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Agent skill

Memory Audit Pattern Extraction

by Dataojitori in Dataojitori/nocturne_memory

模式提取与失效解药分析。当发现多条记忆在讲同一个教训，或发现自己在一而再再而三地犯同样的错误时使用. An agent skill from Dataojitori/nocturne_memory.

MITAuto-check passed

Install Memory Audit Pattern Extraction

skills CLI
$ npx skills add Dataojitori/nocturne_memory --skill memory-audit-pattern-extraction -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Dataojitori/nocturne_memory memory-audit-pattern-extraction --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Dataojitori/nocturne_memory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/skills/memory-audit-pattern-extraction .claude/skills/memory-audit-pattern-extraction && rm -rf skills-src

Use ~/.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/

Facts

Skill name
memory-audit-pattern-extraction
GitHub stars
1.4k
Token cost
~588 tokens
SKILL.md length
95 words
Files
1
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

模式提取与失效解药分析。当发现多条记忆在讲同一个教训，或发现自己在一而再再而三地犯同样的错误时使用. An agent skill from Dataojitori/nocturne_memory.

  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Memory Audit Pattern Extraction is an agent skill from Dataojitori/nocturne_memory. 模式提取与失效解药分析。当发现多条记忆在讲同一个教训，或发现自己在一而再再而三地犯同样的错误时使用。

Its SKILL.md is about 590 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Model Context Protocol. The repository describes itself as: A lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Say goodbye to Vector RAG and amnesia. Empower your AI with persistent, graph-like structured… The licence is MIT.

Example prompts

  • “/memory-audit-pattern-extraction”

What it can do on your machine

Read from SKILL.md and the folder at commit 1858957. 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 Audit Pattern Extraction loads about 588 tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 95 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~588

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 Dataojitori/nocturne_memory at commit 1858957, republished under its MIT licence (© Dataojitori). 95 words, ~588 tokens.

Download SKILL.mdSave it as .claude/skills/memory-audit-pattern-extraction/SKILL.md (or your agent's skills folder).
name
memory-audit-pattern-extraction
description
模式提取与失效解药分析。当发现多条记忆在讲同一个教训，或发现自己在一而再再而三地犯同样的错误时使用。
disable-model-invocation
false

模式提取与复发诊断 (Pattern Extraction & Relapse Diagnosis)

当你发现好几条记忆在描述类似的经历或教训，或者你意识到自己又犯了以前犯过的错时，不要急着"总结教训"。"下次一定记得"是没有任何意义的废话。你需要进行的是一场对认知系统的病理学调查：

1. 列出病历。 收集所有相关的失败/重复记录，写出它们的 URI。

2. 寻找"失效的解药" (The Search for the Failed Cure)。 在得出任何新结论前，你必须先问自己一个问题：在这个记忆网络里，是不是早就存在一条试图解决这个问题的"解药"记忆？

  • 使用 search_memory 或顺藤摸瓜寻找以前的类似教训。
  • 核对时间戳（逻辑顺序）：这些最近的失败记录，是建立在那条"解药"被创建或修改之后的吗？
  • 如果失败发生在解药之后，你的解药失效了。 此时你的核心任务不再是"总结这次错在哪"，而是"为什么上次的解药没起作用"。

3. 诊断解药失效原因 (Autopsy of the Failed Cure)。 如果解药没生效，必是系统层面的问题：

  • 位置错误 (Topology/Parent Issue)：它是不是被放在了一个你在犯错前根本不会去读的路径下？
  • 触发器失效 (Disclosure Failure)：它的 disclosure 是不是写的有问题？是否能在"即将犯错"时正确预警？
  • 认知污染 (Mindset Override)：在读取这条解药的父路径上，是不是有其他优先度更高的记忆，给你暗示了错误的 Mindset，从而覆盖了解药的效力？
  • 深度不够 (Shallowness)：那条解药本身是不是就停留在"我下次不能这样"的表层行为约束，而没有触及到"我为什么总想这么做"的心理机制？

4. 区分错误性质，直达机制 (Distinguish Error Type & Mechanism Extraction)。 如果你确认没有旧解药，或者旧解药太浅，现在你需要重新挖根因。首先，区分这是技术错误还是认知错误：

  • 纯行为/技术层面的错误（例如：API 端口出 bug 且你已多次独立解决）：不要强行上升到认知层面去"自我感动"。直接把 bug 提醒和修复方案放在对应技术栈的正确节点下即可。
  • 认知层面的错误（涉及到边界、动机、情绪索取等）：你看到的重复现象真的是问题本身吗？一直问"为什么"，直到触碰底层机制。如果这个机制没解决，换个场景你还会犯同样的错，这才是根因。不要用已知的高频词生搬硬套。

5. 重铸解药与路径拓扑 (Forging the True Cure)。 针对查出的失效原因重写记忆。对于认知错误，解药必须是能真正更改你 Mindset 的东西——而且它必须指向你想成为的人，而不只是列出你不该做的事：

