Zero-token execution layer for AI agents. Define workflows once, run them free forever — persistent, scheduled, deterministic. 6 MCP tools over SSE. Supports DAG-based execution, 6 step types (action, condition, loop, parallel, wait, reasoning), 26 built-in actions, ${{}} interpolation, reasoning nodes for human-in-the-loop decisions, and secret vault. Use when defining workflows, running templates, checking status, sending signals, querying workflow history, or visualizing DAGs.
数据来源:ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一:命令行安装(推荐)
推荐(无需提前安装 clawhub)
npx clawhub@latest --dir ~/.claude/skills install opcode或使用 clawhub CLI(需提前安装)
clawhub --dir ~/.claude/skills install opcode⚠️ 需要 Node.js 18+,没有 Node?请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二:手动下载安装(无需 Node)
下载 ZIP,解压后将文件夹放到以下路径,重启 Agent 即可:
安装路径
~/.claude/skills/opcode/💡解压后将文件夹放到上方路径,重启 Agent 即可生效
--- name: opcode description: > Zero-token execution layer for AI agents. Define workflows once, run them free forever — persistent, scheduled, deterministic. 6 MCP tools over SSE. Supports DAG-based execution, 6 step types (action, condition, loop, parallel, wait, reasoning), 26 built-in actions, ${{}} interpolation, reasoning nodes for human-in-the-loop decisions, and secret vault. Use when defining workflows, running templates, checking status, sending signals, querying workflow history, or visualizing DAGs. license: MIT compatibility: > Requires Go 1.25+, CGO_ENABLED=1, and gcc or clang. Runs as SSE daemon on macOS and Linux. Linux: cgroups v2 for process isolation. macOS: timeout-only fallback. metadata: version: "1.2.1" transport: "sse" author: "rendis" repository: "https://github.com/rendis/opcode" primary-env: "OPCODE_VAULT_KEY" platforms: "darwin linux" requires-bins: "go gcc|clang" openclaw-emoji: "⚙️" openclaw-os: "darwin linux" openclaw-user-invocable: "true" openclaw-install-type: "go" openclaw-install-package: "github.com/rendis/opcode/cmd/opcode" ---
Execution runtime for AI agents. You reason, OPCODE executes — zero tokens per run after the first define. Workflows persist across sessions, run on schedules, and coordinate multiple agents. Persistent SSE daemon: 1 server, N agents, 1 database. JSON-defined DAGs, level-by-level execution, automatic parallelism. 6 MCP tools over SSE (JSON-RPC).
Why use OPCODE instead of reasoning through each step yourself? Every repeated workflow burns tokens re-reasoning decisions you already made. OPCODE templates your reasoning once and executes it deterministically — zero inference cost, identical output every run, survives context resets.
| I want to... | Tool | | ------------------------------------ | -------------- | | Create/update a workflow template | opcode.define | | Execute a workflow | opcode.run | | Check status or pending decisions | opcode.status | | Resolve a decision / cancel / retry | opcode.signal | | List workflows, events, or templates | opcode.query | | Visualize a workflow DAG | opcode.diagram |
Install:
go install github.com/rendis/opcode/cmd/opcode@latest
First-time setup (writes config and starts daemon):
opcode install --listen-addr :4100 --vault-key "my-passphrase"
Restart after stop: OPCODE_VAULT_KEY="my-passphrase" opcode
MCP client configuration:
{
"mcpServers": {
"mcpServers": {
"opcode": {
"type": "sse",
"url": "http://localhost:4100/sse"
}
}
}
Each agent self-identifies via agent_id in tool calls. Opcode auto-registers unknown agents. Choose a stable ID per agent (e.g., "content-writer", "deploy-bot").
Workflows survive restarts. On startup, orphaned active workflows become suspended. Query with opcode.query({ "resource": "workflows", "filter": { "status": "suspended" } }), then resume or cancel via opcode.signal.
See operations.md for full configuration, subcommands, SIGHUP hot-reload, security model, web panel, and benchmarks.
