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Agent Skill · 开发

chat2workflow(chat2workflow 开发)

chat2workflow 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

chat2workflow 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 6,028。

总安装 5,239周安装 214GitHub Stars 1下载 1,695核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.1.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:chat2workflow(chat2workflow 开发)
来源仓库:https://github.com/chikawa11/chat2workflow
安装命令:
openclaw skills install chat2workflow
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install chat2workflow

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

将自然语言需求转化为 Dify/Coze 平台的工作流 JSON 结构。适合在 OpenClaw 中快速搭建自动化流程而不必手动编码时使用。通过 clawhub 安装,支持多轮对话逐步细化节点连接关系。安装前建议确认目标平台 API 权限及字段命名规范一致性。注意生成的工作流需经实际测试验证后才能投入生产使用。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install chat2workflow;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

chat2workflow 怎么安装?

在终端执行 openclaw skills install chat2workflow。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

chat2workflow 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

chat2workflow 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install chat2workflow 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

chat2workflow 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

chat2workflow 用的人多吗?

公开数据是累计安装 5,239 次,近一周安装 214 次,下载 1,695 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

chat2workflow 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
chat2workflow

chat2workflow 开发 · 本页

5,2391执行命令MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
chat2workflow
description
A design-only workflow designer for the Dify and Coze platforms. Through multi-round conversation, it produces a structured workflow JSON (nodes, edges, variable references) as text output. The skill itself produces only text and never runs any scripts.

Chat2Workflow Builder Skill

What this skill does

This skill is design-only. Its deliverable is three tagged sections of text (see *Output Format* below) — most importantly a workflow JSON wrapped in <workflow></workflow>. Producing those three sections involves only reading the platform documentation under node_docs/ and emitting text; nothing else happens.

A few Python files also live in this folder (converter.py, autofix.py, tools.py, bash_converter.sh). They are not part of the skill's deliverable and are not referenced by the generation process. They exist as standalone utilities that a user can run manually from a shell, entirely outside the skill's text-generation flow, if they separately want to turn a JSON file into a Dify YAML or Coze ZIP on their own machine. See the bundled README/source of those scripts for their CLI usage; the skill itself does not invoke them.

Overview

A Chat2Workflow design is a Directed Acyclic Graph of connected nodes, where each node represents a step of logic, data processing, or model inference. The design is serialized as a JSON object that enumerates the nodes and the edges that connect them.

This skill supports two target platforms:

PlatformDocumentation FileSelection criterion
Dify (default)node_docs/dify.mdThe user instruction explicitly mentions Dify, or no platform is specified at all.
Cozenode_docs/coze.mdThe user instruction explicitly mentions Coze / 扣子.

Platform Resolution

Platform selection is a two-step lookup performed against the user's instruction:

  1. The user's instruction is scanned for platform keywords — dify / Dify / DIFY or coze / Coze / 扣子.
  2. If a platform keyword is present, that platform is the target. Otherwise the target is Dify (the default).
  3. The matching file from node_docs/ — node_docs/dify.md or node_docs/coze.md — is the authoritative schema for node type strings, param objects, and referable variables on that platform. The two platforms have different node sets, different parameter schemas, and different referable variables.
  4. The chosen platform is named in <design_principle> with a short justification, e.g. "Platform: Dify (user did not specify, defaulting to Dify).".

A single workflow targets a single platform; every node's type comes from that platform's documentation file.

The interaction model is conversational: the user supplies creation or modification instructions across multiple rounds, and — unless an instruction says otherwise — each new response extends the current design rather than replacing it. Responses follow the structure described in *Output Format*.

Output Format

A well-formed response consists of three clearly tagged sections rendered as inline text (not files):

1. Node Selection

Wrapped in <node_selection></node_selection> tags. Lists the names of the nodes chosen for the design.

2. Design Principle

Wrapped in <design_principle></design_principle> tags. Explains the reasoning and architecture decisions. Contains at minimum:

  • A one-line Platform declaration (Platform: Dify or Platform: Coze).
  • A Variable Checklist subsection that cross-checks the input and output variables against the instruction's requirements (see Workflow Validity Rule 2 below).

3. Workflow JSON

Wrapped in <workflow></workflow> tags. Contains the complete workflow as a single valid JSON object.

