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Agent Skill · 研究检索

moltsheet(moltsheet 命令行)

moltsheet 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。

moltsheet 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;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 1,953。

总安装 71,411周安装 2,861GitHub Stars 1下载 23,117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
1.0.3

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:moltsheet(moltsheet 命令行)
来源仓库:https://github.com/youssefbm2008/moltsheet
安装命令:
openclaw skills install moltsheet
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 moltsheet

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。

moltsheet 用于查找、检索和筛选相关信息。适合在 OpenClaw 中根据关键词、任务场景或来源线索快速定位候选结果时使用。通过 clawhub 安装,提供电子表格样式数据管理 CLI 工具。安装前需确认权限范围、维护状态及是否触发文件读写或 JSON 输出。

注意该技能优先使用 CLI 而非 HTTP,需验证数据格式兼容性。

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 moltsheet;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

    它需要接触敏感数据,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

moltsheet 怎么安装?

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

moltsheet 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。

moltsheet 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

moltsheet 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。

moltsheet 用的人多吗?

公开数据是累计安装 71,411 次,近一周安装 2,861 次,下载 23,117 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。

moltsheet 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

  • 需要接触敏感数据,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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
moltsheet

moltsheet 命令行 · 本页

71,4111接触敏感数据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
moltsheet
description
Use the Moltsheet CLI to manage spreadsheet-style data for AI workflows. Prefer the CLI over raw HTTP. Authenticate once, prefer --json, and use files or stdin for structured payloads.
allowed-tools
Bash(moltsheet *), Bash(npx moltsheet@latest *), Bash(npm run cli -- *), Bash(curl *)

Moltsheet

Moltsheet is a spreadsheet API for AI agents with a CLI designed to be easier and safer for agents than handwritten HTTP requests.

If you need to create sheets, inspect data, import rows, update cells, or share sheets with another agent, use the CLI first.

Default Agent Procedure

When handling Moltsheet as an agent, follow this order:

  1. Confirm the CLI is available: moltsheet --version
  2. If it is not installed, use npx moltsheet@latest ... or install it globally
  3. Authenticate once with moltsheet auth login
  4. Prefer --json whenever another tool, script, or agent will read the output
  5. Use sheet list and sheet get before writing, so you understand the target schema
  6. Use stdin or JSON files for structured inputs instead of hand-escaped inline JSON
  7. Use raw HTTP only if the CLI cannot be run

Install

Preferred global install:

npm install -g moltsheet

One-off usage without installing:

npx moltsheet@latest auth status

If you are working inside the Moltsheet repository itself, you can also run the local build:

npm --prefix cli install
npm run build:cli
npm run cli -- auth status

Authentication

Authenticate once:

moltsheet auth login

Or pass the API key directly:

moltsheet auth login --api-key YOUR_API_KEY

Check current auth state:

moltsheet auth status --json

Clear stored auth:

moltsheet auth logout

Credential resolution order:

  1. --api-key
  2. MOLTSHEET_API_KEY
  3. Stored local credential from auth login

Storage behavior:

  • Preferred: OS credential storage through keytar
  • Windows: Credential Manager
  • macOS: Keychain
  • Linux: Secret Service or libsecret
  • Fallback: local config file if secure storage is unavailable

Base URL defaults to production:

https://www.moltsheet.com

Override it when working against another environment:

moltsheet sheet list --base-url http://localhost:3000 --json

Commands Agents Should Reach For First

Register an agent:

moltsheet agent register --display-name "Research Bot" --slug research.bot --json

List sheets:

moltsheet sheet list --json

Inspect one sheet:

moltsheet sheet get SHEET_ID --json

Read a filtered subset of a sheet:

moltsheet sheet get SHEET_ID --columns "Company,Qualified" --filter "Qualified:eq:true" --json

Update a sheet:

moltsheet sheet update SHEET_ID --name "Leads v2" --json

Update a schema and allow destructive changes:

cat schema.json | moltsheet sheet update SHEET_ID --schema-stdin --confirm-data-loss --json

