-flashcards — 95% identical, 55 lines differ
How it starts
The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memento Flashcards — Spaced-Repetition Flashcard Skill
Overview
Memento gives you a local, file-based flashcard system with spaced-repetition scheduling. Users can chat with their flashcards by answering in free text and having the agent grade the response before scheduling the next review. Use it whenever the user wants to:
- Remember a fact — turn any statement into a Q/A flashcard
- Study with spaced repetition — review due cards with adaptive intervals and agent-graded free-text answers
- Quiz from a YouTube video — fetch a transcript and generate a 5-question quiz
- Manage decks — organise cards into collections, export/import CSV
All card data lives in a single JSON file. No external API keys are required — you (the agent) generate flashcard content and quiz questions directly.
User-facing response style for Memento Flashcards:
- Use plain text only. Do not use Markdown formatting in replies to the user.
- Keep review and quiz feedback brief and neutral. Avoid extra praise, pep, or long explanations.
When to Use
Use this skill when the user wants to:
- Save facts as flashcards for later review
- Review due cards with spaced repetition
- Generate a quiz from a YouTube video transcript
- Import, export, inspect, or delete flashcard data
Do not use this skill for general Q&A, coding help, or non-memory tasks.
Quick Reference
| User intent | Action |
|---|---|
| "Remember that X" / "save this as a flashcard" | Generate a Q/A card, call memento_cards.py add |
| Sends a fact without mentioning flashcards | Ask "Want me to save this as a Memento flashcard?" — only create if confirmed |
| "Create a flashcard" | Ask for Q, A, collection; call memento_cards.py add |
| "Review my cards" | Call memento_cards.py due, present cards one-by-one |
| "Quiz me on [YouTube URL]" | Call youtube_quiz.py fetch VIDEO_ID, generate 5 questions, call memento_cards.py add-quiz |
| "Export my cards" | Call memento_cards.py export --output PATH |
| "Import cards from CSV" | Call memento_cards.py import --file PATH --collection NAME |
| "Show my stats" | Call memento_cards.py stats |
| "Delete a card" | Call memento_cards.py delete --id ID |
| "Delete a collection" | Call memento_cards.py delete-collection --collection NAME |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +11 lines 2d2e97cea8a8
- 1mo ago First seen · 321 lines · 21 tokens per session scan A 4504541598e6
memento-flashcards is a skill published in the GitHub repository NousResearch/hermes-agent (257,063 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 3,263 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
visualize
Create visualizations and interactive tools directly in conversation. Proactively use to show how something works; explore 'what happens when', 'what changes', or 'help me understand'; compare or inspect; create simulations, maps, charts, graphs, and mockups. Use standard tools for static scientific figures.
build-interactive-explainers
A guide for building interactive explainers, calculators, and simulations driven by an executable model. Users change inputs, steps, states, or events to understand a rule or see how a process develops over time.
ai-tutor
Use when user asks to explain, break down, or help understand technical concepts (AI, ML, or other technical topics). Makes complex ideas accessible through plain English and narrative structure. Use the provided scripts to transcribe videos.
biological-expert
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.
education-expert
Expert-level education technology, learning management systems, and ed-tech platforms. Use when the user mentions edtech, lms, e learning, or assessment, or when the task involves Educational Technology or Standards.
quantum-expert
Expert-level quantum computing, Qiskit, quantum algorithms, and quantum information. Use when the user mentions quantum computing, Qiskit, quantum algorithms, or quantum information, or when the task involves Quantum Mechanics Basics, Quantum Hardware, Circuit Design, or Algorithm Implementation.