菜单
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.
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 behaviors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。
正在显示 SKILL.md
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.
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.
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
| name | behaviors |
| description | 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. |
⚙️ Requires MemMesh hosted mode. Calibrated prediction and behavior discovery run on the hosted engine — set your
mm-API key. On a local / open-source install these tools (memory_predict,memory_build_context) are not registered; if a call returns "unknown tool", tell the user this is a hosted capability and fall back tosearch/recallfor what's already known.
Show the patterns MemMesh discovered on its own. These behavior_pattern
memories are what predict projects forward — inspecting them explains the
forecasts.
{ "name": "memory_search",
"arguments": { "type": "behavior_pattern", "projectId": "<repo>", "limit": 50 } }
Or scope to one subject and read them out of the context bundle:
{ "name": "memory_build_context",
"arguments": { "subjectKind": "user", "subjectId": "<id>", "include": ["patterns"] } }
The discovery pass that finds patterns nobody predefined runs on the SDK:
const behaviors = await memory.behaviors.discover({ projectId: "myapp" });
// each: { pattern, prevalence, stability, evidenceMemoryIds }
For each pattern show: the behavior, how often it holds (prevalence), how stable it is over time (stability), and a couple of evidence memories. Rank by stability × prevalence — the strongest, most reliable habits first.
A vector-recall memory layer can only return facts you already stated. MemMesh
derives structure — "books gym classes on Mondays", "reorders ~every 6 weeks" —
from raw observations. That derived structure is the input to predict and the
reason the predictions have provenance.