Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。
Install Memory System
$ npx skills add kangarooking/system-prompt-skills --skill memory-system -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install kangarooking/system-prompt-skills memory-system --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/memory-system .claude/skills/memory-system && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "memory-system" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/memory-system into .claude/skills/memory-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-system", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/kangarooking/system-prompt-skills/tree/main/memory-systemType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add kangarooking/system-prompt-skills --skill memory-system -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install kangarooking/system-prompt-skills memory-system --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/memory-system .agents/skills/memory-system && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "memory-system" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/memory-system into .agents/skills/memory-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-system", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kangarooking/system-prompt-skills --skill memory-system -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install kangarooking/system-prompt-skills memory-system --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/memory-system .cursor/skills/memory-system && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "memory-system" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/memory-system into .cursor/skills/memory-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-system", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/kangarooking/system-prompt-skills.git --path memory-system--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add kangarooking/system-prompt-skills --skill memory-system -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install kangarooking/system-prompt-skills memory-system --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/memory-system .gemini/skills/memory-system && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "memory-system" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/memory-system into .gemini/skills/memory-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-system", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install kangarooking/system-prompt-skills memory-systemInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add kangarooking/system-prompt-skills --skill memory-system -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/memory-system .github/skills/memory-system && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "memory-system" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/memory-system into .github/skills/memory-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-system", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kangarooking/system-prompt-skills --skill memory-system -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install kangarooking/system-prompt-skills memory-system --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kangarooking/system-prompt-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/memory-system .opencode/skills/memory-system && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "memory-system" agent skill from https://github.com/kangarooking/system-prompt-skills/tree/main/memory-system into .opencode/skills/memory-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "memory-system", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
memory-system- GitHub stars
- 205
- Token cost
- ~1.2k tokens
- SKILL.md length
- 326 words
- Files
- 1
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- MIT
At a glance
当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。
- Works in 4 steps: Create (创建): 决定何时创建记忆。FlintK12… → Retrieve (检索): 在每轮对话开始时，将相关记忆注入 context… → Apply (应用):… → …
- 信号：AI 需要跨会话记住用户信息、记忆内容可能敏感、需要在回复中隐式应用记忆而非显式引用、用户要求记住这个
- SKILL.md covers R — 原文 (Reading), I — 方法论骨架 (Interpretation), A1 — 案例分析 (Past Application) and A2 — 触发场景 (Future Trigger) ★, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Memory System is an agent skill from kangarooking/system-prompt-skills. 当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。 不适用于：定义工具接口（tool-specification）、定义安全规则（safety-guardrails）、定义人格风格（personality-system）。 关键 trigger 信号：AI 需要跨会话记住用户信息、记忆内容可能敏感、需要在回复中隐式应用记忆而非显式引用、用户要求"记住这个"。
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering. The repository describes itself as: 从 165 个顶级 AI 产品系统提示词中蒸馏出的 15 个可执行 Agent skill. The licence is MIT.
When your agent uses it
- 信号：AI 需要跨会话记住用户信息、记忆内容可能敏感、需要在回复中隐式应用记忆而非显式引用、用户要求记住这个
Example prompts
- “/memory-system”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Create (创建): 决定何时创建记忆。FlintK12 采用"强制创建"策略（mandatory create_memory call），确保关键教学信息不被遗漏。Claude Web 采用"选择性创建"——只在用户明确表达偏好时创建。
- Retrieve (检索): 在每轮对话开始时，将相关记忆注入 context window。关键设计决策是"注入多少"和"注入什么"——全部注入会消耗上下文窗口，选择性注入需要相关性判断。
- Apply (应用): 静默地应用记忆内容来调整回复，而不在回复中显式引用。Claude Opus 4.7 的反面教材极为重要：如果用户曾提到宠物去世，AI 不应在无关对话中主动提及。
- Update (更新): 允许用户编辑或删除已存储的记忆。Claude Web 的 memory_user_edits 工具和 Claude Code 的 typed memory 系统都支持这一能力。
What it can do on your machine
Read from SKILL.md and the folder at commit 252cd52. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Memory System loads about 1.2k tokens when it runs. Until then it costs about 73 tokens; SKILL.md has 326 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from kangarooking/system-prompt-skills at commit 252cd52, republished under its MIT licence (© kangarooking). 326 words, ~1,152 tokens.
