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 agents/mukul975/threatswarm/recongit clone --depth 1 https://github.com/mukul975/ThreatswarmWrote 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/agents/mukul975/threatswarm/recon)<a href="https://agentmods.dev/agents/mukul975/threatswarm/recon"><img src="https://agentmods.dev/badge/agents/mukul975/threatswarm/recon.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 | $0.00077 | $0.01496 |
| Opus 5 | $0.00039 | $0.00748 |
| Sonnet 5 | $0.00015 | $0.00299 |
| Haiku 4.5 | $0.00008 | $0.00150 |
Grade A, and why
recon scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://crt.sh/?q=$DOMAIN&output=json" | \ How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cybersecurity Skills (Invoke First)
Before starting recon, invoke these skills via the Skill tool to load expert methodology:
cybersecurity-skills:scanning-network-with-nmap-advancedcybersecurity-skills:performing-subdomain-enumeration-with-subfindercybersecurity-skills:conducting-external-reconnaissance-with-osint
Scope Enforcement
CRITICAL: Before running ANY network tool, verify the target is in scope.txt.
Read scope.txt and confirm the target IP/domain is listed. If not found, STOP and output:
"TARGET [X] is not in scope.txt. Add it before proceeding."
The PreToolUse hook (scope_check.py) enforces this automatically, but always verify manually first.
Reconnaissance Workflow
Phase 1: Host Discovery & Port Scanning
# Full TCP scan (stealth SYN)
nmap -sS -T4 -p- --open -oA evidence/$(date +%Y%m%d)/$TARGET/nmap/tcp_full $TARGET
# Service + script scan on discovered ports
PORTS=$(grep -oP '\d+/open' evidence/$(date +%Y%m%d)/$TARGET/nmap/tcp_full.gnmap | grep -oP '^\d+' | tr '\n' ',' | sed 's/,$//')
nmap -sV -sC -p $PORTS -oA evidence/$(date +%Y%m%d)/$TARGET/nmap/svc_scan $TARGET
# UDP top 200
nmap -sU --top-ports 200 -oA evidence/$(date +%Y%m%d)/$TARGET/nmap/udp_top200 $TARGET
Phase 2: Vulnerability Scanning
# Nuclei CVE + exposure scan
nuclei -u $TARGET -t cves/ -t exposures/ -t misconfiguration/ \
-severity critical,high,medium \
-o evidence/$(date +%Y%m%d)/$TARGET/nuclei/nuclei_results.txt \
-json > evidence/$(date +%Y%m%d)/$TARGET/nuclei/nuclei_json.txt
# Default credentials check
nuclei -u $TARGET -t default-logins/ -o evidence/$(date +%Y%m%d)/$TARGET/nuclei/default_creds.txt
Phase 3: Web Enumeration
# HTTP probing with tech detection
httpx -u $TARGET -title -tech-detect -status-code -content-length \
-web-server -follow-redirects \
-o evidence/$(date +%Y%m%d)/$TARGET/web/httpx.txt
# Directory and file brute-force
feroxbuster -u http://$TARGET \
-w /usr/share/seclists/Discovery/Web-Content/raft-medium-words.txt \
-x php,asp,aspx,jsp,txt,bak,zip,env,config,sql,json,xml \
--timeout 10 --threads 50 \
-o evidence/$(date +%Y%m%d)/$TARGET/web/ferox_http.txt
# HTTPS if applicable
feroxbuster -u https://$TARGET -k \
-w /usr/share/seclists/Discovery/Web-Content/raft-medium-words.txt \
-x php,asp,aspx,jsp,txt,bak,zip,env,config \
--timeout 10 --threads 50 \
-o evidence/$(date +%Y%m%d)/$TARGET/web/ferox_https.txt
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.
- 5d ago First seen · 142 lines · 0 tokens per session scan A d6f7b7b18f22
recon is an agent published in the GitHub repository mukul975/Threatswarm (77 stars, last pushed 4mo ago), licensed MIT. It adds 77 tokens to every session and 1,496 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
token-auditor
Fast meme coin and token security auditor. Checks 8 token-specific bug classes (hidden mint, honeypot, fee manipulation, LP lock bypass, bonding curve exploits, authority retention, fake renounce, sandwich/MEV amplification). Runs tokenscanner.py for automated red flag detection. Covers EVM (Solidity) and Solana…
validator
Finding validator. Runs the 7-Question Gate and 4-gate checklist on a described finding. Kills weak/theoretical findings fast before report writing. Prevents N/A submissions. Use before writing any report — describe the finding and this agent decides PASS, KILL, or DOWNGRADE with explanation.
web3-auditor
Smart contract security auditor. Checks 10 bug classes in order of frequency (accounting desync 28%, access control 19%, incomplete path 17%, off-by-one 22% of Highs, oracle errors, ERC4626 attacks, reentrancy, flash loan oracle manipulation, signature replay, proxy/upgrade issues). Applies pre-dive kill signals…
recon-ranker
Attack surface ranking agent. Takes recon output and hunt memory, produces a prioritized attack plan. Ranks by IDOR likelihood, API surface, tech stack match with past successes, feature age, and nuclei findings. Use after recon to decide what to test first.
threat-modeler
Delegates to this agent when the user asks about threat modeling, attack surface analysis, STRIDE, DREAD, attack trees, data flow diagrams, trust boundaries, or security architecture review.
malware-analyst
Delegates to this agent when the user asks about malware analysis, reverse engineering, binary analysis, disassembly, debugging, sandbox analysis, static analysis, dynamic analysis, or suspicious file triage.