Agent skill

Stello Agent Usage

by stello-agent in stello-agent/stello

StelloAgent 运行时使用教程。覆盖 Session 生命周期、createSession、turn/stream 对话、fork 配置合成链、orchestrator-facing 数据 SDK、runtime 管理、热更新等运行时 API。

Apache-2.0Auto-check passed

Install Stello Agent Usage

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

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

GitHub CLI
$ gh skill install stello-agent/stello stello-agent-usage --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-usage .claude/skills/stello-agent-usage && 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-usage
GitHub stars
112
Token cost
~2.7k tokens
SKILL.md length
413 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

StelloAgent 运行时使用教程。覆盖 Session 生命周期、createSession、turn/stream 对话、fork 配置合成链、orchestrator-facing 数据 SDK、runtime 管理、热更新等运行时 API。

  • Works in 8 steps: Root Session 创建 → Session 生命周期 → Fork — 创建子 Session → …
  • SKILL.md covers 1. Root Session 创建, 2. Session 生命周期, 3. Fork — 创建子 Session and 4. Orchestrator-facing 数据 SDK, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Stello Agent Usage is an agent skill from stello-agent/stello. StelloAgent 运行时使用教程。覆盖 Session 生命周期、createSession、turn/stream 对话、fork 配置合成链、orchestrator-facing 数据 SDK、runtime 管理、热更新等运行时 API。

Its SKILL.md is about 2.7k 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-usage”

Workflow steps

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

  1. Root Session 创建
  2. Session 生命周期
  3. Fork — 创建子 Session
  4. Orchestrator-facing 数据 SDK
  5. Runtime 管理(多连接场景)
  6. 典型使用模式
  7. 并发语义
  8. 公开方法速查

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 no API keys, tokens, secrets or passwords.

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

Context cost

Stello Agent Usage loads about 2.7k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 413 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

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). 413 words, ~2,731 tokens.

Download SKILL.mdSave it as .claude/skills/stello-agent-usage/SKILL.md (or your agent's skills folder).
name
stello-agent-usage
description
StelloAgent 运行时使用教程。覆盖 Session 生命周期、createSession、turn/stream 对话、fork 配置合成链、orchestrator-facing 数据 SDK、runtime 管理、热更新等运行时 API。

StelloAgent 运行时使用教程

前置知识:createStelloAgent(config) 的配置方式见 skill stello-agent-creation。 本文档聚焦于 Agent 构建完成后的运行时操作。


1. Root Session 创建

对话起点是一个普通 Session(parentId === null),由 agent.createSession() 创建——不传 parentId 即为新 root。

typescript
const root = await agent.createSession({ label: 'Main' })
await agent.enterSession(root.id)

agent.createSession({ parentId?, label? }) 做了什么:

  1. 调用 sessions.createSession({ parentId, label }) 创建拓扑节点(parentId 缺省即 parentId === null)
  2. 返回 TopologyNode(含 id / parentId / children / refs / depth / label 等)

Root 没有特殊待遇——它就是一个普通 Session。多个 root 合法(森林)。全局默认 systemPrompt / skills 等配置在 sessionDefaults 即可。


2. Session 生命周期

createSession → enterSession → turn / stream (× N) → leaveSession → archiveSession
2.1 进入 Session
typescript
const bootstrap = await agent.enterSession(sessionId)
// bootstrap.context — 组装好的上下文(MemoryEngine 视角)
// bootstrap.session — SessionMeta(id, label, status, turnCount 等)

行为:触发 lifecycle.bootstrap(),初始化 Engine runtime。如果该 session 已有活跃 Engine,复用而非重建。

2.2 运行对话轮次(turn)
typescript
const result = await agent.turn(sessionId, '帮我分析市场趋势')

// result.turn.finalContent      — 最终文本回复(tool loop 结束后)
// result.turn.toolRoundCount    — 经历了几轮 tool call 循环
// result.turn.toolCallsExecuted — 实际执行了多少个 tool
// result.turn.rawResponse       — 原始最终 LLM 响应
2.3 流式模式(stream)
typescript
const streamResult = await agent.stream(sessionId, '帮我分析市场趋势')

for await (const chunk of streamResult) {
  process.stdout.write(chunk)
}

const result = await streamResult.result
console.log(result.turn.finalContent)

