Agentic Bug Hunter is a terminal toolkit that uses AI to investigate security targets, test for vulnerabilities, validate findings, and write bug bounty reports. It is for ethical hackers submitting findings to platforms such as HackerOne, Bugcrowd, Intigriti, or Immunefi, and can resume previous sessions. The catalogue entries package commands, skills, agents, instructions, hooks, and settings for using the toolkit.
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 agentmods add commands/awarexone/agentic-bug-hunter/osint-employeesgit clone --depth 1 https://github.com/Awarexone/Agentic-Bug-HunterWrote 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/commands/awarexone/agentic-bug-hunter/osint-employees)<a href="https://agentmods.dev/commands/awarexone/agentic-bug-hunter/osint-employees"><img src="https://agentmods.dev/badge/commands/awarexone/agentic-bug-hunter/osint-employees.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00082 | $0.00848 |
| Opus 5 | $0.00041 | $0.00424 |
| Sonnet 5 | $0.00016 | $0.00170 |
| Haiku 4.5 | $0.00008 | $0.00085 |
Grade A, and why
osint-employees 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 6d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/osint-employees
Gather employee names and email patterns for the spray-prep phase. Read-only OSINT — no auth probing.
Usage
/osint-employees target.com
/osint-employees target.com --with-linkedin # add CrossLinked LinkedIn search
/osint-employees target.com --with-pydictor-social # add personal-style password candidates
/osint-employees target.com --company "Acme Corp" # override auto-detected company name
/osint-employees target.com --sources duckduckgo,crtsh --limit 200
Pipeline
- theHarvester — emails + names from search engines + CT logs
- Default sources:
duckduckgo,brave,yahoo,mojeek,crtsh,certspotter,hackertarget,otx - All free, no API keys required, no LinkedIn-specific scraping
- Default sources:
- Derive names from email local-parts (
[email protected]→John Smith)- Ambiguous patterns (
[email protected]) are skipped — not enough signal
- Ambiguous patterns (
- (opt) CrossLinked — LinkedIn employee names via Google/Bing dorks
--with-linkedinopts in- Uses search engines only; no LinkedIn auth required
- username-anarchy — expand "First Last" into 32+ username permutations
john,j.smith,jsmith,smithj,js,john.smith, etc.
- (opt) pydictor --extend — personal-style password candidates
--with-pydictor-socialopts in- Generates
firstname2025!,firstname123style mutations
Output
recon/<target>/osint/
├── theharvester.json # raw theHarvester output
├── emails.txt # extracted emails (unique)
├── employee-names.txt # "First Last" per line
├── usernames.txt # all username permutations
└── (personal-passwords.txt if --with-pydictor-social)
Why opt-in for LinkedIn
CrossLinked queries Google/Bing for site:linkedin.com "Company Name" — public search, no LinkedIn auth required. But some BBP programs classify LinkedIn-based employee identification under "social engineering reconnaissance" which they don't permit. Read the program scope before running with --with-linkedin.
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.
- 6d ago First seen · 68 lines · 82 tokens per session scan A 18682e8d7a0b
osint-employees is a command published in the GitHub repository Awarexone/Agentic-Bug-Hunter (4,711 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 848 once invoked, about $0.0004 per session on Opus 5. 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 2026-08-30.
Other commands, from other repositories
recon
Run full recon pipeline on a target — subdomain enum (Chaos API + subfinder), live host discovery (dnsx + httpx), URL crawl (katana + waybackurls + gau), gf pattern classification, nuclei scan. Outputs to recon/ / directory. Usage: /recon target.com.
hunt
Active vulnerability hunting. Two-track dispatcher — asks Red Team vs WAPT, hands off to hunt-dispatch skill and sibling commands. Usage: /hunt target.com | /hunt .target.com | /hunt targets.txt [--vuln-class X] [--source-code P] [--chrome].
autopilot
Run autonomous hunt loop on a target — scope check → recon → rank surface → hunt → validate → report with configurable checkpoints. Usage: /autopilot target.com [--paranoid|--normal|--yolo].
triage
Quick 7-Question Gate triage on a finding before writing a report. Kills N/A submissions before they happen. Faster than /validate — for quick go/no-go decisions. Usage: /triage.
validate
Validate a finding — runs 7-Question Gate + 4-gate checklist. Kills weak findings before report writing. Prevents N/A submissions that hurt validity ratio. Usage: /validate.
intel
On-demand intelligence fetch for a target — CVEs, disclosed reports, new features. Pulls NVD/GitHub-Advisory CVEs + bundled disclosed reports + hunt memory context. Usage: /intel target.com.