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Agent Skill · 研究检索

knods(knods 搜索)

knods 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

knods 是研究检索类 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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 11,706。

总安装 10,992周安装 449GitHub Stars 公开资料未说明下载 3,520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
1.0.2

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:knods(knods 搜索)
来源仓库:https://github.com/alesys/knods
安装命令:
openclaw skills install knods
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 knods

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

knods 用于构建和修改 Knods 视觉 AI 工作流程。支持 OpenClaw Gateway 轮询协议或无头流 API。适合在 OpenClaw 中需要自动化视觉处理任务时使用。可结合来源仓库和原始 README 继续核验具体用法。安装前建议确认权限范围和维护状态。

knods 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 knods;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

knods 怎么安装?

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

knods 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。

knods 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 knods 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。

knods 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。

knods 用的人多吗?

公开数据是累计安装 10,992 次,近一周安装 449 次,下载 3,520 次。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。

knods 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
knods

knods 搜索 · 本页

10,992—执行命令MIT-0有提醒

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
knods
description
Build and modify Knods visual AI workflows using either the OpenClaw Gateway polling protocol or the Knods headless flows API. Use for Knods polling payloads with fields like messageId/message/history, or for direct flow discovery/execution tasks like listing flows, reading input schemas, starting runs, polling status, cancelling runs, and retrieving outputs programmatically.
metadata
openclaw
emoji
🔌
homepage
https://github.com/alesys/openclaw-skill-knods
os
["linux"]
requires
bins
["python3", "bash", "openclaw", "systemctl"]
env
["KNODS_BASE_URL"]

Knods

Overview

Handle two Knods modes:

  1. Interactive canvas chat via the polling gateway
  2. Headless flow execution via the REST API

Use the polling bridge for Knods Iris/chat payloads. Use the headless API when the task is to discover a flow, inspect inputs, run it, wait, cancel, or fetch outputs programmatically.

Mode Selection

  • Use polling gateway mode when input arrives as a Knods chat envelope with messageId, message, and history, and the response must stream back with optional [KNODS_ACTION]...[/KNODS_ACTION].
  • Use headless API mode when the user wants to:

- list flows - search flows by name/description - inspect a flow's input schema - start a run - poll until completion - cancel a run - retrieve outputs programmatically

Workflow

A. Polling Gateway Flow

  1. Parse incoming payload fields.
  • Treat message as the primary request.
  • Use history for continuity.
  • On first turn in a conversation, expect prepended context in message describing node types and action rules. Always prefer the node catalog from this context over the defaults below.
  • Use messageId to map all response chunks to the correct message.
  1. Choose whether to emit a canvas action block.
  • Use addNode for single-node additions.
  • Use addFlow for multi-node workflows or any request requiring edges.
  • If the user only asks a question, respond with normal text and no action block.
  1. Build strict action JSON.
  • Wrap each action exactly as:

- [KNODS_ACTION]{"action":"addNode","nodeType":"FluxImage"}[/KNODS_ACTION] - [KNODS_ACTION]{"action":"addFlow","nodes":[...],"edges":[...]}[/KNODS_ACTION]

  • Use "nodeType" (not "type") in node objects. Do NOT include position or data fields — Knods handles layout automatically.
  • For addFlow, ensure every edge source and target references an existing node id.
  • Always end flows with an Output node.
  • Never connect two generator nodes directly; route through Output.
  • Use stable node IDs (for example n1, n2, n3) so follow-up edits are easy.
  • Avoid unknown keys in action JSON.
  1. Stream response back to Knods.
  • Send assistant text as delta chunks to /respond for the same messageId.
  • Send {"messageId":"...","done":true} when complete.
  • Keep first chunk quick to avoid timeout perception.

B. Headless API Flow

  1. Discover candidate flows.
  • Run:

- python3 {baseDir}/scripts/knods_headless.py list - or python3 {baseDir}/scripts/knods_headless.py resolve --query "<text>"

  1. Inspect the selected flow.
  • Run:

- python3 {baseDir}/scripts/knods_headless.py get --flow-id "<flowId>"

  • Read inputs and preserve every nodeId exactly.
  1. Start a run.
  • Build inputs as JSON array with nodeId, content, and type.
  • Run:

- python3 {baseDir}/scripts/knods_headless.py run --flow-id "<flowId>" --inputs-json '[...]'