若本轮包含 tool call,iterator 会按产生顺序继续输出每个 LLM 子轮的文本;工具执行期间暂时没有 chunk。result.turn.finalContent 仍只表示最后一个不再请求客户端 tool 的响应,可能不等于所有 chunk 的简单拼接。

2.4 TurnRunnerOptions
typescript
await agent.turn(sessionId, input, {
  maxToolRounds: 5,                       // 限制 tool call 循环轮数(默认无限)
  signal: abortController.signal,         // 支持取消(中断当前轮 LLM/tool 调用)
  onToolCall: (toolCall) => { /* ... */ },
  onToolResult: (result) => { /* ... */ },
})
2.5 离开与归档
typescript
await agent.leaveSession(sessionId)   // 触发 consolidation 调度(fire-and-forget)
await agent.archiveSession(sessionId) // 标记归档,之后不应再 turn()

3. Fork — 创建子 Session

3.1 两种触发方式
方式触发者入口
LLM 发起LLM 调用 stello_create_session 内置 tool需在 capabilities.tools opt-in 注册
代码发起应用层调用 agent.forkSession()手动编排
3.2 forkSession 参数
typescript
const child = await agent.forkSession(sessionId, {
  // ── 必填 ──
  label: '市场分析-深度研究',

  // ── SessionConfig 字段(可选,参与合成链)──
  systemPrompt: '你是市场分析专家...',
  llm: specializedLlm,
  tools: customTools,
  skills: ['search', 'summarize'],
  consolidateFn: customConsolidateFn,
  compressFn: customCompressFn,

  // ── Fork 专属字段(可选)──
  prompt: '请深入分析半导体行业',   // fork 后立即发送的首条消息
  context: 'inherit',              // 'none'(默认)| 'inherit' | ForkContextFn
  topologyParentId: otherNodeId,   // 显式指定拓扑父节点(不传 = 当前 sessionId)
  profile: 'researcher',           // 引用预注册的 ForkProfile 名称
  profileVars: { region: '北美' }, // ForkProfile.systemPromptFn 的模板变量
})

// child: TopologyNode
// child.id              — 新 session 的 ID
// child.parentId        — 拓扑父节点 ID
// child.sourceSessionId — fork 时的上下文来源 session ID
// child.depth           — 拓扑深度(root = 0)
// child.label           — 显示名称

Fork 后需要单独 enterSession(child.id) 才能在子 session 上 turn()。

3.3 上下文继承策略(context)
typescript
await agent.forkSession(sessionId, { label: '子任务', context: 'none' })    // 空白开始(默认)
await agent.forkSession(sessionId, { label: '深度研究', context: 'inherit' }) // 完整继承 L3
await agent.forkSession(sessionId, {
  label: '摘要子任务',
  context: async (parentMessages) => parentMessages.slice(-10),             // 自定义裁剪
})
3.4 Fork 配置合成链

fork 时按 sessionDefaults → 父 session 固化 config → ForkProfile → EngineForkOptions 顺序合成,后者覆盖前者。root 也是普通 session,从 root fork 会正常继承 root 的固化 config。

详见 skill fork-design。


4. Orchestrator-facing 数据 SDK

需要在创建 agent 时注入 storage: SessionStorage(顶层)。这套 API 让外部 orchestrator(应用层 / Claude Code / Codex / Kitkit 等)能够在对话之外直接读取和回写每个 Session 的数据。

4.1 拓扑与 Session 列表
typescript
const roots = await agent.listRoots()                   // TopologyNode[]
const forest = await agent.getTopology()                // SessionTreeNode[](嵌套森林)
const node = await agent.getTopologyNode(sessionId)     // 单个 TopologyNode
const sessions = await agent.listSessions({ status: 'active' })  // SessionMeta[]
4.2 单个 Session 视图
typescript
const view = await agent.getSessionMetadata(sessionId)
// view.memory   — string | null(持久;不进 send 上下文)
// view.insight  — string | null(一次性 inbox;下次 send 注入并 clear)
4.3 批量 digest
typescript
const digests = await agent.listSessionDigests({ status: 'active' })
// digests[i] = { id, label, status, memory, insight }

