菜单
Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes. Use on first run in a repo, when the API key changes, or to re-run setup after config changes.
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 onboard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
| name | onboard |
| description | Set up MemMesh for a new project — verify the MCP server is wired, pick local vs hosted, import any existing project knowledge (MEMORY.md, CLAUDE.md, a mem0 export), and seed initial scopes. Use on first run in a repo, when the API key changes, or to re-run setup after config changes. |
Get a project ready to use MemMesh.
memmesh doctor # binary + MCP config + skill presence
If the MCP server isn't connected, run npx @thinkfleet/memmesh install (see the
memmesh-cli skill), then re-check.
MEMMESH_API_KEY / sync.url in
~/.thinkfleet-memory/config.toml. Needed for cross-device sync, the full SDK,
and server-side prediction/verticals.If the repo already has durable context, import it (see the import skill):
MEMORY.md, CLAUDE.md, an ADR folder, or a mem0 export (then consider
memmesh-migrate for a full switch). Feed each item via memory_observe so the
engine extracts + builds the graph, scoped project.
Run stats to show what's now in scope, and remind the user of the two-rule
loop: the agent will observe their messages and recall at session start
automatically (the always-on memmesh skill). Nothing else to configure.
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.
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.
基于 SOC 职业分类
