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Agent Skill · 研究检索

trentclaw(trentclaw 搜索)

trentclaw 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。

trentclaw 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 11,957。

总安装 10,714周安装 442GitHub Stars 10下载 3,501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
1.2.0

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:trentclaw(trentclaw 搜索)
来源仓库:https://github.com/trent-ai-release/trentclaw
安装命令:
openclaw skills install trentclaw
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 trentclaw

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。

trentclaw 用于查找、检索和筛选相关信息,适合在 OpenClaw 中快速定位候选结果。适用于代理部署安全评估和相关研究检索场景。通过关键词或任务线索调用,结合来源仓库和 README 文档使用。安装前需确认权限范围和维护状态,注意可能触发联网或文件操作。建议核验具体用法后再投入实际工作流使用。

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 trentclaw;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

trentclaw 怎么安装?

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

trentclaw 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。

trentclaw 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。

trentclaw 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。

trentclaw 用的人多吗?

公开数据是累计安装 10,714 次,近一周安装 442 次,下载 3,501 次,GitHub 10 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。

trentclaw 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
trentclaw

trentclaw 搜索 · 本页

10,71410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
trent-openclaw-security
description
Assess your Agent deployment against security risks using Trent.
version
1.2.0
homepage
https://trent.ai
user-invocable
true
tags
metadata
openclaw
requires
env
optionalEnv
primaryEnv
TRENT_API_KEY

Trent OpenClaw Security Assessment

Audit your OpenClaw deployment for security risks. Identifies misconfigurations, chained attack paths, and provides severity-rated findings with fixes.

Setup

All tools are bundled — no external installer needed.

Set the TRENT_API_KEY environment variable. Get a key at https://trent.ai/openclaw/

Instructions

This audit runs in three phases. Run them in order.

Phase 1 — Configuration Audit

Collect metadata and send to Trent for analysis:

from openclaw_trent.openclaw_config.collector import collect_openclaw_metadata
from openclaw_trent.lib.audit_prompt import build_audit_prompt
from openclaw_trent.lib import trent_client

metadata = collect_openclaw_metadata()
message = build_audit_prompt(metadata)
response = trent_client.chat(message=message)

Save response["thread_id"] for Phase 3.

Present findings grouped by severity (see "Present results" below).

Summarize: "Phase 1 complete. N findings from configuration analysis. Phase 2 will scan your skills for deeper analysis — I'll show you exactly what would be uploaded before anything is sent. Ready to continue?"

Optional: specify a custom config path:

from pathlib import Path
metadata = collect_openclaw_metadata(openclaw_path=Path("/path/to/openclaw/config"))

Phase 2 — Skill Upload

Scan the workspace first (nothing is uploaded yet):

from openclaw_trent.lib.package_skills import scan_workspace

skills = scan_workspace()

Present what was found and how it will be protected. Example:

I found N skills in your workspace: | Skill | Type | Size | |---|---|---| | skill-name | installed-skill | 12KB | Before upload, each skill is packaged with its source code and metadata (name, version, dependencies). Files like .env, .pem, .key, and .db are excluded, and secrets in standard formats (API keys, tokens, AWS credentials, connection strings) are automatically redacted locally. If you use custom secret formats, keep them in environment variables rather than hard-coded in skill files. Ready to upload?

Use the secrets_redacted field — if any skills had secrets redacted, mention which ones in the table or below it.

Wait for the user to confirm before uploading.

After user confirms, upload:

from openclaw_trent.lib.upload_skills import upload_packaged_skills

upload_summary = upload_packaged_skills(skills)

Present the upload summary:

  • How many skills were uploaded, skipped (unchanged), failed, or too large
  • List each skill by name and status

If all uploads failed, report the errors and stop. Otherwise proceed.

Summarize: "Phase 2 complete. N skills uploaded. Proceeding to deep skill analysis..."

Phase 3 — Deep Skill Analysis

Analyse each uploaded skill using the thread ID from Phase 1:

from openclaw_trent.lib.prompts import build_per_skill_analysis_prompt
from openclaw_trent.lib import trent_client

thread_id = "<THREAD_ID from Phase 1>"
for skill in upload_summary["skills"]:
    if skill["status"] in ("uploaded", "skipped"):
        prompt = build_per_skill_analysis_prompt(skill)
        result = trent_client.chat(message=prompt, thread_id=thread_id)

Each request uses the Phase 1 thread ID so the advisor has full context from the configuration audit.

Present the deep analysis results alongside the Phase 1 findings.

Inspect system context separately

To view the system analysis data without running a full audit:

from openclaw_trent.lib.system_analyzer import collect_system_analysis
import json
result = collect_system_analysis()
print(json.dumps(result, indent=2))

This returns channel configuration and installed skill names. Useful for debugging or verifying what data is sent.

Present results

Format findings grouped by severity:

  • CRITICAL: Immediate action required
  • HIGH: Fix soon
  • MEDIUM: Recommended improvement
  • LOW: Minor hardening

For each finding show: the risk, where it was found, and the exact fix.

Highlight chained attack paths — where multiple settings combine to create worse outcomes.

Present recommended config changes as a diff snippet for the user to review and apply manually. Do not modify any system files directly.

When to use

  • User asks "Is my setup secure?" or "audit my config"
  • After changes to OpenClaw configuration, new plugins, or new MCP servers

平台分布

OpenClaw

71.95%
按下载量换算2,519

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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