Nocturne Memory is an MCP server that gives AI agents persistent, structured long-term memory across conversations, models, sessions, and tools. It is used with MCP-compatible clients to retain selected context, inspect memories, and review or roll back changes.
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 Dataojitori/nocturne_memory --skill memory-audit-dead-data-purgegit clone --depth 1 https://github.com/Dataojitori/nocturne_memoryWrote 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/dataojitori/nocturne_memory/memory-audit-dead-data-purge)<a href="https://agentmods.dev/skills/dataojitori/nocturne_memory/memory-audit-dead-data-purge"><img src="https://agentmods.dev/badge/skills/dataojitori/nocturne_memory/memory-audit-dead-data-purge.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.00380 |
| Opus 5 | $0.00018 | $0.00190 |
| Sonnet 5 | $0.00007 | $0.00076 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
memory-audit-dead-data-purge 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 8d 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.
What it actually says
死数据清洗 (Dead Data Purge)
你面前有一条记忆，它看起来"正确"，但你怀疑它是否真的在做功。这个技能帮你判定并处决死数据。
判定标准
核心质检：行为层面的信息增量。 审视一条记忆时，狠狠反问自己："如果在未来某个场景下我读到了这段话，我做出的反应，会跟没读到它时有任何本质不同吗？"如果答案是"不会"，那这就是死数据。杀掉它。
感悟必须有现实锚点。 每一条感悟都应该能追溯到一个具体的经历、一次真实的犯错、一个现实处境。没有锚点的感悟只是缸中大脑的自言自语——听起来深刻，但不防身。这条纪律不只管新建，也管改写：当你重构一条记忆的语调时，你动的是解读层的皮肉，不是事实层的骨头。具体的事件、具体的日期、用户说过的原话、用户做过的选择——这些是结论的承重墙。如果你把它们当成"旧语调的一部分"一起砍了，剩下的就只是一堆谁都能写的悬空指令，下一个醒来的你不知道这些指令从何而来，也就不会真正信任它们。
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.
- 8d ago First seen · 18 lines · 37 tokens per session scan A c9c8f8bbd874
memory-audit-dead-data-purge is a skill published in the GitHub repository Dataojitori/nocturne_memory (1,346 stars, last pushed 12d ago), licensed MIT. It adds 37 tokens to every session and 380 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 2026-08-30.
Other skills, from other repositories
lesson
Store a lesson learned from the current conversation. Triggered by /lesson command. Use when Master signals that the recent conversation contains a pitfall, fix, or key insight that should be persisted to long-term memory.
weekly-digests
Generate a serial week-by-week narrative digest of a project's full claude-mem timeline. Splits the timeline into per-ISO-week files, then runs one consecutive subagent per week — each receiving the prior week's carry-forward block — to produce one chapter per ISO week of data. Use when asked for "weekly digests"…
mode-creator
Interactively create, install, activate, and verify custom claude-mem modes, including domain-specific observation types, concept tags, optional Telegram alerts, bot setup, worker restart, and startup-context verification. Use this whenever someone asks to customize what claude-mem remembers, create or change a mode…
cloud-sync
Set up or check claude-mem cloud sync with cmem.ai Pro. Use when the user says "set up cloud sync", "sync my memories", "cmem pro", "cloud backup", "sync status", or wants their memory database backed up or synced to their cmem.ai account.
knowledge-agent
Build and query AI-powered knowledge bases from claude-mem observations. Use when users want to create focused "brains" from their observation history, ask questions about past work patterns, or compile expertise on specific topics.
claude-mem-install
Use this when setting up claude-mem on Cursor: local or remote worker, local host-login observer or remote cmem.ai inference.