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 hezkvectory/hermes-edu-skills --skill agent-memory-methodgit clone --depth 1 https://github.com/hezkvectory/hermes-edu-skillsWrote 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/hezkvectory/hermes-edu-skills/agent-memory-method)<a href="https://agentmods.dev/skills/hezkvectory/hermes-edu-skills/agent-memory-method"><img src="https://agentmods.dev/badge/skills/hezkvectory/hermes-edu-skills/agent-memory-method/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/hezkvectory/hermes-edu-skills/agent-memory-method"><img src="https://agentmods.dev/badge/skills/hezkvectory/hermes-edu-skills/agent-memory-method.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 | $0.00043 | $0.01275 |
| Opus 5 | $0.00022 | $0.00638 |
| Sonnet 5 | $0.00009 | $0.00255 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
agent-memory-method 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 9d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
记忆方法 Skill
把需要背的内容变成可提取、可复述、可间隔复习的记忆任务，而不是反复机械朗读。
这个 Skill 解决什么问题 / Problem
把需要背的内容变成可提取、可复述、可间隔复习的记忆任务，而不是反复机械朗读。
最适合 / Best For
- 古诗课文背诵
- 英语单词
- 公式概念
- 历史地理知识点
不适合 / Not For
- 超大量内容一次性硬背
- 完全不理解内容就要求长期记住
使用前请准备 / Inputs
- 要记忆的文本/词表/知识点
- 年级
- 截止时间
- 已掌握程度
- 容易忘的部分
推荐工作流 / Recommended Workflow
- 压缩成记忆块
- 设计提取线索
- 安排首次背诵和回想
- 生成间隔复习表
- 用小测检查能否独立提取
输出格式 / Output Format
- 记忆块
- 提取问题
- 背诵步骤
- 间隔复习安排
- 自测题
质量检查 / Quality Checks
- 必须包含主动回忆
- 复习间隔要明确
- 不能只给口诀
- 要区分理解和记忆
没有平台工具时 / Standalone Fallback
- 没有记忆工具时，输出可复制的复习卡片。
- 没有历史记录时，从当前材料生成首轮计划。
示例提示 / Example Prompts
- 帮我把这首古诗拆成今晚能背下来的步骤。
- 这些英语单词容易混，帮我做记忆卡和复习计划。
适用场景 / When To Use
当学习者、家长、老师、学校或教育应用开发者需要处理以下场景时，可以使用这个 Skill。
最适合的场景：
- 背诵记忆
适用角色：
- 学习者
- 家长
- 老师
调用信号 / Invocation Signals
意图：
agent_memory_methodlearning_core背诵记忆
示例表达：
- 开始记忆方法 Skill
- 帮我做背诵记忆
- 根据当前上下文执行记忆方法 Skill
公开 Skill 契约 / Public Skill Contract
- Workflow:
agent_memory_method.run - Category:
learning-core - Stages:
primary,junior,senior - Subjects:
学习能力 - Abilities:
记忆 - Quality Tier:
curated - Standalone Support:
needs_user_input - Public Release:
recommended - Requires Tools:
context.load,entitlement.check,workflow.create,memory.write - Requires Data:
学习目标,年级或水平,用户输入的题目/记录/上下文 - Export Mode:
installable - Release Channel:
recommended
成熟度备注：
- 已按精品 Skill 标准补充边界、输入、工作流、输出格式和示例。
参数化使用 / Parameters
这个 Skill 不再把年级、册别、单元、知识点和难度拆成大量独立 Skill。请在调用时通过参数或自然语言补充这些信息。
- Grades:
一年级,二年级,三年级,四年级,五年级,六年级,七年级,八年级,九年级,高一,高二,高三 - Semesters:
上册,下册,上册,下册,必修一,必修二,选择性必修 - Scenarios:
背诵记忆 - Difficulties:
基础,标准,提高 - Parameterized Dimensions:
grade,semester,unit,lesson,knowledgePointCodes,scenario,difficulty
独立 Hermes 使用方式 / Standalone Hermes Usage
这个 Skill 可以通过 Hermes 的 skills.external_dirs 作为外部 Skill 加载。
如果你有自己的工具、记忆、课程数据或 workflow runner，可以把它们与本 Skill 组合使用。如果没有外部工具，也可以直接使用上面的说明来引导对话，生成有用的学习或教学反馈。
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.
- 9d ago First seen · 145 lines · 43 tokens per session scan A 6180e6ec5280
agent-memory-method is a skill published in the GitHub repository hezkvectory/hermes-edu-skills (94 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 1,275 once invoked, about $0.0002 per session on Opus 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
self-improve-with-tinyfish
Enables Hermes to create new reusable skills for itself by researching live web sources with TinyFish Search and Fetch, analyzing source coverage, writing SKILL.md files, and installing them into Hermes memory. Use when the user asks Hermes to learn, teach itself, upgrade itself, or save a reusable capability.
flow-next-audit
Audit .flow/memory/ entries against current code and keep, update, consolidate, replace, delete, or harden each. Use when asked to audit memory or graduate a recurring lesson into a gate.
mindos
MindOS: local knowledge assistant & shared KB. Keeps decisions, notes, SOPs, debugging lessons, research findings, preferences across sessions/agents. Core: save notes, search KB, organize files, run workflows, review, append CSV, hand off context, distill lessons. NOT for app source or paths outside KB. Triggers…
flashcards
Spaced repetition flashcard system for learning. SM-2 algorithm, supports text and cloze deletion cards.
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.
curate
Adds developer-authored annotations to the gauntlet knowledge base. Use when capturing tribal knowledge or rationale not visible in code.