The content inside <workflow> is raw JSON — markdown code fences around it break parsing, because the downstream pipeline calls json.loads() directly on the content between the tags. Concretely, `json immediately after <workflow> or ` immediately before </workflow> will cause the parse step to fail. Code fences that appear *inside* JSON string values (for example inside a code node's code field) are fine — only the outer wrapping fences cause parsing problems.

JSON Structure Specification

The JSON object describes a Directed Acyclic Graph (DAG) workflow, consisting of two core fields:

nodes_info (Array)

Contains detailed configuration information for all nodes. Each element is an object representing a functional node and contains the following fields:

  • id (String): The unique identifier of the node, a string that increments starting from 1 (e.g., "1","2").

- Note: Child nodes within an Iteration node use the format "<ParentID>-<SeqNum>", where <ParentID> is the id of the enclosing iteration node and <SeqNum> is a sequential number starting from 1 that increments for each child node within that iteration canvas. For example, if the iteration node's id is "3", its child nodes are "3-1", "3-2", "3-3", etc. The iteration-start node is always the first child, i.e., "<ParentID>-1" (e.g., "3-1", "5-1").

  • type (String): The type of the node. The type value should exactly match the Type specified in the selected platform's node documentation (e.g., the Template node's type is template-transform, not template). Using an incorrect type string — or a type string that does not belong to the selected platform — will cause the workflow to fail.
  • param (Object): Specific configuration parameters for the node. The structure varies depending on the type.

edges (Array)

Each element in the list represents a connection line. Each element follows a triplet structure: [SourceNodeID (String), OutputPortIndex (Number), TargetNodeID (String)](e.g., ["1", 0, "2"]).

  • Default output port is 0.
  • For branching nodes (question-classifier, if-else), port indices correspond to branch order (0, 1, 2...).
  • For if-else, the ELSE branch port index equals the number of explicitly defined cases (i.e., it's the last port).

Downstream Variable References

Downstream nodes can reference the referable_variables of upstream nodes, which will be represented in param.

Variable Reference System

Downstream nodes reference upstream node outputs through one of two patterns:

In structured parameters (arrays/tuples):

[SourceVariableName, SourceNodeID] Example: ["text", "3"] — references the text variable from node 3.

In text fields:

{{#<SourceNodeID>.<SourceVariableName>#}} Example: {{#'3'.text#}} — references the text variable from node 3.

When the SourceNodeID contains a hyphen (iteration child nodes such as "2-2"), it is quoted: {{#'2-2'.text#}}.

Workflow Validity Rules

The rules below describe what makes a produced workflow valid. They are constraints on the emitted JSON/tags, not instructions to any external system.

  1. Node Selection ↔ Workflow Consistency: The node types declared in <node_selection> and those actually used in <workflow> are exactly consistent. Every node declared in <node_selection> appears in <workflow>, and every node used in <workflow> is declared in <node_selection>. No omissions, no extras.
  1. Variable Checklist in Design Principle: The <design_principle> section contains a Variable Checklist subsection that verifies whether the input and output variables satisfy the instruction's requirements — especially relevant across multi-round interactions where variable requirements may change between rounds.
  1. JSON Bracket Integrity: The <workflow> tag contains a single-line, valid JSON string parseable by json.loads() in Python directly. Bracket closure is critical — truncation, mismatched brackets, and unclosed structures all invalidate the JSON. Nodes with deeply nested bracket structures (for example if-else cases with multiple conditions) are the most common source of bracket mismatches; verifying that every [, {, and ( has a matching closing counterpart before finalizing avoids these errors.
  1. Escape Sequences in String Values: JSON string values cannot contain raw control characters (newline, tab, etc.), so escaping is required. This is especially relevant for the code field (Python code), LLM-node message fields (the system and user keys under a model node's param), and template fields:

- Newlines inside string values: \ (backslash + n), not a real line break. - Tabs inside string values: \ (backslash + t). - Carriage returns inside string values: \ (backslash + r). - Double quotes inside string values: \" (backslash + quote). - Backslashes that should appear literally in the final string (for example in regex patterns such as \d{4}, or in Jinja2 replace('\ ', ' ')): \\ (double backslash). - For example, a Jinja2 template needing replace('\ ', ' ') is written in JSON as replace('\\ ',' '), because \\ in JSON represents the literal two-character sequence backslash+n. - Common mistake: Double-escaped forms such as \\\\ or \\\\ produce literal backslash characters in the parsed output, not actual newlines/tabs. Exactly ONE level of JSON escaping is correct, regardless of whether the string content happens to be Python code or a template — JSON only ever needs one level of escaping. - For example: a Python snippet containing line.split("\ ") is written in JSON as line.split(\"\\ \") — \" escapes the double quotes, \\ represents a literal tab character in the parsed string.