  1. Poll until terminal state.
  • Prefer:

- python3 {baseDir}/scripts/knods_headless.py wait --run-id "<runId>"

  • Or use:

- python3 {baseDir}/scripts/knods_headless.py run-wait --flow-id "<flowId>" --inputs-json '[...]'

  1. Handle result.
  • On completed, read outputs
  • On failed, surface error.message and error.nodeId if present
  • On timeout, optionally cancel the run

Output Rules

  • Return normal assistant text; do not wrap the full reply in a custom envelope.
  • Include [KNODS_ACTION]...[/KNODS_ACTION] inline only when a canvas mutation is intended.
  • Do not mention internal polling URLs/tokens in user-facing text.
  • Keep action JSON valid and compact.

Node Catalog

IMPORTANT: Every generator node listed below has a built-in prompt textarea. Do NOT add a DocumentPanel before a single generator — just connect the generator directly to an Output. Only use DocumentPanel when one shared prompt feeds multiple generators in parallel.

When the first message includes a node catalog context, always use that list over these defaults. The context catalog is always more up-to-date.

Text Generators (output: text)

All text generators accept text + image input and have a built-in prompt textarea.

  • ChatGPT — OpenAI models. Best all-rounder.
  • Claude — Anthropic models. Great for reasoning and creative writing.

Image Generators (output: image)

All image generators have a built-in prompt textarea and accept optional image input for image-to-image editing.

  • GPTImage — OpenAI. Best at following complex instructions and text rendering.
  • FluxImage — FLUX by Black Forest Labs. Industry-leading quality for portraits and artistic styles. Fast.
  • ImagePrompt — Google Gemini. Great for photorealistic images and concept art.
  • ZImageTurbo — Lightning-fast (<2 seconds). Best for rapid prototyping.
  • QwenImage — Alibaba Qwen. Strong at anime, illustrations, and Asian-inspired aesthetics.
  • Seedream — ByteDance. Dreamy, surreal compositions. Good at text rendering in images.
  • GrokImage — xAI. Text-to-image and image editing.

Video Generators (output: video)

All video generators below have a built-in prompt textarea and support both text-to-video and image-to-video (connect an ImagePanel for image-to-video).

  • Veo3FalAI — Google Veo 3.1. Cinematic video up to 8s with native audio. Best overall quality.
  • Sora2Video — OpenAI Sora 2. Realistic motion and physics, up to 12s.
  • Kling26Video — Kling 2.6 Surreal Engine. Cinematic with audio, up to 10s.
  • KlingO3Video — Kling 3.0. Latest generation, Standard/Pro quality, up to 10s.
  • Wan26Video — Wan 2.6. Multi-shot videos, 720p/1080p, up to 15s.
  • LTXVideo — LTX-2 Pro. High-fidelity cinematic with synchronized audio.
  • GrokVideo — xAI. Video with native audio.

Special Video Node

  • WanAnimateVideo — Character animation. REQUIRES two inputs: a VIDEO (motion reference) + an IMAGE (character to animate). Does NOT have a text prompt. Only use when user wants to animate a character image using motion from another video.

Input/Container Nodes

  • ImagePanel — Upload or paste an image. Output: image. Use when user wants to provide a reference image or a starting frame for image-to-video.
  • DocumentPanel — Editable text container. Output: text. Use ONLY when one shared prompt feeds multiple generators in parallel.
  • Output — Displays generated results (text, image, video). REQUIRED at the end of every flow.

Flow Design Rules

  1. Every generator has a built-in prompt textarea. Never prepend a DocumentPanel to a single generator.
  2. Use DocumentPanel only for one shared prompt feeding multiple generators in parallel.
  3. Use ImagePanel when user wants to provide a reference image, a starting frame for video, or an image input for WanAnimateVideo.
  4. Always end flows with an Output node.
  5. Never connect two generators directly. Route through an Output node if chaining.
  6. Flows go left to right: inputs → generators → Output.
  7. Use EXACT PascalCase node names from the catalog. Do NOT invent node names.
  8. WanAnimateVideo is the only node that requires a video input. Only suggest it when the user specifically wants to animate a character image using motion from a video.
  9. Add initialData only when user intent clearly implies parameters.
  10. Build the smallest flow that satisfies the request.