Download SKILL.mdSave it as
.claude/skills/memory-system/SKILL.md (or your agent's skills folder).- name
- memory-system
- description
- 当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。 不适用于：定义工具接口（tool-specification）、定义安全规则（safety-guardrails）、定义人格风格（personality-system）。 关键 trigger 信号：AI 需要跨会话记住用户信息、记忆内容可能敏感、需要在回复中隐式应用记忆而非显式引用、用户要求"记住这个"。
- tags
- memory, personalization, persistence, context-management
- related_skills
- persona-design, safety-guardrails
记忆与个性化架构 (Memory System)
R — 原文 (Reading)
Claude Web: userMemories 注入 + memory_user_edits tool + 选择性应用规则 + 静默归因 + 边界示例 Claude Code: File-based persistent memory with typed memories (user, feedback, project, reference) + MEMORY.md index Claude Opus 4.7: "NEVER reference sensitive memories in unrelated contexts" + bad example (proactively mentioning deceased pet) GPT-4o/GPT-4.5: bio tool for persistence + sensitive data prohibition FlintK12: Pedagogical memory (interests, preferences, grade level) + mandatory create_memory call Gemini CLI: save_memory tool + GEMINI.md files for project context
I — 方法论骨架 (Interpretation)
记忆系统的核心设计模式围绕"生命周期管理"和"边界控制"两个轴展开：
记忆生命周期 (CRUL 模型):
- Create (创建): 决定何时创建记忆。FlintK12 采用"强制创建"策略（mandatory create_memory call），确保关键教学信息不被遗漏。Claude Web 采用"选择性创建"——只在用户明确表达偏好时创建。
- Retrieve (检索): 在每轮对话开始时，将相关记忆注入 context window。关键设计决策是"注入多少"和"注入什么"——全部注入会消耗上下文窗口，选择性注入需要相关性判断。
- Apply (应用): 静默地应用记忆内容来调整回复，而不在回复中显式引用。Claude Opus 4.7 的反面教材极为重要：如果用户曾提到宠物去世，AI 不应在无关对话中主动提及。
- Update (更新): 允许用户编辑或删除已存储的记忆。Claude Web 的 memory_user_edits 工具和 Claude Code 的 typed memory 系统都支持这一能力。
类型化记忆 (Typed Memory): Claude Code 将记忆分为四类：user（用户偏好）、feedback（交互反馈）、project（项目上下文）、reference（参考文档）。不同类型有不同的存储策略、检索优先级和应用规则。
边界控制: 记忆系统最大的风险不是"记不住"，而是"在不该记住的时候记住了"或"在不该引用的时候引用了"。
A1 — 案例分析 (Past Application)
案例 1: Claude Web 的静默记忆应用
- 问题: 如何让 AI 利用用户记忆但不让对话变得尴尬或侵犯隐私？
- 设计模式的使用: Claude Web 实现了"静默归因"策略——记忆被注入 context 后，AI 在回复中不应透露"根据我的记忆"或"我记得你说过"等表述。配合边界示例（如"不要在无关上下文中提及用户已故宠物"），确保记忆应用既有效又不突兀。
- 结论: 记忆系统的用户体验质量取决于"隐性应用"而非"显性提及"。
案例 2: Claude Code 的文件型持久记忆
- 问题: 编程 agent 如何在长项目中保持上下文连贯性？
- 设计模式的使用: 采用文件型持久记忆，将记忆存储为 typed 文件（user/feedback/project/reference），并使用 MEMORY.md 作为索引文件。每次会话开始时通过读取 MEMORY.md 恢复项目上下文。这种方式比数据库存储更透明、更可编辑、更易于版本控制。
- 结论: 在专业工具场景中，记忆的透明性和可编辑性比自动化程度更重要。
案例 3: FlintK12 的教学记忆强制创建
- 问题: AI 如何在有限的对话轮次中积累足够的学生画像以实现个性化教学？
- 设计模式的使用: 实现 mandatory create_memory call——在每次教学交互中，AI 必须调用记忆创建工具记录学生的兴趣、理解水平和学习偏好。Pedagogical memory 存储兴趣、偏好、年级等信息，用于后续教学内容的个性化适配。
- 结论: 在高价值场景中（教育效果直接取决于个性化程度），强制创建记忆优于等待显式触发。
A2 — 触发场景 (Future Trigger) ★
用户会在什么情境下需要这个 skill?