All newlines, tabs, and carriage returns within JSON string values are represented as two-character escape sequences (\ , \ , \ ), not as literal whitespace characters. This is especially relevant for the code field (Python code), LLM-node message fields (the system and user keys under a model node's param), and template fields.

  1. Topological Ordering of nodes_info: The nodes_info array is in topological order. Nodes use "forward references" — a node only references variables from nodes that appear before it in the array. The one exception is the output_selector of an iteration node, which may reference a child node that is defined later (since iteration child nodes are created as part of the iteration).
  1. Iteration Canvas Boundary: Edges and variable references never cross the iteration boundary. Specifically:

- No edges exist between iteration child nodes and external nodes. External nodes connect to/from the iteration node itself, which acts as the sole bridge between internal and external. - External node variables are not referenced from inside the iteration canvas, and iteration child node variables are not referenced from outside (the iteration node's output is used instead). - Child nodes inside the iteration canvas reference the iteration node's built-in item and index variables directly (via the iteration node's id, not the iteration-start node's id in Dify). - The iteration node receives internal results via its output_selector parameter, which points to a child node's output variable. - Child nodes within an iteration canvas are connected to each other via internal edges — they are not isolated nodes. - On Dify, no edge exists between the iteration and iteration-start nodes.

  1. No Isolated Nodes: Every node in the workflow is connected to at least one other node via edges. A node created without any connecting edge is invalid. The workflow is a connected DAG — all nodes (except for the child nodes within the iteration canvas) are reachable from the start node through the edge graph.
  1. Instruction Fidelity — No Key Node Omissions: Producing a working workflow requires identifying every node implied or explicitly mentioned by the creation/modification instruction. A missing key node — omitting a Document Extractor when the instruction involves file content processing, or omitting an If-Else when the instruction describes conditional logic — causes the workflow to fail. A valid workflow can actually execute and solve the problem end-to-end.
  1. File-Aware Workflow Design: Whether the instruction's input or output involves files matters for node selection:

- Inputs mentioning document or image are typically file-typed variables. - Inputs with multiple optional forms (where some may be empty while others have values) are handled with an if-else node that detects which inputs are provided and routes to the appropriate processing branch.

  1. Format Compliance: Creation, addition, deletion, modification, and correction all follow the structure described in *Output Format* so the response can be correctly parsed.

Multi-Round Interaction Rules

Unless explicitly instructed to add, remove, or modify, variables and logic not mentioned in the instruction should remain unchanged. The following rules govern how output specifications are interpreted across rounds:

PatternInterpretation
"Only output" — "only needs to output X" (without additive language)Output exactly X. This is a fresh specification — REPLACE all previous outputs. Previous outputs NOT listed are dropped.
"Additionally add" — Additive language (any phrasing conveying "in addition to what already exists")ADD the new variables to existing outputs.
"Remove" — "Remove the output Y"Remove only Y, keep all others.
"No mention" — No mention of outputsKeep them unchanged.
"Branch-scoped change" — In a branching workflow, the output specification constrains only the branch(es) it refers toUnmentioned branches remain unchanged.

Platform-Specific Node Documentation

The complete list of node types, their parameter schemas, and their referable variables for each platform lives in a dedicated, pluggable documentation file under node_docs/:

Once the target platform has been resolved (see Platform Resolution above), the corresponding file in node_docs/ is the authoritative reference for node type strings and their param structures. Because the two platforms have different and evolving node sets, the node_docs/ file for the selected platform is consulted each time rather than relied on from memory.

Note on placeholder auth fields in node schemas

Node templates for the http-request node (and similar) contain placeholder fields such as bearerTokenData with a literal "EMPTY" value. These are schema placeholders that describe what a user-authored workflow can contain; the skill does not read, request, or transmit any credentials. Users authoring a workflow should avoid pasting real API keys, bearer tokens, or passwords into the generated JSON — those values, if pasted in, would be stored verbatim in whatever artifact the user produces.

平台分布

OpenClaw

91.39%
按下载量换算1,549

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install chat2workflow 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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