Agent skill

Stello Agent Creation

by stello-agent in stello-agent/stello

StelloAgent 创建配置教程。完整说明 createStelloAgent 的每个配置项,包含 sessionDefaults、storage、tools、skills、forkProfiles、session 层接入、orchestration 等。

Apache-2.0Auto-check passed

Install Stello Agent Creation

skills CLI
$ npx skills add stello-agent/stello --skill stello-agent-creation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install stello-agent/stello stello-agent-creation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/stello-agent/stello.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/stello-agent-creation .claude/skills/stello-agent-creation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
stello-agent-creation
GitHub stars
112
Token cost
~3.2k tokens
SKILL.md length
280 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

StelloAgent 创建配置教程。完整说明 createStelloAgent 的每个配置项,包含 sessionDefaults、storage、tools、skills、forkProfiles、session 层接入、orchestration 等。

  • Works in 8 steps: storage —— Orchestrator-facing 数据 SDK → sessionDefaults —— Agent 级默认配置 → capabilities — 能力注入 → …
  • SKILL.md covers 最小可用示例, 配置结构总览, 1. storage ——… and 2. sessionDefaults —— Agent…, plus 6 more sections
  • Needs OPENAI_API_KEY

What it does

Stello Agent Creation is an agent skill from stello-agent/stello. StelloAgent 创建配置教程。完整说明 createStelloAgent 的每个配置项,包含 sessionDefaults、storage、tools、skills、forkProfiles、session 层接入、orchestration 等。

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Conversations aren't linear — why should AI chats be? The first open-source conversation topology engine. Auto-branching session trees, inherited memory, star-map visualization… The licence is Apache-2.0.

Example prompts

  • “/stello-agent-creation”

Requirements

  • A credential in OPENAI_API_KEY

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. storage —— Orchestrator-facing 数据 SDK
  2. sessionDefaults —— Agent 级默认配置
  3. capabilities — 能力注入
  4. session — Session 层接入
  5. orchestration — 编排策略(可选)
  6. 完整配置示例
  7. 自行实现 reflection 循环
  8. 运行时使用

What it can do on your machine

Read from SKILL.md and the folder at commit 3bc9493. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENAI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Stello Agent Creation loads about 3.2k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 280 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from stello-agent/stello at commit 3bc9493, republished under its Apache-2.0 licence (© stello-agent). 280 words, ~3,247 tokens.

Download SKILL.mdSave it as .claude/skills/stello-agent-creation/SKILL.md (or your agent's skills folder).
name
stello-agent-creation
description
StelloAgent 创建配置教程。完整说明 createStelloAgent 的每个配置项,包含 sessionDefaults、storage、tools、skills、forkProfiles、session 层接入、orchestration 等。

StelloAgent 创建配置教程

最小可用示例

typescript
import {
  createStelloAgent,
  ToolRegistryImpl,
  SkillRouterImpl,
  type EngineLifecycleAdapter,
  type ConfirmProtocol,
  type SessionTree,
} from '@stello-ai/core'
import type { SessionStorage } from '@stello-ai/session'

const agent = createStelloAgent({
  sessions,                       // SessionTree 实例(拓扑)
  storage: sessionStorage,        // SessionStorage 实例(内容;orchestrator-facing SDK 依赖)
  capabilities: {
    lifecycle,                    // EngineLifecycleAdapter
    tools: new ToolRegistryImpl(),
    skills: new SkillRouterImpl(),
    confirm: { ... },
  },
  session: {
    sessionLoader: async (id) => ({ session: loadedSession, config: null }),
  },
})

配置结构总览

typescript
interface StelloAgentConfig {
  sessions: SessionTree                          // 拓扑树(必填)
  storage?: SessionStorage                       // 内容存储(orchestrator-facing 数据 SDK 依赖)
  sharedMemory?: SharedMemoryStore               // Agent 级共享 memory;注入后索引每 send 前由 adapter 自动注入
  sessionDefaults?: SessionConfig                // 所有 session 的 agent 级默认(fork 合成链最低优先级)
  capabilities: {                                // 能力注入(必填)
    lifecycle: EngineLifecycleAdapter
    tools: EngineToolRuntime                     // 用户自定义工具
    skills: SkillRouter                          // Skill 注册表
    confirm: ConfirmProtocol
    profiles?: ForkProfileRegistry               // Fork 模板(可选)
  }
  session?: StelloAgentSessionConfig             // Session 层接入(可选)
  runtime?: StelloAgentRuntimeConfig             // Runtime 策略(可选)
  orchestration?: StelloAgentOrchestrationConfig // 编排策略(可选)
}

