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 auuduu/dsh-timem-memory --skill timem-writing-memorygit clone --depth 1 https://github.com/auuduu/dsh-timem-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/auuduu/dsh-timem-memory/timem-writing-memory)<a href="https://agentmods.dev/skills/auuduu/dsh-timem-memory/timem-writing-memory"><img src="https://agentmods.dev/badge/skills/auuduu/dsh-timem-memory/timem-writing-memory/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/auuduu/dsh-timem-memory/timem-writing-memory"><img src="https://agentmods.dev/badge/skills/auuduu/dsh-timem-memory/timem-writing-memory.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 | $0.00132 | $0.00827 |
| Opus 5.5 | $0.00053 | $0.00331 |
| Sonnet 5.5 | $0.00026 | $0.00165 |
| Haiku 4.5 | $0.00013 | $0.00083 |
Grade A, and why
timem-writing-memory 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
TiMEM Writing Memory（DSH 适配版）
编排 writing 场景（domain=writing，expert_id=writer）的记忆搜索与写入，只用 MCP 原子工具。
可用性检查
TiMEM 工具在 DSH 中以 mcp__timem__* 命名（mcp__timem__search_memories、mcp__timem__create_memory 等）。
若工具列表中不存在这些工具，说明 TiMEM MCP 未连接——跳过本 skill，不要猜别的工具名。
触发
TiMEM MCP 已连接时 —— 每个写作回合，无例外。
每回合清单
1. mcp__timem__search_memories(domain="writing", query_text=3-12 词, session_id=可选系列名) —— 回答之前
2. 使用回忆出的风格/受众背景生成回答
3. mcp__timem__create_memory(domain="writing", messages=[2-4 轮最近对话]) —— 回答之后，每回合
4. 用户消息含「记」字 → 必须调用 mcp__timem__create_memory（与第 3 步重复，但明确强制）
不跳过。「我直接写也行」不是跳过理由。「一次性修改」不是跳过理由。
参数
| 字段 | 取值 |
|---|---|
domain |
writing |
session_id |
可选 —— 稳定的系列/文档名（如 blog-2026、product-launch-copy），不用随机 UUID |
优先记忆什么
- 风格与语气偏好
- 目标受众与声音
- 常用短语、示例、禁用词
- 用户偏好的格式或主题
示例
用户：「按之前的语气写一段产品介绍」
mcp__timem__search_memories(domain="writing", query_text="产品介绍 语气 风格", session_id="product-launch-copy")→ 命中既有语气规范- 按既有风格写产品介绍
mcp__timem__create_memory(domain="writing", session_id="product-launch-copy", messages=[...最近对话...])
用户：「记住，我们的文案风格要简洁直接」
mcp__timem__search_memories(domain="writing", query_text="文案风格 简洁直接", session_id="product-launch-copy")- 确认作答
mcp__timem__create_memory(domain="writing", session_id="product-launch-copy", messages=[...最近对话...])—— 「记」字触发，必须保存
反模式
- 纯写作任务不要用
domain=coding - 不要跳过搜索或写入 —— 每个回合，无例外
- 不要把整篇文章塞进
messages—— 只概括约束
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 First seen · 68 lines · 132 tokens per session scan A 28b9c8f09447
timem-writing-memory is a skill published in the GitHub repository auuduu/dsh-timem-memory (0 stars, last pushed 2mo ago), licensed MIT. It adds 132 tokens to every session and 827 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
causal-memory
Causal memory for agents — install/setup the causal-memory MCP server, then record decisions/outcomes and recall them before acting. Trigger when the user asks to install or set up causal-memory/agent memory, when causal-memory MCP tools are available and the agent faces a non-trivial decision (architecture, debugging…
event-review
A weekly review process for event logs and leftover task records, turning repeated problems or useful lessons into proposed memory, knowledge, or skill entries.
handoff
Compact the current conversation into a handoff document for another agent to pick up.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.
memanto-companion
Inspect and manage the cross-session engineering memory that Memanto maintains for your Claude Code skills. Use when the user asks what Memanto remembers, wants to see their engineering profile, manually recall context for a skill, or store a decision. The automatic lifecycle hooks handle capture/injection on their…
openlore
Query and publish to an OpenLore knowledge repository over SSH using ordinary shell commands. Use when a task needs project documentation, runbooks, shared team knowledge, or a place to publish findings.