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Use when wiring honcho() from @tanstack/ai-memory/honcho — a hosted memory adapter where recall is a dialectic answer over the user's representation (no discrete fragments). Requires the optional @honcho-ai/sdk peer.
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/TanStack/ai --skill tanstack-ai-memory-honcho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。
Entry point for TanStack AI skills. Routes to chat-experience, tool-calling, media-generation, structured-outputs, adapter-configuration, ag-ui-protocol, middleware, locks, custom-backend-integration, and debug-logging, plus the skills shipped by companion packages (@tanstack/ai-persistence, @tanstack/ai-code-mode). Use chat() not streamText(), openaiText() not createOpenAI(), toServerSentEventsResponse() not manual SSE, middleware hooks not onEnd callbacks.
Portable Agent Skills (SKILL.md) for TanStack AI with @tanstack/ai-skills. Renders a skill catalog and a load_skill tool via the withSkills middleware so any tool-calling model loads skills on demand, on any provider. Covers the SkillSource interface, inlineSkill/skillDirectory/staticSkills, the aggregate/ dedupe/filter/cache combinators, read_skill_resource, and the conformance suite. Use for provider-agnostic runtime skills — NOT hosted provider skills (codeExecutionTool/shellTool), which run in a provider sandbox.
does the alpha thing
基于 SOC 职业分类
正在显示 SKILL.md
| name | tanstack-ai-memory-honcho |
| description | Use when wiring honcho() from @tanstack/ai-memory/honcho — a hosted memory adapter where recall is a dialectic answer over the user's representation (no discrete fragments). Requires the optional @honcho-ai/sdk peer. |
Hosted recall/save adapter backed by Honcho. Honcho models memory as peers
exchanging messages in a session; recall returns a synthesized dialectic answer
over the user peer's representation (so there are no discrete fragments), and save
appends the turn's messages to the session.
import { memoryMiddleware } from '@tanstack/ai-memory'
import { honcho } from '@tanstack/ai-memory/honcho'
const memory = honcho({ user: currentUserId }) // baseURL defaults to HONCHO_URL
memoryMiddleware({ adapter: memory, scope })
@honcho-ai/sdk is an optional peer dependency, loaded lazily on first use — install
it where you use honcho().
user — user peer id (falls back to scope.userId, then 'demo-user').baseURL — Honcho server URL (default HONCHO_URL or http://localhost:8001).workspaceId — default HONCHO_APP_NAME or 'ai-memory'.apiKey — default HONCHO_API_KEY.assistantId — assistant peer id (default 'assistant').Scope fields: session key = {tenantId|_}__{threadId}; peer id is
{tenantId}__{user} when tenantId is set, otherwise user / scope.userId.
namespace is ignored.
recall calls the user peer's dialectic chat() and injects the answer as the system
prompt; Honcho exposes no LLM tools.