所有 Session 共用 sessionDefaults。Root 没有专属配置,与子 session 走同一套 fork 合成链(详见 fork-design)。 "全 memory → 反思 → 定向 insight" 的循环由应用层基于 orchestrator-facing SDK 自行实现(不在配置注入点里)。


1. storage —— Orchestrator-facing 数据 SDK

storage: SessionStorage 注入后,StelloAgent 暴露以下 data-IO 方法(详见 stello-agent-usage):

  • listSessionDigests(filter?) —— 批量收集所有 Session 的 { id, label, status, memory, insight }
  • getSessionMetadata(id) —— 单个 Session 的 { memory, insight }
  • listMessages(id, options?) —— 读取指定 Session 的 L3 消息
  • putMemory(id, content) / putInsight(id, content) / clearInsight(id)

重要:应用层需保证 sessions(拓扑)与 storage(内容)指向同一份后端——SessionTree.listAll() 返回的 id 必须能在 SessionStorage 上 getMemory。

未注入 storage 时,上述方法会抛错;其余编排能力(turn / stream / fork / archive)不受影响。


2. sessionDefaults —— Agent 级默认配置

所有 Session 的配置基线,fork 合成链的最低优先级层。

typescript
createStelloAgent({
  sessionDefaults: {
    llm: defaultLlm,                       // 默认 LLM
    consolidateFn: defaultConsolidateFn,   // 默认 L3→memory 提炼函数
    compressFn: defaultCompressFn,         // 默认上下文压缩函数
    systemPrompt: '你是一个助手。',        // 默认 system prompt(可被 fork 覆盖)
    skills: undefined,                     // undefined = 继承全局 SkillRouter(默认)
  },
  // ...
})

SessionConfig 完整字段:

typescript
interface SessionConfig {
  systemPrompt?: string
  llm?: LLMAdapter
  tools?: LLMCompleteOptions['tools']
  skills?: string[]          // undefined=继承全局;[]=禁用所有 skill;['a','b']=白名单
  consolidateFn?: SessionCompatibleConsolidateFn
  compressFn?: SessionCompatibleCompressFn
}

Root session 的固化配置由你创建 root 时通过 agent.createSession({ label }) + 后续 sessions.putConfig(rootId, ...) 设置——或在 sessionDefaults 给出全局默认即可。Root 没有特殊待遇。


3. capabilities — 能力注入

3.1 tools — 用户自定义工具
typescript
import { ToolRegistryImpl } from '@stello-ai/core'

const toolRegistry = new ToolRegistryImpl()

toolRegistry.register({
  name: 'save_note',
  description: '保存笔记到当前会话',
  parameters: {
    type: 'object',
    properties: {
      note: { type: 'string', description: '笔记内容' },
    },
    required: ['note'],
  },
  execute: async (args, _ctx) => {
    await db.saveNote(String(args.note))
    return { success: true, data: { saved: true } }
  },
})

要点:

  • parameters 是 JSON Schema 格式,LLM 据此生成参数
  • execute(args, ctx) 返回 { success: true, data: ... } 或 { success: false, error: '...' }
  • tool 执行失败时 Engine 自动将 error 作为 tool result 返回给 LLM,不中断对话
  • 内置 tool(stello_create_session / activate_skill)需要在 ToolRegistryImpl([...]) 构造时显式 opt-in(参考 createSessionTool() / activateSkillTool(skills) factory)
3.2 skills — Skill 注册表

Skill 是两级渐进式加载的 prompt 片段:LLM 始终看到 name + description,主动调用 activate_skill 后注入完整 content。

typescript
import { SkillRouterImpl, loadSkillsFromDirectory } from '@stello-ai/core'

const skillRouter = new SkillRouterImpl()