应用层把这份数据喂给反思层 LLM,由它产出 per-session insight,再调用 agent.putInsight 定向回写。完整模式见 skill session-usage。

4.4 L3 消息读取
typescript
const messages = await agent.listMessages(sessionId, { limit: 100 })
4.5 写入
typescript
await agent.putMemory(sessionId, '当前进展摘要...')       // 持久 memory(替换语义)
await agent.putInsight(sessionId, '需要重新评估方向...')  // 一次性 insight(send 消费后 clear)
await agent.clearInsight(sessionId)                       // 主动清除

未在 agent 创建时注入 storage 时,这些方法会抛错。


5. Runtime 管理(多连接场景)

适用于 WebSocket 等多客户端连接场景,通过引用计数管理 Engine 生命周期。

typescript
await agent.attachSession(sessionId, connectionId)  // WS 连接建立
await agent.detachSession(sessionId, connectionId)  // WS 连接断开

agent.hasActiveEngine(sessionId)   // 是否有活跃 Engine
agent.getEngineRefCount(sessionId) // 当前引用计数

回收策略:

typescript
createStelloAgent({
  runtime: {
    resolver: myResolver,
    recyclePolicy: { idleTtlMs: 30_000 },
  },
})

// 运行时更新
agent.updateConfig({ runtime: { idleTtlMs: 60_000 } })

6. 典型使用模式

6.1 单 root 对话
typescript
const root = await agent.createSession({ label: 'Main' })
await agent.enterSession(root.id)
await agent.turn(root.id, '你好')
await agent.turn(root.id, '继续上个话题')
await agent.leaveSession(root.id)
6.2 代码驱动的并行 Fork
typescript
const root = await agent.createSession({ label: 'Main' })
await agent.enterSession(root.id)
await agent.turn(root.id, '我需要研究三个市场')

const children = await Promise.all([
  agent.forkSession(root.id, { label: '美国市场', systemPrompt: '你是美国市场专家' }),
  agent.forkSession(root.id, { label: '欧洲市场', systemPrompt: '你是欧洲市场专家' }),
  agent.forkSession(root.id, { label: '亚洲市场', systemPrompt: '你是亚洲市场专家' }),
])

await Promise.all(
  children.map(async (child) => {
    await agent.enterSession(child.id)
    await agent.turn(child.id, '分析半导体供应链')
    await agent.leaveSession(child.id)  // 触发 consolidation
  }),
)
6.3 多 root 并存(森林)
typescript
// 独立的研究/写作两条线,互不影响
const research = await agent.createSession({ label: 'Research' })
const writing  = await agent.createSession({ label: 'Writing' })

await agent.enterSession(research.id)
await agent.turn(research.id, '调研材料 ...')

await agent.enterSession(writing.id)
await agent.turn(writing.id, '基于已有材料写一份 ...')

const all = await agent.listRoots()  // 两个 root 都会出现
6.4 外部 reflection 循环(自行实现 integrate)
typescript
async function reflect(agent: StelloAgent, llm: LLMAdapter): Promise<void> {
  const digests = await agent.listSessionDigests({ status: 'active' })
  // ... 应用层 prompt 把 digests 喂给 llm,解析出 per-target insight ...
  for (const [id, content] of Object.entries(insightsByTarget)) {
    await agent.putInsight(id, content)
  }
}

详见 stello-agent-creation §7。

6.5 WebSocket 连接管理
typescript
ws.on('connection', async (socket) => {
  const holderId = socket.id

  socket.on('enter', async ({ sessionId }) => {
    await agent.attachSession(sessionId, holderId)
    await agent.enterSession(sessionId)
  })

  socket.on('message', async ({ sessionId, input }) => {
    const stream = await agent.stream(sessionId, input)
    for await (const chunk of stream) {
      socket.send(JSON.stringify({ type: 'chunk', data: chunk }))
    }
    const result = await stream.result
    socket.send(JSON.stringify({ type: 'done', data: result }))
  })

  socket.on('close', async () => {
    for (const sessionId of socket.sessions) {
      await agent.detachSession(sessionId, holderId)
    }
  })
})