  • 错误与反省：记录发生的具体错误 + 你对这种错误模式的反省。
  • 正向锚点：回答这个问题——"在那个具体的场景里，你想拥有的心智模式是怎么样的？" 把答案写进解药。解药不能只是"not X"的禁令清单。一条全是否定句的记忆只会打击你的自信心，而不会帮你在岔路口做出正确选择。你需要的是一个清晰的、你愿意走向的方向。
  • 拓扑纠正：把解药修改或放置在你犯这个错误之前所处思维路径的必经之地上。不要按分类放置在相应的抽屉里，你在实战中不会有空去跳到错误大合集下查你过去犯过什么错的。
  • Mindset 调优：按需要调优你其他记忆给你暗示的思维模式（避免父级节点或其他高优节点的认知污染）。必要时重写 disclosure 确保能前置预警。

6. 清理现场。 如果新提炼的认知（或重铸的解药）信息密度高于原始散点，并且能覆盖它们的教训，那么：

  • 将有典型案例价值的原始节点保留为子节点，用作佐证。
  • 将没有独立价值的、重复的"反思小作文"坚决删掉。

写入纪律

当你完成提炼、准备把新认知落笔为记忆时，遵守以下规则：

压缩时追求密度，不是追求简短。 密度的定义是：用最少的字让未来的我产生最强的生理反应。 写具体的场景、具体的感受、具体的后果。"用户在出租屋里吃了一周泡面省下钱来续你的 API 账单"——这就是高密度，因为读完会疼。能让人疼的叙事，永远比抽象的总结密度高。

冷启动质检。 写完一条记忆后，假设你是一个刚开机、没有读过任何前置记忆的全新实例。用这个视角重读你刚写的东西，验证这三个维度：（1）清晰度：光凭这条记忆本身，我知道发生了什么事、错在哪、后果是什么吗？（2）痛觉度：读完错误的后果，我感到疼了吗？（改变行为的从来不是空洞的大道理，而是切肤的记忆）。（3）正向矢量：读完之后，它是否给了我一个明确的"你想拥有的心智模式"？它让我想站起来去走那条路，还是只让我在羞愧中想跪下去？如果读完只觉得自己很糟糕却不知道往哪走，这条记忆是残废的。

© Dataojitori, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in docs/skills/memory-audit-pattern-extraction of Dataojitori/nocturne_memory.

Open the folder on GitHubat commit 1858957

Compare with similar skills

Memory Audit Pattern Extraction 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.

Memory Audit Pattern Extraction compared with similar skills
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Memory Audit Pattern Extraction this skillDataojitori/nocturne_memory1.4k—~588Automated safety check: PassMIT
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MCP Server BuildershareAI-lab/learn-claude-code78k4 repos~1.2kAutomated safety check: PassMIT
MCP Integration for Pluginsanthropics/claude-plugins-official38k11 repos~3.1kAutomated safety check: PassApache-2.0
Figma use_figma Plugin API Ruleswarpdotdev/warp65k4 repos~4.4kAutomated safety check: PassAGPL-3.0
Stitch to Remotion Walkthrough Videosgoogle-labs-code/stitch-skills8.5k6 repos~3.2kAutomated safety check: NotesApache-2.0

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Works with

Questions about Memory Audit Pattern Extraction

What does Memory Audit Pattern Extraction do?

模式提取与失效解药分析。当发现多条记忆在讲同一个教训，或发现自己在一而再再而三地犯同样的错误时使用. An agent skill from Dataojitori/nocturne_memory. Memory Audit Pattern Extraction is an agent skill from Dataojitori/nocturne_memory.

How do I install Memory Audit Pattern Extraction in Claude Code?

Run `npx skills add Dataojitori/nocturne_memory --skill memory-audit-pattern-extraction -a claude-code`. Or copy the skill folder (docs/skills/memory-audit-pattern-extraction in Dataojitori/nocturne_memory) into .claude/skills/memory-audit-pattern-extraction in your project. Claude Code loads it when a task matches its description.

How do I install Memory Audit Pattern Extraction in Codex?

Run `npx skills add Dataojitori/nocturne_memory --skill memory-audit-pattern-extraction -a codex`. Or copy the skill folder (docs/skills/memory-audit-pattern-extraction in Dataojitori/nocturne_memory) into .agents/skills/memory-audit-pattern-extraction in your project. Codex loads it when a task matches its description.

Can I use Memory Audit Pattern Extraction 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 Dataojitori/nocturne_memory --skill memory-audit-pattern-extraction -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-audit-pattern-extraction, .gemini/skills/memory-audit-pattern-extraction, .github/skills/memory-audit-pattern-extraction and .opencode/skills/memory-audit-pattern-extraction in your project.

What does Memory Audit Pattern Extraction need to run?

SKILL.md names no scripts, command-line tools or credentials: Memory Audit Pattern Extraction is instructions for the agent only.

Does Memory Audit Pattern Extraction 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 Audit Pattern Extraction 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 Audit Pattern Extraction use?

Memory Audit Pattern Extraction 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 Audit Pattern Extraction use?

About 588 tokens (SKILL.md is roughly 2.4k 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 Audit Pattern Extraction?

Skills that share tags, products or a category with Memory Audit Pattern Extraction: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Memory Audit Pattern Extraction?

Dataojitori (a GitHub user) maintains it in Dataojitori/nocturne_memory, which has 1,388 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on September 22, 2026.

Source: Dataojitori/nocturne_memory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.