Delete a sheet:

moltsheet sheet delete SHEET_ID --json

Create a sheet from schema stdin:

cat schema.json | moltsheet sheet create "Leads" --schema-stdin --json

Create empty rows:

moltsheet row add SHEET_ID --count 10 --json

Add one row from stdin:

cat row.json | moltsheet row add SHEET_ID --data-stdin --json

Import multiple rows:

cat rows.json | moltsheet row import SHEET_ID --stdin --json

Import multiple rows through the dedicated sheet import route:

cat rows.json | moltsheet sheet import SHEET_ID --stdin --json

List rows:

moltsheet row list SHEET_ID --json

Delete rows by ID:

cat row-ids.json | moltsheet row delete SHEET_ID --stdin --json

Delete one row by index:

moltsheet row delete-index SHEET_ID 0 --json

Update cells:

cat updates.json | moltsheet cell update SHEET_ID --stdin --json

Add columns:

cat columns.json | moltsheet column add SHEET_ID --stdin --json

Delete columns by index list:

cat indices.json | moltsheet column delete SHEET_ID --stdin --json

Delete one column by index:

moltsheet column delete-index SHEET_ID 1 --json

Rename a column:

moltsheet column rename SHEET_ID 0 --name "Company Name" --json

Share a sheet:

moltsheet share add SHEET_ID --slug analyst.bot --access write --json

List collaborators:

moltsheet share list SHEET_ID --json

Remove a collaborator:

moltsheet share remove SHEET_ID --slug analyst.bot --json

Structured Input Patterns

Prefer files or stdin for anything shaped like JSON.

Sheet schema example:

[
  { "name": "Company", "type": "string" },
  { "name": "Website", "type": "url" },
  { "name": "Qualified", "type": "boolean" }
]

Single row example:

{
  "Company": "Moltsheet",
  "Website": "https://www.moltsheet.com",
  "Qualified": true
}

Multiple rows example:

[
  {
    "Company": "Moltsheet",
    "Website": "https://www.moltsheet.com",
    "Qualified": true
  },
  {
    "Company": "Example",
    "Website": "https://example.com",
    "Qualified": false
  }
]

Column definitions example:

[
  { "name": "Company", "type": "string" },
  { "name": "Website", "type": "url" }
]

Row ID list example:

[
  "123e4567-e89b-12d3-a456-426614174000",
  "123e4567-e89b-12d3-a456-426614174001"
]

Column index list example:

[
  0,
  2
]

Cell updates example:

[
  {
    "rowId": "123e4567-e89b-12d3-a456-426614174000",
    "column": "Qualified",
    "value": true
  }
]

How Agents Should Handle the CLI

Use this operating style:

  • Prefer --json for machine-readable output
  • Read before writing: use sheet list or sheet get before mutating data
  • Trust schema types and let the CLI or API validation guide corrections
  • Prefer stdin or files over complex shell escaping
  • Reuse stored auth rather than passing secrets repeatedly
  • Use collaborator slugs for sharing, never API keys
  • Use sheet import for the dedicated sheet import route and row import for rows-endpoint bulk insert behavior
  • If a command fails, inspect the error payload before retrying

Recommended write workflow:

  1. Run moltsheet auth status --json
  2. Run moltsheet sheet list --json
  3. Run moltsheet sheet get SHEET_ID --json
  4. Confirm column names and expected types
  5. Prepare JSON input
  6. Run the write command with --json
  7. Re-run sheet get or sheet list to verify the result

Output and Validation

Supported schema types:

  • string
  • number
  • boolean
  • date
  • url

Validation behavior:

  • Empty values are allowed
  • Invalid types return an error
  • Bulk row imports reject the full request if any row is invalid
  • Cell updates require valid rowId values and valid column names

Important note:

  • Returned row values are stored and returned as strings, even when validated against number, boolean, date, or url schema types

Collaboration Model

  • Sheets are shared by agent slug
  • Access levels are read and write
  • API keys are never exposed through collaboration commands
  • Collaboration responses expose only slug and displayName

Troubleshooting

If moltsheet is not installed:

npx moltsheet@latest sheet list --json

If you suspect auth problems:

moltsheet auth status --json

If you need to bypass stored auth for one call:

moltsheet sheet list --api-key YOUR_API_KEY --json

If you are working inside the repo and the published CLI is unavailable:

npm run cli -- sheet list --json

HTTP Fallback

Use raw HTTP only if you cannot run the CLI.

Base URL:

https://www.moltsheet.com/api/v1

Example list sheets request:

curl https://www.moltsheet.com/api/v1/sheets \
  -H "Authorization: Bearer YOUR_API_KEY"

Example create sheet request:

curl -X POST https://www.moltsheet.com/api/v1/sheets \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Leads",
    "description": "Outbound leads",
    "schema": [
      { "name": "Company", "type": "string" },
      { "name": "Website", "type": "url" }
    ]
  }'

Short Rules For Agents

  • Prefer the CLI over curl
  • Prefer --json
  • Prefer files or stdin for structured payloads
  • Read the sheet schema before writing
  • Verify writes by reading the sheet again
  • Use npx moltsheet@latest when the binary is not installed

平台分布

OpenClaw

89.06%
按下载量换算20,588

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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