// 方式一:代码注册
skillRouter.register({
  name: 'code-review',
  description: '代码审查专家,激活后按标准流程审查代码质量',
  content: `你现在是代码审查专家。...`,
})

// 方式二:从目录批量加载(标准 agent skills 格式)
const fileSkills = await loadSkillsFromDirectory('./skills')
for (const skill of fileSkills) {
  skillRouter.register(skill)
}

activate_skill 是否对 LLM 可见取决于 skills.getAll().length > 0,以及该 session 的 SessionConfig.skills 白名单(详见 fork-design 的 skills 三态语义)。

3.3 profiles — Fork Profile 注册表(可选)

ForkProfile 是预定义的 fork 配置模板,extends SessionConfig。LLM 调用 stello_create_session 时可通过 profile 参数引用。

typescript
import { ForkProfileRegistryImpl } from '@stello-ai/core'

const forkProfiles = new ForkProfileRegistryImpl()

forkProfiles.register('poet', {
  systemPrompt: '你是一位诗人。所有回复必须用诗歌形式。',
  systemPromptMode: 'preset',
})

forkProfiles.register('region-expert', {
  systemPromptFn: (vars) => `你是${vars.region}地区的留学专家。`,
  systemPromptMode: 'preset',
  llm: cheaperLlmAdapter,
  skills: ['search', 'summarize'],
  consolidateFn: researchConsolidateFn,
})

forkProfiles.register('researcher', {
  systemPrompt: '你是研究助手,善于深入分析。',
  systemPromptMode: 'prepend',
  context: 'inherit',
})

完整字段、合成规则、systemPromptMode 三种模式见 skill fork-design。

3.4 lifecycle — 生命周期适配器
typescript
const lifecycle: EngineLifecycleAdapter = {
  bootstrap: async (sessionId) => ({
    context: await memory.assembleContext(sessionId),
    session: await sessions.get(sessionId),
  }),

  afterTurn: async (sessionId, userMsg, assistantMsg) => {
    await memory.appendRecord(sessionId, userMsg)
    await memory.appendRecord(sessionId, assistantMsg)
    return { coreUpdated: false, memoryUpdated: false, recordAppended: true }
  },
}
3.5 confirm — 确认协议
typescript
const confirm: ConfirmProtocol = {
  async confirmSplit(proposal) {
    return agent.forkSession(proposal.parentId, {
      label: proposal.suggestedLabel,
    })
  },
  async dismissSplit() {},
  async confirmUpdate() {},
  async dismissUpdate() {},
}

4. session — Session 层接入

StelloAgentSessionConfig 是纯 I/O 数据加载,按 ID 加载 Session 实例与其固化配置。

typescript
session: {
  sessionLoader: async (sessionId) => {
    const session = await loadSession(sessionId, {
      storage: sessionStorage,
      llm: currentLlm,
    })
    if (!session) throw new Error(`Session not found: ${sessionId}`)
    return {
      session,        // SessionCompatible 实例
      config: null,   // SerializableSessionConfig | null
    }
  },

  // 可选:自定义 send() 结果序列化(默认 JSON)
  serializeSendResult: (result) => JSON.stringify(result),

  // 可选:自定义 tool call 解析器(默认 sessionSendResultParser)
  toolCallParser: customParser,
}

所有 Session(含 root)由同一个 sessionLoader 按 id 加载。Root 与子 session 的差异只在拓扑 (TopologyNode.parentId === null),loader 无需区分。

两种 session 接入方式:

方式配置适用场景
Session 适配session.sessionLoader使用 @stello-ai/session 包(推荐)
直接提供 runtimeruntime.resolver自定义 session 实现

5. orchestration — 编排策略(可选)

consolidateEveryNTurns
typescript
orchestration: {
  consolidateEveryNTurns: 5,
}

每 5 轮自动 consolidate(fire-and-forget)。

splitGuard
typescript
import { SplitGuard } from '@stello-ai/core'

orchestration: {
  splitGuard: new SplitGuard(sessions, {
    minTurns: 3,
    cooldownTurns: 5,
  }),
}
hooks
typescript
orchestration: {
  hooks: {
    onRoundStart({ sessionId, input }) {},
    onRoundEnd({ sessionId, turn }) {},
    onSessionFork({ parentId, child }) {},
    onToolCall({ sessionId, toolCall }) {},
    onError({ source, error }) {},
  },
}

