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investigator（investigator 搜索）

Agent Skill

investigator 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

7,950

周安装

338

GitHub Stars

1

下载量

2,785
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：investigator（investigator 搜索）
来源仓库：https://github.com/elyasuuuuu/investigator
安装命令：
openclaw skills install investigator
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 investigator

简介

investigator 利用开源情报技术调查用户名、邮箱、公司或域名公开足迹。

  • 适用于背景核查、数字足迹追踪或网络安全初步筛查。
  • 支持批量查询但受限于公开数据可用性，私有信息无法获取。
  • 不得用于非法目的，尊重他人隐私与法律边界。investigator 属于研究检索类 Skill，可作为该场景下的辅助能力补充。
  • 输出结果仅供参考，不能作为唯一依据做出重大判断。

SKILL.md

name
osint-investigator
description
Investigate public online footprints using open-source intelligence techniques. Use when a user wants to research a username, email, person, company, domain, IP, phone number, location, image, or other public target using publicly available information; correlate findings; validate profile candidates; enrich results with web search; capture public profile links and profile images; run optional defensive HIBP email checks when configured; or export structured JSON and reports.

OSINT Investigator

Use this skill for broad public-footprint OSINT.

Supported target types include:

  • username / handle
  • email address
  • person / alias
  • organisation / company
  • domain / website
  • IP address
  • phone number
  • location / address
  • image

Read as needed:

  • references/target-types.md for classification
  • references/workflow.md for investigation flow
  • references/modules.md for module selection
  • references/osint-sources.md for source categories
  • references/platforms.md for target platforms and search ideas
  • references/platform-validation.md for platform-specific validation rules
  • references/profile-media.md for profile image handling
  • references/scoring.md for confidence logic
  • references/aggregation.md for overall scoring and result merging
  • references/variants.md for handle-variant generation
  • references/tooling.md for lightweight discovery helpers
  • references/breach-checks.md for optional defensive breach lookup behavior
  • references/apis.md for optional API enrichment
  • references/configuration.md for HIBP API key setup
  • references/report-format.md for structured reporting
  • references/safety.md for acceptable-use boundaries
  • references/output.md for response structure

Use scripts when helpful:

  • scripts/generate_variants.py for plausible username variants
  • scripts/check_profiles.py for first-pass platform checks with platform-aware validation
  • scripts/check_hibp.py for optional Have I Been Pwned email checks
  • scripts/check_domain.py for lightweight domain enrichment
  • scripts/check_ip.py for lightweight IP enrichment
  • scripts/aggregate_results.py to merge findings into a scored summary
  • scripts/export_json.py for structured JSON output
  • scripts/build_report.py for compact report generation from structured results

Use web_search and web_fetch to confirm weak findings, enrich strong ones, and gather public evidence when helper-script results alone are ambiguous.

Core behavior

  • Focus on public data only.
  • Prefer lightweight verification over aggressive scraping.
  • A 200 HTTP status is not enough to confirm a profile.
  • Separate facts from guesses.
  • Report confidence, not certainty.
  • Keep results structured and easy to audit.
  • Prefer a smaller set of verified findings over a noisy wall of guesses.
  • Run only the modules relevant to the target.

Workflow

  1. Classify the target using references/target-types.md.
  2. Normalize the input.
  3. Select relevant modules using references/modules.md.
  4. Run lightweight helper scripts where useful.
  5. Use targeted web search to confirm or enrich weak and likely matches.
  6. Capture final links and public profile image URLs when available.
  7. If an email is provided and HIBP is configured, run a defensive breach check.
  8. For domains or IPs, run the relevant lightweight helper.
  9. Record exact matches, likely matches, weak matches, no-results, and not-verifiable results.
  10. Compare public signals across findings.
  11. Aggregate the findings into a scored summary using scripts/aggregate_results.py and references/aggregation.md.
  12. Return a concise human summary or a structured report depending on the request.
  13. Export JSON if requested.

Output rules

Always distinguish between:

  • confirmed public match
  • likely match
  • weak/uncertain match
  • not verifiable
  • no evidence found

Include final links for meaningful findings. Include profile image links only when they are publicly exposed and easy to extract. If HIBP is used, report breach results as defensive exposure information, not identity proof. If using domain/IP helpers, treat them as enrichment, not full attribution.

Do not overclaim identity resolution. If evidence is thin, say so clearly. If evidence conflicts, say so clearly. Lead with the strongest public evidence first. Prefer the compact format by default; use an extended report only when the user asks for depth.

Safety

Read references/safety.md when the request could drift into harassment, private-person targeting, or invasive tracking.

Do not help with:

  • credential theft
  • account takeover
  • bypassing access controls
  • doxxing
  • stalking or targeted harassment
  • collecting non-public personal data
  • invasive private-person targeting

Style

  • concise
  • factual
  • audit-friendly
  • explicit about uncertainty

适合场景

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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

76.02%
按下载量换算2,117

安全审计

VirusTotal

可疑

ClawScan

可疑

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

GitHub 仓库

github.com

/elyasuuuuu/investigator

SKILL.md 来源

clawhub.ai

/elyasuuuuu/investigator

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