Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add liortesta/ClawdAgent --skill osint-reconnaissancegit clone --depth 1 https://github.com/liortesta/ClawdAgentWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/liortesta/clawdagent/osint-reconnaissance)<a href="https://agentmods.dev/skills/liortesta/clawdagent/osint-reconnaissance"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/osint-reconnaissance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/liortesta/clawdagent/osint-reconnaissance"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/osint-reconnaissance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium YARA Match · line 38 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00655 |
| Opus 5 | $0.00000 | $0.00328 |
| Sonnet 5 | $0.00000 | $0.00131 |
| Haiku 4.5 | $0.00000 | $0.00065 |
Grade A, and why
osint-reconnaissance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OSINT Reconnaissance — Big Brother V3.0
For authorized security testing, penetration testing, and defensive security auditing only.
Overview
Big Brother V3.0 is a comprehensive OSINT (Open Source Intelligence) platform for gathering publicly available information during authorized security assessments. It consolidates multiple reconnaissance techniques into a unified framework.
Core Capabilities
- Domain Reconnaissance: WHOIS lookups, DNS enumeration, subdomain discovery, certificate transparency logs
- IP Intelligence: Geolocation, ASN lookup, reverse DNS, port scanning, service fingerprinting
- Email OSINT: Email validation, breach database checks (HaveIBeenPwned-style), associated accounts discovery
- Social Media Mapping: Username enumeration across platforms, profile correlation, digital footprint analysis
- Phone Number OSINT: Carrier lookup, location history (where legally available), associated accounts
- Organization Profiling: Employee enumeration, technology stack detection, infrastructure mapping
Security Testing Workflow
1. SCOPE DEFINITION → Define authorized target scope (domains, IPs, personnel)
2. PASSIVE RECON → Gather publicly available info without touching target
- DNS records, WHOIS, certificate transparency
- Social media profiles, breach databases
- Technology stack (Wappalyzer, BuiltWith)
3. ACTIVE RECON → Authorized scanning
- Port scanning (nmap)
- Service enumeration
- Web application fingerprinting
4. CORRELATION → Cross-reference findings
- Map attack surface
- Identify potential entry points
- Prioritize vulnerabilities
5. REPORT → Document findings with remediation advice
Key OSINT Techniques for Security Auditing
- Subdomain Enumeration: amass, subfinder, assetfinder
- Technology Detection: Wappalyzer, WhatWeb, BuiltWith
- Certificate Transparency: crt.sh, Censys, Certificate Search
- DNS Recon: dig, nslookup, dnsenum, fierce
- Breach Data: HaveIBeenPwned API, DeHashed (with authorization)
- Metadata Extraction: ExifTool, FOCA, metagoofil
- Google Dorking: site:, inurl:, intitle:, filetype: operators
- Shodan/Censys: Internet-wide scanning for exposed services
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 58 lines · 0 tokens per session scan A e71faed0d35c
osint-reconnaissance is a skill published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 15d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 655 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…