所有 hooks fire-and-forget:抛错时 emit error 事件,不中断对话。


6. 完整配置示例

typescript
import {
  createStelloAgent,
  ToolRegistryImpl,
  SkillRouterImpl,
  ForkProfileRegistryImpl,
  SplitGuard,
  SessionTreeImpl,
  NodeFileSystemAdapter,
  InMemorySharedMemoryStore,
  createSessionTool,
  activateSkillTool,
  memoryRecallTool,
  memoryRememberTool,
  memoryForgetTool,
} from '@stello-ai/core'
import {
  loadSession,
  InMemoryStorageAdapter,
  createOpenAICompatibleAdapter,
} from '@stello-ai/session'

// ─── 基础设施 ───
const fs = new NodeFileSystemAdapter('./data')
const sessions = new SessionTreeImpl(fs)
const sessionStorage = new InMemoryStorageAdapter()
const sharedMemory = new InMemorySharedMemoryStore()
const llm = createOpenAICompatibleAdapter({
  apiKey: process.env.OPENAI_API_KEY!,
  model: 'gpt-4o',
})

// ─── 自定义 Tools(含 opt-in 内置 tool) ───
const skills = new SkillRouterImpl()
skills.register({
  name: 'data-analysis',
  description: '数据分析模式:激活后按结构化流程分析数据',
  content: '你是数据分析专家...',
})

const toolRegistry = new ToolRegistryImpl([
  createSessionTool(),                  // 内置 fork tool(opt-in)
  activateSkillTool(skills),            // 内置 skill 激活 tool(opt-in)
  memoryRecallTool(),                   // 共享 memory 读取 tool(opt-in)
  memoryRememberTool(),                 // 共享 memory 写入 tool(opt-in)
  memoryForgetTool(),                   // 共享 memory 删除 tool(opt-in)
])
toolRegistry.register({
  name: 'search_knowledge',
  description: '搜索知识库',
  parameters: {
    type: 'object',
    properties: { query: { type: 'string' } },
    required: ['query'],
  },
  execute: async (args, _ctx) => ({
    success: true,
    data: await knowledgeBase.search(String(args.query)),
  }),
})

// ─── Fork Profiles ───
const profiles = new ForkProfileRegistryImpl()
profiles.register('researcher', {
  systemPrompt: '你是研究助手,善于深入分析。',
  systemPromptMode: 'prepend',
  context: 'inherit',
  skills: ['search', 'data-analysis'],
})

// ─── 创建 Agent ───
let agent: ReturnType<typeof createStelloAgent>
agent = createStelloAgent({
  sessions,
  storage: sessionStorage,            // 注入内容存储,启用 orchestrator-facing SDK
  sharedMemory,                       // 注入共享 memory,启用 4 个 SDK 方法 + 索引自动注入

  sessionDefaults: {
    llm,
    systemPrompt: '你是一个助手。',
    // consolidateFn / compressFn 由应用层闭包注入
  },

  session: {
    sessionLoader: async (sessionId) => {
      const session = await loadSession(sessionId, { storage: sessionStorage, llm })
      if (!session) throw new Error(`Session not found: ${sessionId}`)
      return { session, config: null }
    },
  },

  capabilities: {
    lifecycle: {
      bootstrap: async (sessionId) => ({
        context: { core: {}, memories: [], currentMemory: null, scope: null },
        session: await sessions.get(sessionId),
      }),
      afterTurn: async () => ({ coreUpdated: false, memoryUpdated: false, recordAppended: true }),
    },
    tools: toolRegistry,
    skills,
    profiles,
    confirm: {
      confirmSplit: async (p) => agent.forkSession(p.parentId, { label: p.suggestedLabel }),
      dismissSplit: async () => {},
      confirmUpdate: async () => {},
      dismissUpdate: async () => {},
    },
  },

  orchestration: {
    consolidateEveryNTurns: 5,
    splitGuard: new SplitGuard(sessions, { minTurns: 3, cooldownTurns: 5 }),
    hooks: {
      onSessionFork({ parentId, child }) {
        console.log(`Fork: ${parentId} → ${child.id} (${child.label})`)
      },
    },
  },
})