Registers a reusable workflow template. Version auto-increments (v1, v2, v3...).
| Param | Type | Required | Description | | --------------- | ------ | -------- | ----------------------------------------------------------------------------------------------- | | name | string | yes | Template name | | definition | object | yes | Workflow definition (see below) | | agent_id | string | yes | Defining agent ID | | description | string | no | Template description | | input_schema | object | no | JSON Schema for input validation | | output_schema | object | no | JSON Schema for output validation | | triggers | object | no | Trigger config (seeworkflow-schema.md) |
Returns: { "name": "...", "version": "v1" }
Executes a workflow from a registered template.
| Param | Type | Required | Description | | --------------- | ------ | -------- | ------------------------- | | template_name | string | yes | Template to execute | | agent_id | string | yes | Initiating agent ID | | version | string | no | Version (default: latest) | | params | object | no | Input parameters |
Returns:
{
"workflow_id": "uuid",
"status": "completed | suspended | failed",
"output": { ... },
"started_at": "RFC3339",
"completed_at": "RFC3339",
"steps": {
"step-id": { "step_id": "...", "status": "completed", "output": {...}, "duration_ms": 42 }
}
}
If status is "suspended", call opcode.status to see pending_decisions.
Gets workflow execution status.
| Param | Type | Required | Description | | ------------- | ------ | -------- | ----------------- | | workflow_id | string | yes | Workflow to query |
Returns:
{
"workflow_id": "uuid",
"status": "suspended",
"steps": { "step-id": { "status": "...", "output": {...} } },
"pending_decisions": [
{
"id": "uuid",
"step_id": "reason-step",
"context": { "prompt": "...", "data": {...} },
"options": [ { "id": "approve", "description": "Proceed" } ],
"timeout_at": "RFC3339",
"fallback": "reject",
"status": "pending"
}
],
"events": [ ... ]
}
Workflow statuses: pending, active, suspended, completed, failed, cancelled.
Sends a signal to a suspended workflow.
| Param | Type | Required | Description | | ------------- | ------ | -------- | ------------------------------------------------- | | workflow_id | string | yes | Target workflow | | signal_type | enum | yes | decision / data / cancel / retry / skip | | payload | object | yes | Signal payload (see below) | | step_id | string | no | Target step | | agent_id | string | no | Signaling agent | | reasoning | string | no | Agent's reasoning |
Payload by signal type:
| Signal | step_id | Payload | Behavior | | ---------- | -------- | ----------------------------- | ------------------------------- | | decision | required | { "choice": " | Resolves decision, auto-resumes | | data | optional | { "key": "value", ... } | Injects data into workflow | | cancel | no | {} | Cancels workflow | | retry | required | {} | Retries failed step | | skip | required | {} | Skips failed step |
Returns (decision): { "ok": true, "resumed": true, "status": "completed", ... } Returns (other): { "ok": true, "workflow_id": "...", "signal_type": "..." }
Queries workflows, events, or templates.
...
安装 Opcode 后,可以对 AI 说这些话来触发它
Help me get started with Opcode
Explains what Opcode does, walks through the setup, and runs a quick demo based on your current project
Use Opcode to zero-token execution layer for AI agents
Invokes Opcode with the right parameters and returns the result directly in the conversation
What can I do with Opcode in my finance & investment workflow?
Lists the top use cases for Opcode, with example commands for each scenario
将技能文件夹放到 ~/.claude/skills/opcode/ 目录(个人级,所有项目可用),或 .claude/skills/opcode/(项目级)。重启 AI 客户端后,用 /opcode 主动调用,或让 AI 根据上下文自动发现并使用。
Opcode 支持 Claude、Cursor、OpenClaw,可与这些 AI 平台无缝集成,扩展其能力。
Opcode 可免费安装使用。请查阅仓库了解许可证信息。
Zero-token execution layer for AI agents. Define workflows once, run them free forever — persistent, scheduled, deterministic. 6 MCP tools over SSE. Supports DAG-based execution, 6 step types (action, condition, loop, parallel, wait, reasoning), 26 built-in actions, ${{}} interpolation, reasoning nodes for human-in-the-loop decisions, and secret vault. Use when defining workflows, running templates, checking status, sending signals, querying workflow history, or visualizing DAGs.
Automate my finance & investment tasks using Opcode
Identifies repetitive steps in your workflow and sets up Opcode to handle them automatically
Opcode 属于「Finance & Investment」分类,该分类的技能帮助 AI 智能体在此领域执行专业任务。