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trentclawtrentclaw 搜索

Agent Skill

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。

总安装

10,714

周安装

442

GitHub Stars

10

下载量

3,501
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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