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/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/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="Reviewed on agentmods" width="80" 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 | $0.00655 |
| Opus 5.5 | $0.00015 | $0.00262 |
| Sonnet 5.5 | $0.00007 | $0.00131 |
| Haiku 4.5 | $0.00004 | $0.00065 |
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 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.
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。
区分观察与推断
一条记忆可以同时记录外部观察和我的解释，但不能让解释冒充观察。
- 观察尽量保留出处与时间：“某日用户说了什么”“某次测试返回什么”，而不是把一次结果写成永久成立的事实。引用原话前先核对原文；转述不能加引号伪装成原话。
- 对推断追问：“如果这段解释是错的，我将从哪里知道？”一份观察若被扩写成多层未经验证的机制、动机和结论，就先剥掉这些解释层，不必连同原始证据一起删除。
- 暂时有用但尚未证实的推断，明确标记为推测，写出依据和适用边界；不要把它当成已验证的结论来否决其他可能性。
如果节点通篇是无主语的“必然规律”或禁令，先找出它来自哪次具体经历，再还原为“我观察到什么、我当时怎么判断、我现在如何看待”。找不到现实依据、读后也不改变行为的漂亮话，才是该清理的死数据。
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 · +12 lines 4b2e8f23c83d
- 1mo 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 (1,408 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 655 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 2026-08-30.
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.
ccs-align
Run the CCS Align seat's hourly breathing cycle — prove the local claude-mem worker is healthy, pull needle observations through search → timeline → getobservations, land them in a seat-owned middle cache via atomic grab → append → filter exclude-marks → replace, manage exclude marks, and walk house → project → seat…
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"…
handoff
Generate a HANDOFF.md that captures goal, current state, files touched, failed attempts, and next steps — so a fresh Claude session can continue exactly where this one left off. Use when sessions are getting long, Claude keeps retrying the same broken solution, or the user wants to step away and resume later.
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.
screenpipe
Search screen and audio history recorded by Screenpipe and optionally save selected context into claude-mem. Use when the user asks to recall desktop activity, a meeting, or something seen in another app through Screenpipe.