Borrowing it
Nothing to install: this file belongs to u9401066/asset-aware-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/u9401066/asset-aware-mcp/main/.claude/skills/memory-updater/SKILL.mdgit clone --depth 1 https://github.com/u9401066/asset-aware-mcpWrote 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/u9401066/asset-aware-mcp/memory-updater)<a href="https://agentmods.dev/skills/u9401066/asset-aware-mcp/memory-updater"><img src="https://agentmods.dev/badge/skills/u9401066/asset-aware-mcp/memory-updater/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/u9401066/asset-aware-mcp/memory-updater"><img src="https://agentmods.dev/badge/skills/u9401066/asset-aware-mcp/memory-updater.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 | $0.00053 | $0.00290 |
| Opus 5 | $0.00026 | $0.00145 |
| Sonnet 5 | $0.00011 | $0.00058 |
| Haiku 4.5 | $0.00005 | $0.00029 |
Grade A, and why
memory-updater 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.
This is a copy
100% identical to memory-updater — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Memory Bank 更新技能
描述
維護和更新專案的 Memory Bank 記憶系統。
觸發條件
- 「更新 memory bank」
- 「記錄進度」
- 「更新上下文」
- 工作階段結束時
更新的檔案
activeContext.md
當前工作焦點，包含：
- 正在處理的任務
- 相關檔案
- 待解決問題
progress.md
進度追蹤：
- Done: 已完成項目
- Doing: 進行中
- Next: 下一步
decisionLog.md
重要決策記錄：
- 決策內容
- 原因/理由
- 日期
更新原則
- 增量更新：只新增/修改相關內容
- 保持簡潔：避免冗餘描述
- 時間標記：重要項目加上日期
- 關聯性：標記相關檔案和決策
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 · 43 lines · 53 tokens per session scan A 249f84828832
memory-updater is a skill published in the GitHub repository u9401066/asset-aware-mcp (0 stars, last pushed 22d ago), licensed Apache-2.0. It adds 53 tokens to every session and 290 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to memory-updater, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
rag-query
Query the personal LightRAG knowledge graph for persistent memory across sessions.
rag-remember
Store a fact, decision, or observation into the personal LightRAG knowledge graph immediately.
rag-sync
End-of-session review and sync of learnings to the personal LightRAG knowledge graph.
query-memory
A memory-search workflow for finding information from earlier conversations. It can search summaries, retrieve original messages, count messages, and look up logged emotional changes.
lemmalog
Externalize working memory and logical state into the lemmalog Datalog engine (MCP). Use for ANY multi-step task where state should outlive one context window or span agents: long investigations, debugging sessions, audits, multi-agent searches, systematic explorations, planning with many interdependent constraints…
braindb-agent
Persistent memory across sessions via the BrainDB agent. Use at conversation start and whenever you need to recall what you know about the user or save new information to long-term memory.