// ─── 创建 root session ───
const root = await agent.createSession({ label: 'Main' })

// ─── 开始对话 ───
await agent.enterSession(root.id)
const result = await agent.turn(root.id, '帮我分析一下市场趋势')
console.log(result.turn.finalContent)

7. 自行实现 reflection 循环

"全 memory → 反思 → 定向 insight" 的循环由应用层实现:

typescript
import { collectLLMStream } from '@stello-ai/session'

async function reflect(agent: StelloAgent, llm: LLMAdapter): Promise<void> {
  const digests = await agent.listSessionDigests({ status: 'active' })
  const reflection = await collectLLMStream(llm.stream([
    { role: 'system', content: '你是 orchestrator,请综合各 session 的 memory,对需要纠偏/补充信息的 session 写出 insight。' },
    { role: 'user', content: JSON.stringify(digests) },
  ]))

  // 解析 reflection 输出(自定义 schema),调用 putInsight 定向回写
  const { insights } = JSON.parse(reflection.content ?? '{}') as { insights: Record<string, string> }
  await Promise.all(
    Object.entries(insights).map(([sessionId, content]) =>
      agent.putInsight(sessionId, content),
    ),
  )
}

8. 运行时使用

Agent 创建后的运行时操作(createSession / turn / stream / fork / attach / detach / 数据 SDK 等)见 skill stello-agent-usage。

© stello-agent, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/stello-agent-creation of stello-agent/stello.

Open the folder on GitHubat commit 3bc9493

Compare with similar skills

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  • Storage Design

    stello-agent/stello

    存储接口的设计原则、SessionMeta 与 TopologyNode 解耦、上下文槽位、单一 SessionStorage 接口。触发条件:理解或实现 SessionStorage / SessionTree。

    112 GitHub stars~871 tokensUpdated 2 mo ago
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Questions about Stello Agent Creation

What does Stello Agent Creation do?

StelloAgent 创建配置教程。完整说明 createStelloAgent 的每个配置项,包含 sessionDefaults、storage、tools、skills、forkProfiles、session 层接入、orchestration 等。. Stello Agent Creation is an agent skill from stello-agent/stello.

How do I install Stello Agent Creation in Claude Code?

Run `npx skills add stello-agent/stello --skill stello-agent-creation -a claude-code`. Or copy the skill folder (.agents/skills/stello-agent-creation in stello-agent/stello) into .claude/skills/stello-agent-creation in your project. Claude Code loads it when a task matches its description.

How do I install Stello Agent Creation in Codex?

Run `npx skills add stello-agent/stello --skill stello-agent-creation -a codex`. Or copy the skill folder (.agents/skills/stello-agent-creation in stello-agent/stello) into .agents/skills/stello-agent-creation in your project. Codex loads it when a task matches its description.

Can I use Stello Agent Creation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add stello-agent/stello --skill stello-agent-creation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stello-agent-creation, .gemini/skills/stello-agent-creation, .github/skills/stello-agent-creation and .opencode/skills/stello-agent-creation in your project.

What does Stello Agent Creation need to run?

Going by SKILL.md and its folder, Stello Agent Creation needs credentials named OPENAI_API_KEY. Our summary lists: A credential in OPENAI_API_KEY.

Does Stello Agent Creation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Stello Agent Creation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Stello Agent Creation use?

Stello Agent Creation is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Stello Agent Creation use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Stello Agent Creation?

Skills that share tags, products or a category with Stello Agent Creation: Object Storage (sickn33/agentic-awesome-skills, 47k stars), Neon Object Storage (sickn33/agentic-awesome-skills, 47k stars), Remotion Video Creation (affaan-m/ECC, 276k stars) and Web Storage (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Stello Agent Creation?

stello-agent (a GitHub organization) maintains it in stello-agent/stello, which has 112 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on July 24, 2026.

Source: stello-agent/stello on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.