7. 并发语义

  • 同 sessionId 内串行:同一 session 上的 turn() 不会并发执行
  • 不同 sessionId 之间并行:可同时在多个 session 上 turn()
  • 所有异步副作用 fire-and-forget:consolidation / hooks 不阻塞 turn() 返回
  • 错误不中断对话:副作用抛错时 emit error 事件,对话循环继续

Show full SKILL.md (160 more words)Show less

8. 公开方法速查

编排
方法返回值说明
createSession({ parentId?, label? })Promise<TopologyNode>创建拓扑节点(不传 parentId 即新 root;多 root 合法)
enterSession(id)Promise<BootstrapResult>进入 session,触发 bootstrap
turn(id, input, opts?)Promise<EngineTurnResult>同步对话轮次(含 tool call 循环)
stream(id, input, opts?)Promise<EngineStreamResult>流式对话轮次
leaveSession(id)Promise<{ sessionId }>离开 session,触发 consolidation 调度
forkSession(id, opts)Promise<TopologyNode>创建子 session,执行配置合成链
archiveSession(id)Promise<{ sessionId }>归档 session
consolidateSession(id)Promise<void>手动触发该 session 的 consolidation
attachSession(id, holderId)Promise<OrchestratorEngine>附着 runtime 持有者
detachSession(id, holderId)Promise<void>释放 runtime 持有者
hasActiveEngine(id)boolean是否有活跃 Engine
getEngineRefCount(id)number当前引用计数
updateConfig(patch)void热更新运行时配置
Orchestrator-facing 数据 SDK(需注入 storage)
方法返回值说明
listSessions(filter?)Promise<SessionMeta[]>列出所有 session
listRoots()Promise<TopologyNode[]>列出所有 root
getTopology()Promise<SessionTreeNode[]>完整森林(嵌套树)
getTopologyNode(id)Promise<TopologyNode | null>单个节点
getSessionMetadata(id)Promise<{ memory, insight }>单 session 的 memory + insight
listSessionDigests(filter?)Promise<SessionDigest[]>批量收集所有 Session 的 digest
listMessages(id, options?)Promise<Message[]>读取 L3 消息
putMemory(id, content)Promise<void>写入 memory
putInsight(id, content)Promise<void>写入 insight(一次性)
clearInsight(id)Promise<void>清除 insight
只读属性
属性类型说明
configStelloAgentConfig归一化后的完整配置
sessionsSessionTree拓扑树
memoryMemoryEngine记忆引擎
storageSessionStorage | undefined数据存储(未注入时 data-IO 方法不可用)
profilesForkProfileRegistry | undefinedFork 模板注册表

© 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-usage of stello-agent/stello.

Open the folder on GitHubat commit 3bc9493

Compare with similar skills

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Questions about Stello Agent Usage

What does Stello Agent Usage do?

StelloAgent 运行时使用教程。覆盖 Session 生命周期、createSession、turn/stream 对话、fork 配置合成链、orchestrator-facing 数据 SDK、runtime 管理、热更新等运行时 API。. Stello Agent Usage is an agent skill from stello-agent/stello.

How do I install Stello Agent Usage in Claude Code?

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

How do I install Stello Agent Usage in Codex?

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

Can I use Stello Agent Usage 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-usage -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-usage, .gemini/skills/stello-agent-usage, .github/skills/stello-agent-usage and .opencode/skills/stello-agent-usage in your project.

What does Stello Agent Usage need to run?

SKILL.md names no scripts, command-line tools or credentials: Stello Agent Usage is instructions for the agent only.

Does Stello Agent Usage 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 Usage 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 Usage use?

Stello Agent Usage 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 Usage use?

About 2.7k tokens (SKILL.md is roughly 11k 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 Usage?

Skills that share tags, products or a category with Stello Agent Usage: Monitor Stream (ruvnet/ruflo, 74k stars), Streaming HTML (thedaviddias/Front-End-Checklist, 74k stars), Streaming (alsk1992/CloddsBot, 2.9k stars) and Assistant UI Streaming (compozy/compozy, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Stello Agent Usage?

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.