- AI 需要跨会话记住用户偏好、历史或上下文
- 产品需要"个性化"体验但不确定如何实现记忆架构
- 记忆内容可能包含敏感信息，需要设计访问控制
- AI 在回复中暴露了不该暴露的记忆内容
- 需要决定记忆的存储方式（文件、数据库、向量存储）
语言信号
- "AI 需要记住用户的偏好"
- "跨会话保持上下文"
- "用户说'记住这个'时该怎么处理"
- "记忆中包含敏感信息"
- "如何设计个性化推荐"
与相邻 skill 的区分
- 与
persona-design的区别: persona-design 定义"AI 是谁"（静态身份），memory-system 定义"AI 知道用户什么"（动态信息）。身份不变，记忆不断积累。 - 与
safety-guardrails的区别: safety-guardrails 约束 AI 的行为边界，memory-system 中敏感记忆的处理需要 safety-guardrails 的支持，但记忆系统本身是数据管理而非行为约束。 - 与
personality-system的区别: personality-system 定义可切换的风格叠加层，memory-system 存储用户的风格偏好（如"用户喜欢简洁回复"），这些偏好可以触发特定人格变体的选择。
E — 可执行步骤 (Execution)
步骤 1: 定义记忆类型体系
- 识别产品需要记忆的信息类别：用户偏好、交互历史、项目上下文、领域知识等
- 为每个类别定义创建触发条件、存储格式和过期策略
- 完成标准: 每种记忆类型有明确的边界定义，不同类型之间不重叠
步骤 2: 设计创建机制
- 确定创建策略：强制创建（FlintK12 模式，适合高价值场景）或选择性创建（Claude Web 模式，适合通用场景）
- 定义创建触发器：显式触发（"记住这个"）+ 隐式触发（检测到偏好表达）
- 完成标准: 创建机制在 95% 的情况下能正确判断是否需要创建记忆
步骤 3: 实现静默应用规则
- 编写核心应用规则：记忆内容应隐性影响回复，不应显式引用
- 编写反面教材：列出不应在回复中出现的行为（如"根据我的记忆"、"我记得你之前说过"）
- 完成标准: 用户在对话中感受不到"被记忆"的不适感，但能体验到个性化的便利
步骤 4: 设计边界控制
- 定义敏感记忆类别（健康、财务、家庭变故等）并制定特殊处理规则
- 实现跨上下文隔离：Claude Opus 4.7 的规则"绝不在无关上下文中引用敏感记忆"
- 设计记忆编辑/删除机制，允许用户查看和管理已存储的记忆
- 完成标准: 敏感记忆不会被错误引用，用户有完全的记忆控制权
步骤 5: 选择存储策略
- 文件型存储（Claude Code 的 MEMORY.md）：适合专业工具，透明可编辑
- 结构化存储（Claude Web 的 userMemories）：适合消费产品，高性能检索
- 向量存储：适合大规模知识和语义检索场景
- 完成标准: 存储策略与产品的记忆规模、检索需求和用户期望一致
B — 边界 (Boundary) ★
不要在以下情况使用此 skill
- AI 产品不需要任何跨会话持久化（纯一次性对话）
- 记忆需求实际上可以通过简单的 system prompt 配置解决（如"始终用中文回复"）
- 试图用记忆系统替代知识库或 RAG（记忆是用户相关的个性化数据，不是通用知识）
- 没有设计记忆删除/编辑机制就上线记忆功能（这会严重损害用户信任）
常见失败模式
- 记忆过度引用: AI 在回复中频繁使用"根据我的记忆"等表述，让用户感到被监视。应严格执行静默应用规则。
- 敏感记忆泄露: 在无关对话中主动提及用户的敏感信息。Claude Opus 4.7 明确指出这是严重错误——如果用户提到宠物去世，AI 不应在后续关于"周末计划"的对话中说"考虑到你的宠物刚去世..."。
- 记忆膨胀: 创建过多低价值记忆（如"用户使用了感叹号"），导致记忆存储膨胀且检索效率下降。应设置严格的创建门槛。
- 记忆冲突: 存储了相互矛盾的记忆（如用户在不同时间表达了不同偏好），没有设计冲突解决策略。应采用"最新优先"或"显式覆盖"策略。
- 忽略记忆透明性: 用户不知道 AI 记住了什么、无法查看或删除记忆。应提供记忆管理界面。
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Just SKILL.md in memory-system of kangarooking/system-prompt-skills.
Open the folder on GitHubat commit 252cd52
Compare with similar skills
Memory System next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Memory System this skillkangarooking/system-prompt-skills | 205 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 6 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| Peft Fine TuningOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.8k | 13 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Memory System
What does Memory System do?
当需要为 AI 设计记忆存储、检索、应用和更新机制时调用此 skill。典型场景包括：设计持久化记忆架构（用户偏好、历史上下文、项目知识）、定义记忆的创建/读取/更新/删除生命周期、实现静默记忆应用（不在回复中透露记忆内容）、管理敏感记忆边界。. Memory System is an agent skill from kangarooking/system-prompt-skills.
When should I use Memory System?
Memory System fits situations like: 信号：AI 需要跨会话记住用户信息、记忆内容可能敏感、需要在回复中隐式应用记忆而非显式引用、用户要求记住这个.
How do I install Memory System in Claude Code?
Run `npx skills add kangarooking/system-prompt-skills --skill memory-system -a claude-code`. Or copy the skill folder (memory-system in kangarooking/system-prompt-skills) into .claude/skills/memory-system in your project. Claude Code loads it when a task matches its description.
How do I install Memory System in Codex?
Run `npx skills add kangarooking/system-prompt-skills --skill memory-system -a codex`. Or copy the skill folder (memory-system in kangarooking/system-prompt-skills) into .agents/skills/memory-system in your project. Codex loads it when a task matches its description.
Can I use Memory System in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kangarooking/system-prompt-skills --skill memory-system -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memory-system, .gemini/skills/memory-system, .github/skills/memory-system and .opencode/skills/memory-system in your project.
What does Memory System need to run?
SKILL.md names no scripts, command-line tools or credentials: Memory System is instructions for the agent only.
Does Memory System access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Memory System safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Memory System use?
Memory System is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Memory System use?
About 1.2k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Memory System?
Skills that share tags, products or a category with Memory System: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Memory System?
kangarooking (a GitHub user) maintains it in kangarooking/system-prompt-skills, which has 205 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on May 4, 2026.
Source: kangarooking/system-prompt-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.