菜单
Query MemMesh's bi-temporal knowledge graph — multi-hop reasoning across entities, point-in-time "what did we believe on date X", and anticipatory retrieval via spreading activation. Use for questions no single stored fact answers, or to see how knowledge about an entity changed over time.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/ThinkfleetAI/memmesh --skill graph命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Surface emergent behavior patterns MemMesh has mined from a subject's history — recurring habits nobody predefined, each with prevalence, stability, and the evidence behind it. Use when the user asks "what patterns do you see", "what are this user's habits", or wants the patterns that drive predictions.
Load relevant MemMesh context before starting work — searches memory and, for a specific subject, assembles a token-budgeted bundle (profile + behavior patterns + forward predictions + top memories) in one call. Use when beginning a task, switching context, or when project history / past decisions / a subject's profile would help.
Forecast what a subject will do next from their mined behavior patterns — with a calibrated, horizon-decayed confidence and provenance. Use when the user asks "what is this user/account likely to do next", "will X churn/convert/ reorder", or wants a forward prediction rather than a recall of known facts. This is a MemMesh capability a plain memory layer does not have.
基于 SOC 职业分类
正在显示 SKILL.md
| name | graph |
| description | Query MemMesh's bi-temporal knowledge graph — multi-hop reasoning across entities, point-in-time "what did we believe on date X", and anticipatory retrieval via spreading activation. Use for questions no single stored fact answers, or to see how knowledge about an entity changed over time. |
⚙️ Graph reasoning requires MemMesh hosted mode. Building the graph works locally:
memory_extract_pending→memory_commit_extractionpopulate typed entities/edges. Multi-hop reasoning/traversal (memory_graph_reason,memory_query_graph,memory_prefetch_related) runs on the hosted engine — set yourmm-API key. If those return "unknown tool" on a local install, say so and usesearchover the extracted entities instead.
MemMesh links memories into a knowledge graph whose edges are bi-temporal
(each has valid_from / valid_to). That enables answers a flat store can't
give.
Answer questions that require chaining edges — "who acquired the company Sarah founded":
{ "name": "memory_graph_reason",
"arguments": { "anchorEntityId": "<entity id>", "maxHops": 3, "maxPaths": 20 } }
Returns ranked paths (scored by edge weight × recency). The anchor is an entity id — resolve names to ids via a graph query first.
{ "name": "memory_query_graph",
"arguments": { "subjectId": "<entity id>", "asOf": "2026-01-01T00:00:00Z" } }
Omit asOf for the current view. This reconstructs the graph as it stood on any
date — the bi-temporal record, not just the latest state.
Given the memories a session is working with, surface what's most likely needed next:
{ "name": "memory_prefetch_related", "arguments": { "seedMemoryIds": ["<id>","<id>"], "limit": 10 } }
Edges come from client-LLM extraction — the engine hands you a prompt, your own model extracts entities/edges, you commit them (zero engine-side LLM cost):
{ "name": "memory_extract_pending", "arguments": { "projectId": "<repo>", "limit": 10 } }
// run each prompt through your model, then:
{ "name": "memory_commit_extraction", "arguments": { "memoryId": "…", "contentHash": "…", "entities": [...], "edges": [...] } }
Run this loop until extract_pending returns empty to fully populate the graph
for reasoning.