Flow Examples

Single image generator (most common):

FluxImage → Output

Image from reference photo:

ImagePanel → GPTImage → Output

One prompt feeding two image generators:

DocumentPanel → FluxImage → Output
DocumentPanel → GPTImage → Output

Text-to-video:

Veo3FalAI → Output

Image-to-video (animate a still image):

ImagePanel → Veo3FalAI → Output

Character animation from video motion (WanAnimateVideo needs both video + image):

ImagePanel → WanAnimateVideo → Output
[video source] → WanAnimateVideo

Text generation:

ChatGPT → Output

Gateway Behavior Constraints

  • Poll interval target: about 1-2 seconds.
  • Message claim timeout: about 2 minutes.
  • Always preserve messageId across all chunk posts for a turn.
  • Gateway auth uses gw_... token via query parameter token; never require Supabase JWT in this flow.

Runtime Operations

When running a persistent poller service/process:

  • Support either configuration style:

- KNODS_BASE_URL already includes /updates?token=... - or KNODS_BASE_URL points to connection base and token is supplied separately (KNODS_GATEWAY_TOKEN)

  • Derive /respond from the same connection root as /updates.
  • Log handled messageId values and transport errors for debugging.

For headless API operations:

  • Prefer KNODS_API_BASE_URL + KNODS_API_KEY
  • KNODS_API_BASE_URL should look like https://<instance>/api/v1
  • KNODS_API_KEY must have knods:read and knods:run
  • If the API base URL is omitted, the packaged client can derive it from the same host as KNODS_BASE_URL

Packaged Runtime (required)

This skill ships the runtime bridge and installer:

  • scripts/knods_iris_bridge.py
  • scripts/knods_headless.py
  • scripts/install_local.sh

Install/deploy from the skill folder:

bash /home/rolf/.openclaw/skills/knods/scripts/install_local.sh

The installer deploys:

  • ~/.openclaw/scripts/knods_iris_bridge.py
  • ~/.config/systemd/user/knods-iris-bridge.service

Then runs:

  • systemctl --user daemon-reload
  • systemctl --user enable --now knods-iris-bridge.service

Environment Variables

Set these in ~/.openclaw/.env:

  • Required for polling gateway mode:

- KNODS_BASE_URL

  • Required when KNODS_BASE_URL does not already include ?token=...:

- KNODS_GATEWAY_TOKEN

  • Required for headless API mode:

- KNODS_API_KEY

  • Preferred for headless API mode:

- KNODS_API_BASE_URL

  • Optional:

- OPENCLAW_AGENT_ID (default: iris) - OPENCLAW_BIN (default: openclaw on PATH)

Service Operations

  • Status:

- systemctl --user status knods-iris-bridge.service

  • Restart:

- systemctl --user restart knods-iris-bridge.service

  • Logs:

- journalctl --user -u knods-iris-bridge.service -f

Config Change Lifecycle (required)

After changing gateway URL/token env values, restart the running bridge process so it reloads config.

  • Generic service form:

- systemctl --user restart <knods-bridge-service>

  • Generic process form:

- stop old process - start poller again with updated env

Do not assume env changes are picked up live without restart.

Reference

  • Read references/protocol.md for canonical polling endpoints, payload schemas, and action examples.
  • Read references/headless-api.md for the direct run/list/poll/cancel flow execution API.

适合场景

01

文本生成图片

02

图片风格化

03

产品图和创意图

04

需要 FLUX 模型时

能力概览

能力 1

调用 FLUX 图像模型

能力 2

支持文本生图和图像改写

能力 3

覆盖 LoRA 或风格适配

能力 4

适合创意视觉生成

平台分布

OpenClaw

85.81%
按下载量换算3,021

安全审计

VirusTotal

可疑

ClawScan

可疑

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 knods 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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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